FOREWOllID

As a part ofthe Long Term Pavement Performance (LTPP) Seasonal Monitoring Program,.

measures of electrical resistance have been used to monitor frost conditions at selected LTIP test

sites. The raw data from the pavement instrumentation have been available in the LTPP database

for some time. However, to be useful in engineering analyses, the raw data must be intel preted to

derive estimates offrost and thaw penetration depths. This report documents analysis condLlcted

to develop and apply procedures for interpretation ofthe raw data. The "computed parameters"

derived through this analysis have been added to the LTPP database.

This report will be ofinterest to those who wish to use the LTPP seasonal mon.itoring dat'i, and to

other users (or potential users) ofsimilar technology for monitoring frost conditions..

7 -II .'IU~~ /..b?

T. aul Teng, P.E.

Director

Office ofInfrastructure

ReserJxch and Development

NOTICE

This document is disseminated under the sponsorship ofthe Department of Transportat.ion in the

interest ofinformation exchange. The United States GoveITill1cnt assumes no liability for its

contents or use thereof This report does not constitute a standard, specification, or regulation.

The United States Government does not endorse products or manufacturers. Trademarks or

manufacturers' names appear herein only because they are considered essential to the object of

this document.

PROTECTED UNDER INTERNATIONAL COPYRIGHT

ALL RIGHTS RESERVED

NATIONAL TECHNICAL INFORMATION SERVICE

U.S. DEPARTMENT OF COMMERCE

Reproduced from

best available copy.
Tee

PB2000-102952

11111111111111I111I111111111 "I1II Page

- 1. Report No.

FHWA-RD-99-088

2. Government Accession No. 3. Recipient's Catalog No.

4. Title and Subtitle

DETERMINATION OF FROST PENETRATION IN LTPP

SECTIONS, Final Report

7. Author(s)

Hesham A. Ali and Shiraz D. Tayabji

9. Performing Organization Name and Address

ERES Consultants, Inc.

9030 Red Branch Road, Suite 210

Columbia, Maryland 21045

12. Sponsoring Agency Name and Address

Office ofInfrastructure Research and Development

Federal Highway Administration

6300 Georgetown Pike

McLean, Virginia 22101-2296

5. Report Date

September 1999

6. Performing Organization Code

8. Performing Organization Report No.

10. Work Unit No. (TRAIS)

C6B

11. Contract or Grant No.

DTFH6l-96-C-00003

13. Type of Report and Period Covered

Final Report

August 1997 to July 1998

14. Sponsoring Agency Code

15. Supplementary Notes

Contracting Officer's Technical Representative (COTR): Cheryl Allen Richter, HRDI

Technical Consultants: Ron Atkins and Dick Berg

16. Abstract

To better understand the environmental factors and their effects on pavement performance, the Long Term

Pavement Performance (LTPP) Seasonal Monitoring Program (SMP) was initiated during 1992. Sixty-four LTPP

pavement sections were identified to be included in the SMP. These sections are monitored frequently to acquire

data on seasonal variation in pavement conditions. As part of this program, work is being performed to identify

and monitor the freeze state in the base, subbase, and subgrade at the SMP sections located in the freeze zone in

the United States and Canada. This work makes use of temperature sensors and electrical resistivity techniques.

The main goal of the study reported here was to determine frost penetration at the selected SMP sections. As part

of the study, an interactive computer program, FROST, was developed to facilitate the interpretation of the

electrical resistivity and temperature data. Analysis results include the freeze state at each electrical resistivity

sensor and the frost penetration at each site.

As part of the overall LTPP data analysis effort, it is expected that the information on the seasonal variation in

the freeze state in the unbound base, subbase, and subgrade will be used to develop improved understanding of

the seasonal variation in the load-carrying capacity of pavements and the subsequent effect on pavement

performance.

17. Key Words

Asphalt pavements, concrete pavements, electrical

resistivity measurement, freeze-thaw, frost penetration,

LTPP, pavement performance, pavement testing, pavement

monitoring, subgrade, subgrade freezing, thaw weakening.

18. Distribution Statement

No restrictions. This document is available to the

public through the National Technical Information

Service, Springfield, VA 22161.

19. Security Classification (of this report)

Unclassified

20. Security Classification (of this page)

Unclassified

21. No. of Pages

142

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
•• •

APPROXIMATE CONVERSIONS TO SI UNITS APPROXIMATE CONVERSIONS FROM SI UNITS

Symbol When You Know Multiply By To Find Symbollfl Symbol When You Know Multiply By To Find Symbol

LENGTH LENGTH

in inches 25.4 millimeters mm mm millimeters 0.039 inches in

It feet 0.305 meters m m meters 3.28 feet It

yd yards 0.914 meters m m meters 1.09 yards yd

mi miles 1.61 kilometers km km kilometers 0.621 miles mi

AREA AREA

in2 square inches 645.2 square millimeters mm2 mm2 square millimeters 0.0016 square inches in2

ft2 square feet 0.093 square meters m2 m2 square meters 10.764 square feet ft2

ydZ square yards 0.836 square meters m2 m2 square meters 1.195 square yards ydZ

ac acres 0.405 hectares ha ha hectares 2.47 acres ac

mi2 square miles 2.59 square kilometers km2 km2 square kilometers 0.386 square miles mi2

VOLUME VOLUME

f10z fluid ounces 29.57 milliliters mL mL milliliters 0.034 fluidounces fI oz

_.

III gal gallons 3.785 _. liters L L liters 0.264 gallons gal

ft3 cubic feet 0.028 cubic meters m3 m3 cubic meters 35.71 cubic feet ft3

yet' cubic yards 0.765 cubic meters m3 m3 cubic meters 1.307 cubic yards yet'

NOTE: Volumes greater than 1000 I shall be shown in m3•

MASS MASS

oz ounces 28.35 grams g g grams 0.035 ounces oz

Ib pounds 0.454 kilograms kg kg kilograms 2.202 pounds Ib

T short tons (2000 Ib) 0.907 megagrams Mg Mg megagrams 1.103 short tons (2000 Ib) T

(or "metric ton") (or or) (or "to) (or "metric ton")

TEMPERATURE (exact) TEMPERATURE (exact)

of Fahrenheit 5(F-32)/9 Celcius °C °C Celcius 1.8C + 32 Fahrenheit of

temperature or (F-32)11.8 temperature temperature temperature

ILLUMINATION ILLUMINATION

fc foot-candles 10.76 lux Ix Ix lux 0.0929 foot-candles fc

fI foot-Lamberts 3.426 candelalm2 cdIm2 cdIm2 candelalm2 0.2919 foot-Lamberts ft

FORCE and PRESSURE or STRESS FORCE and PRESSURE or STRESS

Ibf poundforce 4.45 newtons N N newtons 0.225 poundforce Ibf

Ibflin2 poundforce per 6.89 kilopascals kPa kPa kilopascals 0.145 poundforce per Ibflin2

square inch square inch

• SI is the symbol for the International System of Units. Appropriate (Revised september 1993)

rounding should be made to comply with Section 4 of ASTM E380.
TABLE OF CONTENTS

Section

CHAPTER 1 - INTRODUCTION 1

Need for Frost Penetration Data 1

Objectives 2

Overview ofFreeze-Related Data Collection in LTPP 2

Abbreviation and Tenninology 6

Organization ofthe Report " 6

CHAPTER 2 - THEORETICAL CONSIDERATIONS FOR FROST PENETRATION

PREDICTION AND MEASUREMENT 9

Frost Penetration Prediction Models 9

Frost Penetration Measurement Using ER 9

CHAPTER 3 - PRELIMINARY DATA ANALYSIS 15

Characteristics ofTemperature Data 15

Characteristics ofER Data 20

Potential Data Interpretation Algorithms 23

Implications for ER Data Interpretation 24

CHAPTER 4 - INTERACTIVE PROCEDURE FOR INTERPRETATION

OF ERDATA 27

Procedure Highlights 27

Analysis Steps 29

Guidelines for Defining the ER Threshold Line in FROST 33

Example 34

CHAPTER 5 - ANALYSIS RESULTS 39

Frost Penetration Graphs 39

Comparison With Historical Data 39

Comparison With TDR Data 43

Reliability ofResults 43

Interpretation Results 47

CHAPTER 6 - CONCLUSIONS AND RECOMMENDATIONS 49

Conclusions 49

Recommendations 51

REFERENCES 53

APPENDIX A - FROST PENETRATION PLOTS 55

APPENDIX B - SAMPLE TIME-SERIES ER PLOTS USED IN PROGRAM FROST 83

APPENDIX C - TABLE SMP_FROST_PENETRATION 119

iii
LIST OF FIGURES

1 LTPP SMP instrumentation layout [2] 4

2 Electrical resistivity measurements - CRREL probe (adopted from [2]) 5

3 Phase change events using latent heat offusion 17

4 FROST decision tree 28

5 Schematic diagram ofthe arrangement ofthermistor and resistivity probes 31

6 Example ER time-series graph produced by FROST 35

7 Example frost penetration graph produced by FROST 36

8 Example ER versus depth plot produced by FROST 37

9 Sample frost penetration plot 40

10 Average depth offrost penetration (inches) in the United States [14] 41

11 Comparison between the seasonal variation offrost and moisture content,

section 274040 44

12 Comparison between the seasonal variation offrost and moisture content,

section 041024 45

13 System arrangement for reliability analysis 46

IV
LIST OF TABLES

1 SMP sites with electrical resistivity probes 3

2 Resistivities of different soils (U.S. Bureau of Standards Technical Report 108) 10

3 Resistivities of different soils (Evershed & Vignoles Bulletin 245) 10

4 Effect ofmoisture content on soil resistivity 11

5 Effect ofsalt content on soil resistivity 11

6 Effect oftemperature on soil resistivity 12

7 Change in bulk resistivity per unit change in the volumetric change in ice content. 14

8 Summary ofphase change analysis 19

9 Cold winter months during which freezing is expected 29

10 Temperature substitution hierarchy 32

11 Comparison between computed and average estimated frost penetration 42

v

CHAPTER 1 - INTRODUCTION

Need for Frost Penetration Data

More than 85 percent ofthe land in the United States and Canada is subjected to seasonal

frost. In some areas, particularly at higher altitudes or latitudes, frost penetration can be up to 2.5

m deep. Problems caused by frost include the seasonal change in the bearing capacity ofsoils

brought by freezing and thawing. A large increase in the elastic modulus ofthe unbound road

materials is expected when the material freezes. A factor of 100 is used to relate the modulus at

freezing to the modulus at nonfreezing conditions [1]. When the frost thaws in the spring, the

substantial moisture increase in the soil can lead to weakened support for the pavement structure.

Another mechanical process associated with frost is the volumetric changes that can take place in

frost-susceptible soils. Such volumetric changes could lead to vertical differential movements of

the road and subsequent poor performance. The maintenance of highways and airport runways is

often complicated by heaving ofroadbeds out of vertical alignment and breaking ofthe pavement

surface.

These problems have long been realized. Considerable efforts have been expended to

monitor and predict the frost penetration in different seasons, as well as the associated structural

changes in pavements. One ofthe most common methods to "measure" the frost penetration is

to measure the temperature profile, as a function of depth, and assume the freezing conditions to

exist at temperatures below DOC. However, two problems associated with this method make its

use less reliable than desired. The first problem is that the freezing point could be depressed by

the existence ofsalts in the soil. The second is the zero isothermal (i.e., temperature is constant

at DOC) conditions that are known to exist during the spring thaw, making it difficult to identify

the frost-line location.

To better understand the environmental factors and their effects on pavement

performance, the Long Term Pavement Performance (LTPP) Seasonal Monitoring Program

(SMP) was initiated during 1992. Sixty-four LTPP sections were identified to be included in the

SMP. These sections are monitored frequently to acquire data on seasonal variation in pavement

conditions. As part ofthis program, work is being performed to identify and monitor the freeze

state in the base, subbase, and subgrade at the SMP sections located in the freeze zone in the

United States and Canada. This work makes use oftemperature sensors and electrical resistivity

technique.

The electrical resistivity technique is based on the fact that the bulk resistivity of a soil

increases dramatically when the soil freezes. The technique, referred to as the Electrical

Resistivity (ER) method, involves measurement of electrical resistance and electrical resistivity

ofthe soil material using metal wire electrodes. The probes used in the LTPP program were

developed by the U.S. Army Corps ofEngineers' Cold Regions Research and Engineering

Laboratory (CRREL).

1
Objectives

The objective ofthe freeze-state study reported here was to produce a good estimate of

the freeze state at the selected SMP sections and to create computed quantity tables within the

Information Management System (IMS) that provide an estimate ofthe probable frost locations

within the pavement structure, based on the interpretation of resi~tivity and temperature

measurements at the SMP test sections. Table 1 lists the SMP sites where the electrical

resistivity probes are installed. This report describes the approach and method used to analyze

the data, summarizes the analysis results, and provides some ofthe important background

material related to the collection and analysis of electrical resistivity data.

Overview of Freeze-Related Data Collection in LTPP

The techniques, equipment, and schemes of data collection under the SMP are described

in detail in the LTPP Seasonal Monitoring Program: Instrumentation Installation and Data

Collection Guidelines [2]. For the reader's convenience, a brief description ofER and soil

temperature data is presented in this section.

Collection ofER Data

Data from three ER measurements (resistivity, resistance, and voltage) are collected

approximately every month, every other year, at the selected SMP sections. The resistivity

probes used in the measurement are permanently installed in a O.25-m hole under the pavement,

near the end (or beginning) ofthe test section. The probe consists of 36 metal wire electrodes

spaced approximately 51 mm apart and mounted on a solid polyvinyl chloride (PVC) rod 1.9 m

long. Individual lead wires connect the electrodes to a pin connector. A readout device is

connected to the pin connector to read voltage and current data. A layout ofthe SMP

instrumentation is shown in figure 1.

The first ER measure is termed contact resistance. It is measured using two consecutive

electrodes at a time, so it is often referred to as 2-point resistance. Contact resistance (referred to

as resistance in the remainder ofthe report) is obtained by dividing the voltage drop between two

electrodes by the electrical current passing through the soil from one electrode to the other,

according to Ohm's law. Figure 2 shows the assembly used to obtain resistance data.

The second ER measure is termed resistivity. It is measured using four consecutive

electrodes at a time, so it is often referred to as 4-point resistivity. Resistivity is obtained by

dividing the voltage drop between the two inner electrodes by the electrical current passing

through the soil between the outer two electrodes, and multiplying by a geometric factor. Figure

2 also shows the assembly used to obtain the resistivity. The difference between resistance and

resistivity is that resistivity is a material property. It is the resistance of a unit length and cross

section of a material.

2
Table 1. SMP sites with electrical resistivity probes.

Section State Experiment Pavement TDR Install. Monitoring Seasonal

ID Name Typel Type2 Date3 Period Round

041024 Arizona GPS-1 ACP 08/21195 11195 - 08/96 C

081053 Colorado GPS-1 ACP 07/01193 10/93 - 09/97 A

091803 Connecticut GPS-1 ACP 08/19/93 08/93 - 10/97 A

161010 Idaho GPS-1 ACP 09/30/93 10/93 - 06/97 B

183002 Indiana GPS-3 JPCP 09/07/95 09/95 - 08/96 A

204054 Kansas GPS-4 JRCP 08/24/95 08/95 - 08/96 A

231026 Maine GPS-1 ACP 09/15/93 09/93 - 06/95 A

241634 Maryland GPS-2 ACP 05/11/95 05/95 - 11/97 A

251002 Massachusetts GPS-1 ACP 09/01/93 09/93 - 10/97 A

271018 Minnesota GPS-1 ACP 08/24/93 08/93 - 06/95 A

271028 Minnesota GPS-1 ACP 09/08/93 09/93 - 09/97 B

276251 Minnesota GPS-1 ACP 09/14/93 09/93 - 09/97 C

274040 Minnesota GPS-4 JRCP 09/21/93 09/93 - 09/97 D

308129 Montana GPS-1 ACP 08/12/92 10/93 - 10/97 A

313018 Nebraska GPS-3 JPCP 08/10/95 08/95 - 08/96 B

331001 New Hampshire GPS-1 ACP 10/14/93 10/93 - 10/97 A

364018 New York GPS-4 JRCP 10/27/93 10/93 - 10/97 A

421606 Pennsylvania GPS-4 JRCP 08/09/95 08/95 - 10/97 A

460804 South Dakota SPS-8 ACP 07/14/94 07/94 - 09/97 B

493011 Utah GPS-3 JPCP 08/03/93 11/93 - 09/97 A

501002 Vermont GPS-1 ACP 10/06/93 10/93 - 10/97 A

561007 Wyoming GPS-1 ACP 08/10/93 08/97 - 09/97 A

831801 Manitoba GPS-1 ACP 10/12/93 10/93 - 09/97 A

833802 Manitoba GPS-3 JPCP 10/14/93 10/93 - 09/97 B

871622 Ontario GPS-1 ACP 09/22/93 09/93 - 10/97 A

893015 Quebec GPS-3 JPCP 09/29/93 09/93 - 11/97 A

906405 Saskatchewan GPS-1 ACP 10/06/93 10/93 - 09/97 A

2

GPS = General Pavement Studies, SPS = Specific Pavement Studies.

ACP = Asphalt Concrete Pavement, JPCP = Jointed Plain Concrete Pavement, JRCP = Jointed Reinforced

Concrete Pavement.

TDR = Time-Domain Reflectometry Sensors.

3
TOP VIEW

SIDE VIEW

TDRPrab.. (rotated 10· 110

=

..,n one lIl'.rGng

81de '" re hole)

ConduIt

..g. 51 111m (2 In)

i Conduit

Figure 1. LTPP SMP instrumentation layout [2].

4
Function

Gcncrntor Dlgitnl

Mulllrnclcr 11

Anullclcr

(1)

Probe Digital

Multi",ctcr 12

VoltlUeter

(V)

Electrodes ..,.--4¢::C:~

a. Contact (Two-Point) Resistance Measurement.

SI- Spacing between II and VI electrodes.

SJ-Spaclng between VI and VJelectrodes.

S, - Spacing between VJ and IJelectrodes.

Digital

Multimeler 12

Voltmeter

(V)

Digital

Multlmeler 11

Ammeter

(1)

Function

Generator

b. Four-Point Resistivity Measurement.

Figure 2. Electrical resistivity measurements - CRREL probe (adopted from [2]).

5
The third measure is the automated volt drop measure, in which the volt drop between

two electrodes is measured by a readout device. Volt drop is representative ofthe resistance of

the soil between the two electrodes. Volt drop will be referred to as voltage in the remainder of

this report.

All three ER measures are collected sequentially along the probe depth. Due to the

testing arrangement, there are 35 resistance readings, 35 voltage readings, and 33 resistivity

readings for each probe, representing resistance, voltage, and resistivity at different depths.

Collection ofSoil Temperature Data

In the SMP, the temperature profile is measured at 18 depths, measured by 18 thermistors

that are permanently installed in a O.25-m-diameter hole located near the section end. The first

three thermistors are embedded in the surface bound layer, and the rest are embedded in the base,

subbase, and subgrade layers. Data from the first five thermistors are recorded hourly. Daily

temperature statistics, including maximum, minimum, average temperature, and times of

maximum and minimum temperature, are recorded for all thermistors. Temperature data were

used jointly with resistivity data to determine the frost depth.

Abbreviation and Terminology

Throughout this report the following terminology will be used:

Abbreviation

LTPP

SMP

IMS

ER

Resistance

Resistivity

Voltage

Organization ofthe Report

Description

Long Term Pavement Performance program

Seasonal Monitoring Program

Information Management System ofLTPP

Electrical Resistivity, including resistance, resistivity, and voltage

Soil electrical resistance as measured from the 2-point resistance

(contact resistance) measurement

Soil electrical resistivity as measured from the 4-point resistivity

measurement

Voltage drop as determined by the automated volt drop

measurement

This report consists ofsix chapters. Chapter 1 introduces the problem offrost, the

objective ofthe research, and the collection ofER and temperature data. Chapter 2 reviews some

ofthe theoretical models developed to predict frost penetration depth, presents some ofthe

theoretical considerations on the sensitivity ofER data to other variables, and presents some of

the models developed to quantify such effects. Chapter 3 explores the characteristics ofER and

temperature data as they relate to frost penetration analysis. Chapter 4 presents the procedure

selected for data interpretation, including the development ofthe computer program FROST to

interpret the ER and temperature data. Chapter 5 reports the results ofthe analysis and the output

6
format. Chapter 6 presents the study conclusions and recommendations for future data collection

and analysis.

Frost penetration plots are given in appendix A for each ofthe 27 SMP sections with

electrical resistivity probes. Sample time-series electrical resistivity plots used in the computer

program FROST are given in appendix B. The computed parameter table

SMP_FROST_PENETRATION is given in appendix C.

7

CHAPTER 2 - THEORETICAL CONSIDERATIONS FOR FROST PENETRATION

PREDICTION AND MEASUREMENT

Due to the significant impact offrost on environmental, agricultural, and engineering

systems, many models have been developed to predict the maximum frost penetration depth and

the rate of penetration for specific areas. These models are based on thermodynamic laws of

energy and moisture balance. Techniques have also been developed to measure frost penetration.

This chapter provides a briefreview offrost penetration prediction models and methods offrost

depth measurement.

Frost Penetration Prediction Models

A number oftheoretical models have been developed to predict frost penetration at a

specific location, given climatological data and soil type. These models are discussed in

references 3 through 13.

Frost Penetration Measurement Using ER

To better understand the freezing and thawing processes, and to be able to predict their

occurrence, more data about the frost depth and rate offormulation at various regions and

various climatic and geological conditions are needed. Frost tubes have been used as a direct

method to measure frost depth in soils. In addition, soil temperature and soil ER have been used

to estimate the depth offrost penetration in soils. Since the LTPP program uses ER probes to

measure frost penetration, this section describes some ofthe theoretical considerations for ER

data.

Factors Influencing Soil ER

It is important to realize that the ER of a soil element is the result ofthe resistivities ofthe

element components: soil particles, water, ice, and air. The ER ofthe soil particles is large

enough to consider them as an insulator. The water resistivity is very low, depending on the salt

content ofthe water. Ice resistivity is much larger than water resistivity. Following are the

factors that influence the bulk resistivity of a soil.

Type of Soil

Whether a soil is largely clay or very sandy can change the resistivity very much. In

addition, there is a wide variation in the resistivity range within a given soil type, depending on

the soil composition. Tables 2 and 3 show the resistivity ranges of different soils from two

different sources. The tables show the wide range of variation within and between different soil

types. It should be noted that the maximum resistivity ofthese soils does not correspond to

completely dry conditions (the effect ofmoisture content is demonstrated in table 4).

9 Preceding Page Blank
Table 2. Resistivities of different soils (U.S. Bureau of Standards Technical Report 108).

Resistivity, ohm-em

Soil Average Minimum Maximum

Fills-ashes, cinders, brine wastes 2,370 590 7,000

Clay, shale, gumbo, loam 4,060 340 16,300

Same-with varying proportions ofsand and gravel 15,800 1,020 135,000

Gravel, sand, stones, with little clay or loam 94,000 59,000 458,000

Table 3. Resistivities of different soils (Evershed & Vignoles Bulletin 245).

Resistivity, ohm-em

Soil

Minimum Maximum

Surface soils, loam, etc. 100 5,000

Clay 200 10,000

Sand and gravel 5,000 100,000

Surface limestone 10,000 1,000,000

Limestones 500 400,000

Shales 500 10,000

Sandstone 2,000 200,000

Granites, basalts, etc. 100,000 (average)

Decomposed gneiss 5,000 50,000

Slates, etc. 1,000 10,000

Moisture Content

Because the resistivity ofwater is much less than that ofsoil particles, the bulk resistivity

decreases as the moisture content increases. Table 4 shows examples ofthe effect ofmoisture

content on soil resistivity. The table shows the dramatic decrease in soil resistivity with an

increase in moisture content, especially after the first 2.5 percent moisture content by weight.

For the two types ofsoil listed in the table, it is evident that the soil is a good insulator when dry.

The introduction of 15 percent moisture content led to a decrease ofresistivity by a factor of

about 50,000.

10
Table 4. Effect ofmoisture content on soil resistivity.*

Moisture Content, % Resistivity, ohm-em

by Weight Top Soil Sandy Loam

0 1,000 * 106 1,000 * 106

2.5 250,000 150,000

5 165,000 43,000

10 53,000 22,000

15 21,000 13,000

20 12,000 10,000

30 10,000 8,000

*From "An InvestigatIOn ofEarthing Resistance," by P.l Higgs, I.E.E. Journal, vol. 68, p. 736, February

1930.

Dissolved Salt Concentration

Pure water has high resistivity that is greatly reduced by adding salts. In natural soils,

salts such as sodium chloride, copper sulphate, and sodium carbonate can exist. Since water is

the most important component through which current is passed, the soil resistivity is also

influenced by the dissolved salt concentration. Table 5 demonstrates the effect ofsalt content on

soil resistivity.

Table 5. Effect ofsalt content on soil resistivity.*

Added Salt % by Weight Resistivity,

of Moisture ohm-em

0 10,700

0.1 1,800

1.0 460

5 190

10 130

20 100

*For sandy loam-mOIsture content, 15 percent by weIght, temperature, l70e (63 OF). Getting

Down to Earth-Manual on Earth-Resistance Testingfor Practical Man, 4th edition, Biddle

Instrument, April 1981.

Temperature

Little information has been collected regarding the effect oftemperature on resistivity.

However, two facts have been observed: water present in soil mostly determines the resistivity,

and temperature serves as a catalyst that increases the conductivity ofthe dissolved ions in the

water. An increase in temperature markedly decreases water resistivity. Table 6 shows the effect

11
oftemperature on soil resistivity. The table demonstrates a large reduction in soil resistivity with

temperature increase. It should be noted that there is a large increase in resistivity between ice at

O°C and water at O°C. The resistivity keeps increasing as the temperature decreases below O°C.

Table 6. Effect oftemperature on soil resistivity.*

Temperature Resistivity,

ohm-em

°C of

20 68 7,200

10 50 9,900

0 32 (water) 13,800

0 32 (ice) 30,000

-5 23 79,000

-15 14 330,000

* For sandy loam, 15.2 percent moisture.

From the above discussion, it is evident that the interpretation ofER data ofsoils is a

complex matter. Soil ER varies with temperature, moisture content, salinity, soil type, and

freezing conditions; hence, such data must be analyzed carefully.

Mixing Model for Electrical Resistivity

The soil resistivity may be computed using mixing models. Assume that the soil cylinder

through which the current is passed consists offour homogenous parts, each with a volume

component as follows:

soil particles

liquid water

frozen water

Ifthe electrical current passes through the soil components in a parallel manner, then the

bulk (equivalent) resistivity (Pe) may be expressed by:

where PFW

PLW

Ps

=

=

resistivity ofthe frozen water, typically = 500,000 (ohm-m).

resistivity ofthe liquid water, typically = 20,000 (ohm-m).

resistivity ofthe soil solids, typically in millions of ohm-m.

12

(1)
Ifthe soil is not expansive (subject to frost heave or moisture-induced volume changes),

then the volumetric changes resulting from freezing can be neglected. In this case, the sum ofthe

frozen and liquid water volumes equals the initial (before freezing) volumetric moisture content

(Vw). If air volume is neglected, then the solids volume is the total volume less the initial water

content. The above equation can be rewritten in terms ofthe initial volumetric moisture content

as follows:

(2)

where: Vw~ Vpw.

As expected, the above equation shows that when the ice content (Vpw) is zero, then the

ice resistivity term will vanish and the bulk resistivity will only depend on the moisture content

and the resistivity of water and solid particles. Also, when the initial water content (Vw) is zero,

the first two terms in the denominator will vanish and the bulk resistivity equals the solid

particles' resistivity.

When the ice volume (Vpw) equals the initial volumetric moisture content (Vw), the liquid

water resistivity term will vanish and the bulk resistivity is determined by the ice content and the

resistivities ofsoil particles and ice. Equation 2 also shows that when the ice content increases,

the value ofthe denominator is reduced (since the liquid water resistivity is less than that ofthe

frozen water), and the bulk resistivity is increased.

The change in bulk resistivity per unit change in the volumetric ice content may be

obtained analytically by differentiating the bulk resistivity with respect to the volumetric ice

content. Alternatively, numerical substitution in the above equation can be used to quantify the

change in resistivity due to change in ice content.

Example

Assume the following for a soil:

• Initial volumetric moisture content = 10 percent.

• Ice resistivity = 500,000 ohm-m.

• Water resistivity = 20,000 ohm-m.

• Soil particle resistivity = 3,000,000 ohm-m.

Table 7 shows the expected resistivity as a function ofthe ice content (changes from 0 to

10 percent) using equation 2. The change in resistivity is shown in the third column. The table

shows that the theoretical bulk resistivity increases when ice content increases and that the

amount ofincrease is a function ofthe amount ofremaining unfrozen moisture in the soil. The

use ofthese equations in the analysis ofER data requires the knowledge ofmoisture content and

the electrical resistivity parameters for all soil components. It should be noted that the electrical

13
resistivity ofwater is greatly influenced by the water salinity. Also, soil particle resistivity is a

function ofthe chemical composition ofthe particles; hence, some field calibration may be

required to use the above models.

The mixing models are a simplification ofreality. Many factors can influence the

measured resistivity, such as the complexity ofthe path ofthe electrical current within the soil

mass (not in series or in parallel, but in a combination of both). In addition, factors such as the

quality of contact between the electrodes and the soil, and between the soil particles may

influence the measured bulk resistivity.

Table 7. Change in bulk resistivity per unit change in the volumetric change in ice content.

Percent Ice Resistivity (ohm-m) Change in Resistivity

Content (Equation 2) (ohm-m)

0 188,679 ---

I 207,469 18,790

2 230,415 22,946

3 259,067 28,652

4 295,858 36,791

5 344,828 48,970

6 413,223 68,395

7 515,464 102,241

8 684,932 169,468

9 1,020,408 335,476

10 2,000,000 979,592

14
CHAPTER 3 - PRELIMINARY DATA ANALYSIS

A number of diagnostic studies performed to explore the characteristics ofER and

temperature data can be used to identify the freeze state ofsoils. Diagnostic studies were aimed

at identifying erroneous or suspicious data that should not be used in the analysis ofthe freeze

state. This chapter presents the findings ofthe diagnostic studies that were conducted to identify

data concerns.

Characteristics ofTemperature Data

A variety of visualization tools were utilized to determine the characteristics of

temperature data. These include categorized line plots, summary statistics, and histograms ofthe

time of maximum and minimum temperature. The following are the highlights ofthe

temperature characterization study:

1. Temperature versus time plots at each thermistor take a sinusoidal shape. The

amplitude, which represents the seasonal variation in temperature, declines with

thermistor depth.

2. Diurnal variation is represented by the separation between the minimum and

maximum daily temperature curves. It should be noted that the separation

decreases with the thermistor depth, until the maximum and minimum

temperatures become almost identical in the lower eight thermistors.

3. The deeper thermistors show smoother time-series curves than the upper ones.

This indicates that day-to-day variations are smaller for deeper thermistors.

4. Maximum and minimum daily temperature curves are mostly parallel. This

indicates that both maximum and minimum temperatures increase on hot days.

5. Temperature time-series curves at various thermistors are generally parallel. This

indicates that when a day is hot, temperature should increase in all thermistors,

and vice versa. However, a lag may exist between the upper and lower

thermistors.

6. Based on the review ofthe time-series plots and summary statistics, the general

temperature trends were reasonable and as expected. Comparisons between

various temperature measures, taken at various times and locations, showed that

the majority oftemperature data appear reasonable and agree with expected

trends. Missing, erroneous, and suspicious data were identified so they could be

isolated from the database and their sources investigated.

7. For the purpose ofthis study, subsequent analysis excluded all suspicious data. It

should be noted that only a few thousand records appear to be suspicious.

15
Compared to the size ofthe total data set (about 400,000 records), the portion of

suspicious data is less than 1 percent.

8. There are about 6,000 records ofER data for which soil temperature is not

currently available. Based on observation 3 above, temperature within 2 days

from the measurement date can be used as a substitute. More details on this are

presented in chapter 4.

The Latent Heat ofFusion Phenomenon

As discussed earlier, temperature by itselfis not a reliable indicator ofthe freeze state of

soils. Although a soil temperature above ODC may confirm that the soil is not frozen,

temperature cannot confirm freezing for the following reasons: First, the freezing point may

change due to salt concentration in the soil and in the pore water. Second, ifthe soil is dry, then

there will be no moisture to freeze, regardless oftemperature. Third, during spring thaw,

temperature may stay constant at O°C; hence, there could be uncertainty in the freeze state

determined if only temperature data are used. It is also known that these conditions could extend

deep in the soil, possibly resulting in a large error in determining the frost depth.

To evaluate the usefulness oftemperature for identifying freezing conditions, a set of

conditions was defined to indicate that the soil is in the process offreezing. These represent the

phenomenon ofthe latent heat offusion, that is, when the soil is undergoing freezing (or

thawing), temperature stays constant about the thawing point until the entire water body is frozen

(or thawed). It is assumed that this process takes a long time because the subsurface temperature

changes usually occur at a slow rate. The following conditions are assumed to indicate a phase

change:

• Temperature is at or below ODC.

• Maximum and minimum daily temperatures are the same (constant temperature

during that day).

• There is no change in the average daily temperature in at least 2 consecutive days.

This condition is added because it was found that the day-to-day temperature

changes in the lower thermistors are normally small (less than 1DC). If only the

zero temperature variation in one day is used to identify phase change, then too

many phase change points would be identified, many ofwhich would be false.

The above conditions were programmed, and any particular thermistor satisfying these

conditions on a given day was identified. Line plots were generated for each section and

thermistor, with the average temperature plotted against the time ofthe year. Points where phase

change took place were identified on the plot. Figure 3 is a sample plot showing the possible

points of phase change for section 833802. It should be noted that the upper three thermistors

were excluded from this analysis because they are embedded in the surface layer.

16
Section 833802

-

-1

:~ Ell]"=- ~:tt[ttt[:l:::::i:djjj~;:::::::::[[[tt[tm:·::::!.i:· :[~t[ttm::!:::;::::j;jjj~;::;::i:[t::ttt::m..::.:::.; ·:[::ttt[[:l:!:·::jij;jj;;:;;'::::::::t[mm::!;

1: '::,:;t:::::::::1:U:j~:::: i::;.:';:': ~,;:::::::::': :\:::;~;;:;::::j::::::1Uj)::::::: i:;:;,:;:,;:::::::::: ::. :::~;;;:::'::.::::j)jU: i:::i::::;:.::::::::::.t·, 1 ::::::::::::::;::::::::::::::::::::::1 0 ;StSJ::tUUtG2SSt0?:

THERM_NO: THERM_NO: THERM_NO: THERM_NO:

8 9 10 11

~1~EElIEI!!

1 :·:[::[:[:m::!:!J:,,;····;'::'·::::tt:mm::·r ·:::::[:tm:··:·::m:';;'i";:::'·':::tttttm:::::.J:: .:::[ttmw:::::!!m:'~~:::::;::':·::::ttm!::::::·.: :'::[t:t[tm:::::m:~~~:::::;:::':::ttttm:::!:::: .. . . .

=i~~~~

THERM_NO: THERM_NO: THERM_NO: THERM_NO:

4 5 6 7

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............. . .

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+-

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THERM_NO: THERM_NO: THERM_NO:

12 13 14

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;;:.;.:,:. :.:.:.;.:.:.:.:.:.:.:.:.:.:~~.:;:,:;:;.iii; j:'; ;;.;;:.:.:.:.:.:.:.:.:.: ':';;:';' :.:.:.:.:.;.:.:.:.:.:.:.:.:.:.:.:.:.:;;; iii j i:' i;':' ;:.:.;.:.;.:.;.;.;.;.: ; j; ii; :.:.:.:.;.;.:.:.:.:.:.:.:.:.:.:• .:.:; iii i j j;;':,:';' i:·:·:':':.:.:·:':·;': ..............................................................- .

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=i ·:[:·;[[:[:::II·i:]]]jjj]]]:::::::[:::[[[imJ·:Ii. ·::::[[[:WIIiII]l]j)J:J:::::::::::[[[[ii[iIII:·i ·::::[:mm!·:I:·I;;JJ)J::::::::::::[::;:IIIII·II~

THERM_NO:

16'

THERM_NO:

17

THERM_NO:

18

THERM_NO:

15

AV TEMP

.....*.... PHASE CH

Time (Days)

Figure 3. Phase change events using latent heat affusion.
Based on inspection ofsuch plots, the timing and pattern of phase change points are very

reasonable. Test sections with high temperatures, by definition, did not experience phase

change. Some sections showed a persistent pattern with two distinct phase change points

(possibly indicating freezing in winter and thawing in spring). In other cases, some upper

thermistors did not exhibit phase change, while lower ones did. That can be caused by the

absence ofmoisture in the upper layers.

Table 8 summarizes the results ofthis analysis. The table shows a phase change in 20 of

45 sections. Out ofthe 25 sections that did not exhibit a phase change, 20 had a minimum

temperature above OCC. The closeness ofthermistors that exhibit a phase change (i.e., freezing

thermistors being adjacent) is expected, since ice formation is more likely to be in a form of

continuous layers rather than in the form of broken lenses, although the latter also is possible.

Limitations

There are limitations in the use ofthe latent heat offusion phenomenon for identifying

the freeze state. One limitation is caused by the frequency oftemperature measurement. As

indicated earlier, in order to define the process offreezing, we analyze the temperature data. If

the temperature data do not change for more than 2 days, it is assumed that the phase change may

be taking place. If a large drop in temperature caused a quick phase change that lasted only a

fraction of a day, then the system cannot detect such an event.

Most upper thermistors did not indicate a freeze state, even when temperature was

significantly below OCC. It is possible that the temperature change in the upper thermistors is

faster than that ofthe lower ones, and that the phase change events in the upper thermistors

(which may have occurred in less than a day) were not detected. Also, if a phase change

occurred in fractions of2 days (i.e., starting some time in one day and ending some time in the

next), it will not be detected by the algorithm because the temperature may be variable on both

days.

Another limitation is that the day-to-day variations in the lower thermistors are small. As

indicated earlier, the day-to-day variations in the average temperature in the lower nine

thermistors are less than 1cC. Hence, a constant temperature does not necessarily mean a phase

change. Plots oftemperature data showed constant temperature in both cold and hot conditions.

Summary

Based on the review oftime-series plots and summary statistics, the general temperature

trends were found to be reasonable and as expected. Comparisons between various temperature

measures, taken at various times and locations, showed that the majority oftemperature data

appear reasonable and agree with expected trends. Missing, erroneous, and suspicious data were

identified so they could be isolated from the database and their sources investigated.

18
Table 8. Summary of phase change analysis.

Minimum Thermistors with Minimum Thermistors with

Section Temp.,oC phase change Section Temp.,oC phase change

041024 3.9 none 364018 -12.9 5-18

063042 3.9 none 371028 -0.1 none

081053 -11 none 404165 1 none

091803 -5.5 none 421606 -5.8 5-7

131005 3.9 none 469187 -10.4 5-6, 7-10

131031 0.7 none 481060 8.4 none

133019 0.5 none 481068 4.2 none

161010 -9.6 4-9 481077 0 none

183002 16 6-9 481122 6.3 none

204054 -3.4 none 483739 7.4 none

231026 -15.8 5-16 484142 9.5 none

241634 -1.7 none 484143 11.4 none

251002 -9.6 none 491001 -12.3 none

271018 -22.1 9-18 493011 -0.8 none

271028 -25.6 12-18 501002 -17.4 9-14

274040 -23.3 6-18 533813 -0.4 none

276251 -27.9 6,9, 11, 13-18 561007 -8.8 6-8

281016 0.6 none 831801 -29.1 9-18

281802 2.5 none 833802 -24.9 5-18

308129 -15.1 10-12 871622 -21.6 11-16

313018 -10.3 10 893015 -20.4 9-14

331001 -12.1 9-13 906405 -28.3 7-18

351112 2.3 none

19
The use ofthe latent heat offusion phenomenon to identify the timing ofthe soil phase

change appears promising. The conditions used to identify a phase change event are negative

temperature (below O°C) and constant temperature over a period of2 days. These conditions

identified a phase change in 20 of 45 SMP sections, with a plausible profile and reasonable

timing for a phase change. However, there is a limitation dictated by the arbitrary assumption of

a 2-day time period over which constant temperatures indicate a phase change. In actuality, this

period is variable, depending on the rate oftemperature change and the thermal properties ofthe

soil. This limitation makes the latent heat offusion method incomplete. Supporting measures

from other sources (such as resistivity, resistance, and voltage data) are needed to complement

the latent heat offusion method.

Characteristics ofER Data

A diagnostic study ofER data was undertaken. The objective ofthe diagnostic study was

to verify the consistency ofthe data and the reasonableness ofresistivity measures. Of particular

interest was the relationship between resistivity measures and the definition ofthe characteristics

of ER data as affected by the freeze state ofthe soil. A brief discussion ofthe analysis methods

and results is presented below.

Consistency ofER Data

To investigate the consistency ofthe three ER measures (resistivity, resistance, and

voltage), overlay plots ofthese three measures were generated. Each plot showed the variation in

voltage, resistivity, and contact resistance at a given depth as a function oftime. For each

resistivity measure, the within-same-day values were averaged to generate a single value at each

experiment depth. The generated plots were examined with two questions in mind: Are the plots

ofthe three measures parallel (i.e., are the measures consistent with each other)? and Are the

measures reflective ofthe season (i.e., showing very high values during the freezing season)?

The correlation coefficients between each pair ofresistivity measures were computed.

The correlation coefficient quantifies the magnitude of correlation between the two variables; the

squared correlation coefficient represents the amount of common variability ofthe two variables.

The results ofthese analyses may be summarized as follows:

• Agreement between resistivity measures is reached in 62 percent ofthe cases; this

includes partial and total agreements. Hence, more reliable results will be

obtained when using all measures, rather than a single one.

• Agreement between resistivity measures cannot be reached in 38 percent ofthe

cases. Hence, different results can be reached using different resistivity measures.

• In 56 percent ofthe observations, some or all resistivity measures were successful

in identifying freezing conditions. Hence, more reliable results are obtained using

all measures rather than a single method.

20
• In 44 percent ofthe observations, none ofthe resistivity measures were successful

in identifying freezing conditions. Hence, there seems to be a reliability problem

with resistivity data.

Variability Characteristics ofER Data

In this analysis, the variation characteristics ofresistivity measures were studied. Three

variability components were quantified for each section: same day non-winter, same day winter,

and diurnal electrode variabilities.

Same Day Non-Winter Variability

For each section, the magnitude of variation between any two depths in non-winter

months (May to November) was computed. The maximum values were retained as an upper

limit ofthe variability. These measures were computed for resistivity, resistance, and voltage

values. This variability encompasses electrode-to-electrode variability in the same day due to

material change and diurnal variations at each electrode, but it does not include changes due to

freezing or thawing since only the non-winter measurements are considered.

Same Day Winter Variability

For each section, the magnitude of variation between any two depths in winter months

(December through March) was computed. The maximum values were retained as an upper limit

ofthe variability. These measures were computed for resistivity, resistance, and voltage values.

This variability encompasses electrode-to-electrode variability in the same day, diurnal variations

at each electrode, and changes due to freezing or thawing, since winter measurements are

considered.

Diurnal Electrode Variability

The diurnal electrode variability refers to the variation between the readings taken during

the same day at the same electrode. This variability does not include the effect ofthe change in

material type. Because the time between measurements is short (a few hours), no significant

change in moisture condition or freeze state is expected to take place during the measurement

time. Therefore, this variability provides a measure ofthe stability ofresistivity measures.

The results ofthe comparative evaluations and statistical analysis performed on the above

variabilities revealed the following characteristics:

• There is a large within-day non-winter variability in all resistivity measures. That

variability is attributed to non-freezing (or thawing) factors, including the effect of

material variability on resistivity. In some sections, this variability approached

the extent ofthe resistivity, resistance, and voltage scale, leaving only a small

margin for changes due to actual freezing conditions.

21
• The within-day winter variability is not always larger than that ofthe non-winter

variability. This would indicate that the section in question has not experienced

freezing. However, some measures (voltage) showed this condition in sections

that are known to experience freezing.

• Approximately one-third ofthe sections had a ratio of winter to non-winter

variability ofless than 1 (indicating no freezing took place), one-third had a ratio

greater than or equal to 3 (indicating freezing took place), and one-third showed a

ratio between 1 and 3 (indicating that it will not be easy to infer whether freezing

took place).

• The correlation coefficient between contact resistance and resistivity variability

ratio is notably large (0.9), compared to those of other pairs. Hence, contact

resistance and resistivity are more likely to be in agreement than any other pair of

resistivity measures. Furthermore, the agreement between contact resistance and

voltage is better than that between the voltage and resistivity. This supports the

earlier observation that contact resistance seems to be in better agreement with

other measures.

• On average, the resistivity measures have a larger value when the temperature is

below freezing. In some cases, the difference in the average values is large

enough to enable reliable interpretation. In other cases, the difference is not large

enough to be distinguishable among other variabilities, including diurnal,

seasonal, and electrode-to-electrode variability. It is noted that resistivity

measures are more meaningful when analyzed in conjunction with temperature

data.

• Resistivity measures showed various degrees oftime stability. Diurnal electrode

variability ranged from single-digit numbers to readings in the hundreds of

thousands. Most resistivity and all voltage readings had an average variability of

less than 100 (measurement unit). Most contact resistance had an average

variability ofless than 500 ohm.

• In some cases, the diurnal electrode variability can be very large. There are two

possible causes for such a large variability: data collection error and unstable

measurements. It is possible that data collection error is the primary cause ofsuch

variability.

• Approximately 7 percent ofresistivity records were suspicious. Their inclusion in

the analysis may cause significant noise and unreliable results. These records

were flagged for investigation.

• Each resistivity measure was found to have some advantages over other measures.

Contact resistance seemed to be more in agreement with other resistivity measures

22
than are resistivity or voltage. Resistivity data had lower diurnal variability and

had more sections with larger winter to non-winter variability ratios than contact

resistance. Voltage data seemed to be more stable (low diurnal variation) and had

no zero value records.

Potential Data Interpretation Algorithms

The research team worked on developing an automated procedure that combines ER data

with soil temperature data to determine the soil freeze state. A number of candidate procedures

were investigated for use in interpretation ofER and temperature data in a systematic, structured

manner. The following is a brief description of each approach.

The first approach is to average the lowest three ER readings, then multiply that value by

a multiplier (e.g., 3, or site dependent). That resulting value is then used as a threshold value.

Any higher reading is considered indicative of a frozen state. Values that fall between the

average and the threshold are considered in transition, and any value below the average is

considered not frozen. This approach may not work if all sensors are frozen. Many ofthe ER

records had such a large difference between the maximum and minimum readings that it was

clear that a multiplier of 3 would not yield reasonable values. This suggests that the multiplier is

site dependent. The threshold may be calculated using an "arbitrary but consistent" number of

standard deviations above the average ofthe lowest three sensors.

Another approach is to consider the average ofthe maximum and minimum values as a

threshold. Again, this method will not work if all probes are frozen or all probes are not frozen.

It could work ifthere is a large difference in resistance between the maximum and minimum

values.

The third approach is to look at the year-round ER values of each probe and establish the

ER values corresponding to freezing and thawing conditions for that probe. A threshold value

for each probe may be calculated as described above. The advantage ofthis method is that it

compares the readings at the same probe, which cancels the effect ofmaterial variation with

depth. The problem, however, is that without the year-round values, there will not be a complete

assessment ofthe variation in resistivity readings and, therefore, it would not be possible to

establish freezing and thawing values.

The fourth approach is to find the maximum drop in ER (theoretically associated with

thawing conditions) and the associated depth. Check the soil temperature at one point above and

one point below that point (the temperature thermistor directly above and below that point). If

the temperature of at least one ofthese points is less than or equal to 0 0C, then the above point of

resistivity drop corresponds to the frost depth and the search should be stopped. If not, then

move on (toward the pavement temperature decrease, excluding data below the first point of

maximum resistivity drop), searching for the next maximum drop in resistivity. Repeat the

process until a large ER drop corresponding to a negative temperature (frost conditions) is

reached, or a positive temperature is encountered throughout the upper resistivity probe (no frost

conditions). The problem with this algorithm is its ability to produce false frozen conditions

23
corresponding to small changes in ER values, unless it is "told" what to consider a large enough

change in ER.

The fifth approach is based on finding two resistance (or resistivity) threshold values that

correspond to unfrozen and frozen soil conditions. These values are section specific. Materials

with resistance values below the nonfrozen threshold (NFT) value are considered nonfrozen.

Materials with resistance values larger than the freezing threshold (FT) value are considered

frozen. Materials with resistance values between NFT and FT are considered to be in a

transitional state. The process offinding the site-specific NFT and FT is that of optimization,

with the goal ofmaximizing the agreement between resistance/resistivity and temperature, in

classifying the freeze state ofsoils.

In addition to its capability to handle the site-specific nature ofresistance values, the fifth

approach worked very well when tested on data from a northern test section (section 893015 in

Quebec). Hence, the approach was initially recommended for use. Like all other approaches, the

recommended approach assumed an L-shaped curve between temperature and ER; however,

diagnostic studies showed that the expected L-shaped curve between temperature and ER data is

not vividly present in a good portion ofthe data, and that ER data may contain too much noise to

be interpreted automatically.

Implications for ER Data Interpretation

The results from the above diagnostic studies and experiments with interpretation

algorithms suggest the following:

• The expected significant resistivity peak during freezing has been observed in

about 33 percent ofthe data, cannot be verified in 33 percent ofthe data, and is

not apparent in 33 percent ofthe data. Also, freezing-triggered ER peaks are

masked by large non-winter, seasonal, and diurnal variabilities. In addition, more

noise is possibly introduced by data collection problems. With the noise inherent

in ER data, fully automated interpretation procedures for ER data are not

considered reliable at the present time.

• Capitalizing on the strengths of a number ofreadily available algorithms, a

successful ER data interpretation method may be developed. However, unless a

better understanding and control ofER data is realized, the method of

interpretation may be interactive rather than automated.

• Resistivity measures are not fully redundant, and each measure has some

advantage over the others. Therefore, more reliable results can be obtained by

using all measures simultaneously. Furthermore, the fact that very large

resistivity values were observed in unfrozen conditions (e.g., section 041024 in

Arizona) suggests that temperature data should play an important role in the

interpretation of ER data. Therefore, the analysis ofER data was planned to be

based on resistivity, contact resistance, voltage, temperature, and other supporting

24
data, such as time ofthe year, location ofmeasurement, and knowledge of

established freezing patterns at the given location.

25

CHAPTER 4 - INTERACTIVE PROCEDURE FOR INTERPRETATION

OFERDATA

An interactive computer program has been developed to process electrical resistivity data

to determine the frost penetration in SMP sections. The program, called FROST, determines the

freeze state of a soil based on the values ofthe three ER measures (voltage, contact resistance,

resistivity) and soil temperature. Based on time-series plots of all ofthe ER variables at each

depth and temperature data, the user determines (using established guidelines) the threshold ER

value above which the soil is considered potentially frozen. Ifthe soil temperature is negative

and the ER value is larger than the established threshold, the soil is considered frozen.

The program stores the results in a database format, which can be retrieved and

reprocessed later. Other graphing capabilities in the program include frost penetration plots, ER

time-series plots by electrode, and ER profile by depth at a given date-all designed to assist the

user in interpreting the data.

Procedure Highlights

The following are the highlights ofthe procedure:

• Uses five variables simultaneously in the analysis (three ER measures, soil

temperature, and date) to improve reliability.

• Uses normalized ER parameters that are based on relative values at each

electrode, thereby reducing the noise due to material variability. Normalized ER

measures are numbers between 0.0 and 1.0 representing the magnitude of

resistance, resistivity, or voltage relative to their extreme values measured at a

specific electrode. For instance, a normalized voltage value of 0.0 indicates that

this reading is the lowest voltage value recorded at that electrode. A value of 1.0

corresponds to the highest value ever recorded at that electrode.

• Uses average ER values for a given date to stabilize diurnal variability.

• Software assists the user with warning messages and a variety of graphing views.

The procedure requires user interaction and familiarity with ER data interpretation.

Program Decision Tree

Figure 4 illustrates the decision process used by FROST to determine the freeze state.

Table 9 provides the list ofmonths during which negative soil temperatures were observed at the

27 SMP sections being assessed as part ofthis study. This information given in table 9 is

necessary for cases where soil temperature is missing.

27
Normalized Electrical

Resistivity

At least one measure is

larger than Threshold

Value?

>----NOI-------.~IL_ __N_o_F_r_e_ez_e _

Ys

Greater than 0 °C No Freeze

Average Temperature Freeze Less than or equal to

OoC 1---.L- -----J

Month ofthe Year

Cold Winter

Month?1 N°-1L__N_o_F_r_ee_z_e_----.J

YeS---"~LI F_r_e_ez_e _

lCold winter months were established based on the daily temperature records for each site. Cold winter

months are defined as months during which negative (below O°C) soil temperature values were observed at

the top ofthe base layer.

Figure 4. FROST decision tree.

28
Table 9. Cold winter months during which freezing is expected

Section From To

041024 - -

081053 Nov Feb

091803 Jan Mar

161010 Dec Feb

183002 Dec Mar

204054 Dec Feb

231026 Nov Apr

241634 Jan Feb

251002 Jan Mar -

271018 Nov Apr

271028 Nov Apr

274040 Nov Apr

276251 Nov Apr

308129 Nov Mar

Section From To

313018 Dec Mar

331001 Nov Mar

364018 Dec Mar

421606 Dec Mar

460804 Nov Apr

493011 Nov Mar

501002 Nov Apr

561007 Nov Apr

831801 Oct Apr

833802 Oct Apr

871622 Nov Apr

893015 Nov Apr

906405 Oct Apr

User Influence

Selection ofreasonable values for the ER threshold is important. Ifthe user selects too

Iowa value, the freeze state will be determined based on temperature only. Ifthe user selects too

high a value, the freeze state will be "no freeze," regardless oftemperature. Guidelines for

selecting the ER threshold values are presented later in this chapter.

Analysis Steps

The determination ofthe freeze state using ER and soil temperature data may be

described in terms ofthree steps: preprocessing, processing, and smoothing. A brief description

of each follows.

29
(3)

Preprocessing

FROST uses data from six IMS tables. To facilitate data acquisition and reduce the

program run time, a single database table was created to store all data elements required by

FROST. The ACCESSTM database table from which FROST reads and to which it writes needs

to be in a specific format. The following steps are required to produce the intermediate variables

required by FROST:

1. Compute measurement depth (the average depth ofthe electrodes used in the

measurements). It is noted that each voltage and contact resistance measurement

is performed using two electrodes and each resistivity measurement is performed

using four electrodes.

2. Compute the average resistance, resistivity, and voltage for testing day and

measurement depth.

3. Query contact resistance, resistivity, and voltage, for matching section, date, and

measurement depth. The query should be designed to return all available records

where at least one ER measure is available.

4. For each measurement depth, normalize resistivity, resistance, and voltage with

respect to their extreme values. For instance, normalized resistivity may be

computed according to the following formula:

R- R .. R = I mm, I

N,i R .- R .. max, I mm, I

where: RN,i

Ri =

R maxi

Rmin,i

normalized resistivity, at measurement depth i.

actual resistivity taken at measurement depth i.

maximum resistivity value measured at depth i.

minimum resistivity value measured at depth i.

The normalized contact resistance and voltage can be obtained in a similar

fashion. It should be noted that the actual and normalized ER values are linearly

related and the normalization process does not "distort" the ER profiles.

5. Interpolate the average soil temperature at each ER measurement depth. As

shown in figure 5, the measurement depths ofER probes do not match those of

thermistor probes.

30
Upper

thermistors

@76mm

Lower

thermistors

@152mm

Electrodes 36 @ 50 mm

Instrumentation Hole 250

mmdiameter

L

Thermistor Probe

Tl

x

ER measurement depth

T2

Thermistor Probe Resistivity Probe

Not to scale

Figure 5. Schematic diagram ofthe arrangement ofthermistor and resistivity probes.

To obtain the temperature at the ER measurement depth (as shown in the

schematic), the following linear interpolation formula was used:

where: Ti = interpolated temperature.

T1 temperature at the upper thermistor.

T2 = temperature at the lower thermistor.

X = distance from the ER measurement depth to the

upper thermistor.

L = distance between the two thermistors.

(4)

6. Ifsame-day temperature is missing, then use temperature within 2 days. In 25

percent ofthe records (6,000 of 24,000), soil temperature data were missing.

Missing temperature data can affect the determination ofthe frost penetration.

Since the average absolute difference between the average temperature of any 2

consecutive days was 0.4 DC, temperatures within 2 days ofthe site visit date were

used to fill in the missing temperature data. In so doing, the number ofmissing

temperature records was reduced to about 1,800. The hierarchy used to select

available temperature data is shown in table 10.

31
Table 10. Temperature substitution hierarchy.

First Preference Same-day temperature

Second Preference Previous-day temperature

Third Preference Next-day temperature

Fourth Preference 2-days-previous temperature

Fifth Preference 2-days-Iater temperature

7. Determine cold winter months for each section based on historical temperatures.

As shown in the FROST decision tree (figure 4), cold winter months are used to

confirm ER peaks only iftemperature data are missing. Table 9 shows the cold

temperature months for the selected SMP sections, as determined from

temperature data. Cold winter months were defined as those months in which a

negative temperature (below O°C) was measured at the top unbound pavement

layer.

Processing Using FROST

Once the data are prepared and saved in a specific format, FROST is used to determine

the freeze state and frost penetration. As mentioned earlier, FROST requires user interaction to

determine the soil freeze state. The user must input an ER threshold line that separates freezing

from nonfreezing conditions. Guidelines on placing the ER threshold line are presented later.

Based on these values, FROST determines the freeze state according to the decision tree shown

in figure 4.

Smoothing the Frost Profile

In some cases, FROST determines a frost profile that may consist ofthin layers offrozen

soil and thawed pockets ofsoil. Although this may not be uncommon, it is possible that highly

irregular frost profiles may be caused by noisy data. In addition, it is recognized that thawing

and freezing often occur gradually, possibly forming zones oftransitional freeze state. However,

current ER data do not seem precise enough to enable reliable identification ofsuch a transitional

state. As such, the current method ofER data interpretation uses a "freeze/no-freeze"

designation, with the inherent assumption that the transitional freeze state could be upgraded to

freeze or downgraded to no-freeze based on the overall frost profile.

Electrical resistivity probes are placed approximately 50 mm apart, depthwise.

Therefore, the layer thickness resolution for freeze-state determination is 50 mm. However, from

a pavement engineering perspective, a 50-mm-thick layer ofthawed soil between two thick

frozen layers is very likely to be ignored, given the resolution of current structural analysis

techniques. Based on these considerations, smoothing ofthe frost profile was implemented as

follows:

32
• After using time-series ER plots to define the threshold line at the 35

measurement depths, FROST will display the frost penetration profile. The user

should inspect the frost penetration profile and manually change the freeze state at

any location on the graph. Ifthe freezing condition at a particular point is in

disagreement with surrounding points (e.g., the point shows freezing while the

soil above and below show a no-freeze state), then the freeze state ofthat point

could be forced to agree with that ofsurrounding soil. In addition to the option to

manually change the freeze state, FROST includes an option to "smooth" the frost

profile obtained at any given date.

• Selecting the automated smoothing option will invoke a procedure that starts from

the top down, comparing the freeze state at each point with those ofthe points

above and below it. Ifthe freeze state is different from these two points, the

freeze state at the point under consideration will be changed to agree with that of

surrounding points. This procedure is executed for all points except the top point.

Smoothing the frost penetration profile will only result in changes for layers less

than 100 mm thick.

It should be noted that smoothing was performed in only 25 out of approximately 24,000

records, amounting to approximately 0.1 percent ofthe data.

Guidelines for Defining the ER Threshold Line in FROST

The concept involves drawing a threshold line that separates peak ER values from the rest

ofthe data on the ER time-series plots. An ER peak is a relatively large ER value that occurs in

cold temperature and winter months. Peaks are not consistent in their absolute values from one

year to another. Therefore, the threshold line must account for all potential peaks for multi-year

data. The program places a vertical line through each point having the right condition for

freezing according to temperature or season. These points should be inspected carefully, since

the user input will affect only these points. All other data points are automatically considered

unfrozen, regardless ofthe user input.

The user should inspect the marked points and ensure that they are all above the threshold

line. Marked points that do not show an ER peak (i.e., their ER values are not significantly

larger than those of non-winter points) should not be placed above the threshold line, especially

if placing such points above the threshold line may result in lowering the line such that nonwinter readings will fall above the threshold line (which would violate the definition ofthe

threshold line).

Tips

The analyst should be aware ofthe following:

33
• It does not matter how low or high the threshold line is, as long as it separates the

peaks from the rest ofthe data. In many cases, the user has some flexibility in

placing the threshold line and can still achieve the same results.

• Points midway between peaks and valleys should be included with peaks, as long

as they occur in the winter months. In such cases, temperature will determine the

freeze state for such observations.

• It does not matter if one, two, or all three ofthe ER measures are above the

threshold line. If any measure is above the line, it is considered to be a candidate

for a freezing condition. In most cases, the peaks ofER values are at different

magnitudes. Therefore, the user does not need to place all ER peaks for a given

date above the threshold line.

• Points located near the threshold line (especially at a freeze temperature) should

be examined for discontinuity. Ifthe freezing condition at that particular point is

in disagreement with surrounding points, then the threshold line may be moved to

produce more consistent results.

Example

Figure 6 shows an example plot used in the freeze-state determination. The plot

represents the time-series of normalized ER measures and temperature for Saskatchewan section

906405, about 0.4 m below the pavement surface. Based on temperature and season, there are

four incidents of possible freezing in the winter of 1993/1994, five in the winter of 1994/1995,

and two in the winter of 1996/1997. These incidents are marked by a vertical line.

The user first attempts to place all marked points above the line. As indicated earlier,

only the upper ER peak needs to be placed above the line. For instance, in the last possible

freezing incident marked by the right-most vertical line in the graph, the line passes through three

points: voltage (upper curve), resistance, then resistivity (lowest value). Only the upper peak for

voltage needs to be placed above the threshold line.

It can be seen that some ofthe marked points do not show an ER peak and cannot be

placed above the threshold line without bringing the line too low. Such points are left below the

line, indicating a no-freeze condition.

Figure 7 shows a frost penetration profile at section 271028 in Minnesota. Each point in

the graph represents the freeze state at a specific time and location. Dark areas indicate frozen

soil, whereas white areas indicate unfrozen conditions. By clicking on any point on the graph,

the user can access a number of utilities, including an interactive time-series ER plot (similar to

that shown in figure 6), ER versus depth graph for the selected date (similar to that shown in

figure 8), manual freeze-state change option, and frost profile smoothing option.

34
Section 906405: Saskatchewan

Depth = 0.41 m

1.2.-r "'ifl,: ; "TT:I Ir : , : ; .. ····1:. I···; ; ...: 130

, 1 25

1.0+1.11,- -I ,'liliH II, ,/1-" -, -1- +1, ~--1 if '- ~~ • Voltage

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a:::: 0.8 1 .'j';: i '1 i} 10 , ,,__ • Resistance 1 j j l 5

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-20

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M

t:) c o.."St:) U C Q.U C Q.U C Q.U

O ro « a ID ID ID ID ID ID ID 0 m 0 m 0 m 0

Figure 6. Example ER time-series graph produced by FROST.
LD

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36
Section 271028: Minnesota

On Jan/1 0/95 C)

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it.. Resistivity X 50

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• Resistance X 300

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ER

Figure 8. Example ER versus depth plot produced by FROST.

CHAPTER 5 - ANALYSIS RESULTS

The procedure used for ER data interpretation was described in chapter 4. The results of

the analysis are presented in this chapter. To assess the reliability ofthe results, the computed

frost penetration was compared to historical average frost penetration in each section, as found in

climatic maps. Correlations with time-domain reflectometry (TDR) measurements were also

investigated.

Frost Penetration Graphs

For each SMP section, a graph is created to depict the freezing profile during the entire

data collection period. Appendix A contains these frost penetration plots for the 27 SMP

sections that had valid ER data. Figure 9 is an example frost penetration plot for section 231026

in Maine. The graph shows the formation offrost some time between November 10, 1993, and

January 10, 1994 (no readings in December 1993). The frost depth increases until it reaches

1.75 m below the pavement surface on February 28, 1994. It then thaws between March 24,

1994, and April 11, 1994.

Another freezing cycle was observed in the winter of 1994/1995. The frost formed

between December 12, 1994, and January 17, 1995. It reached a maximum depth of 1 m on

February 14, 1995, then thawed before March 6, 1995. It should be noted that the freeze cycle

was shorter and the frost penetration shallower during the winter of 1994/1995 than that during

the winter of 1993/1994. Samples ofthe interpretation plots are provided in appendix B, which

shows the time-series ER plots used to define the threshold line at section 231026 in Maine.

Comparison With Historical Data

The computed frost penetration was compared to the historical value at each test location,

as published in climatic maps. Figure 10 is a contour map ofthe average frost penetration depth

in the United States, adopted from Climatic Maps a/Geologic Interest [14]. The comparison

between computed and historical frost depth is summarized in table 11. The table shows that for

some sections, there is an excellent match, whereas for others, the difference between computed

and historical depths is up to 0.8 m. A perfect match is not expected for the following reasons:

1. The estimates provided in the map are reflective of collective conditions that have

occurred over a period of years. Observed conditions for any given day, week,

month, or even year often differ sharply from those indicated in the map.

2. Within the same region, local conditions may vary due to factors such as soil type,

soil moisture content, sunshine, altitude, or demographic activities. Such

microchanges are not reflected in the map.

39 Preceding Page Blank
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40
-

Q MILES 300

Figure 10. Average depth offrost penetration (inches) in the United States [14].

1 in = 25.4 mm

1 mi = 1.61 km
Table 11. Comparison between computed and average estimated frost penetration.

Historic Average Historic Average Maximum

Section State Frost Penetration Frost Penetration Computed Frost Comments

ill (in) (m) Penetration (m)

041024 Arizona 1 0.0254 0 warm weather

081053 Colorado 20 0.508 0 warm weather

091803 Connecticut 25 0.635 0 mostly warm weather

161010 Idaho 25 0.635 0.75

183002 Indiana 25 0.635 0 no sufficient winter readings

204054 Kansas 15 0.381 0

231026 Maine 35 0.889 1.75

241634 Maryland 7 0.1778 0

251002 Massachusetts 30 0.762 0.75

271018 Minnesota 45 1.143 2.2

271028 Minnesota 45 1.143 2.3

274040 Minnesota 45 1.143 2.0

276251 Minnesota 45 1.143 2.25

308129 Montana 40 1.016 1.05

313018 Nebraska 25 0.635 1.2

331001 New Hampshire 35 0.889 0

364018 New York 45 1.143 2.0

421606 Pennsylvania 15 0.381 0

469187 South Dakota 35 0.889 1.2

493011 Utah 15 0.381 0

501002 Vermont 45 1.143 0.8

561007 Wyoming 40 1.016 0 no winter readings

831801 Manitoba 55 1.397 1.5

833802 Manitoba 55 1.397 1.5

871622 Ontario 55 1.397 1.25

893015 Quebec 60 1.524 1.55

906405 Saskatchewan 50 1.27 2.05

42
3. Although the map data provide estimates for natural (uncovered) land, the

computed frost penetration was for conditions under pavements, which may very

well affect the frost penetration. In addition, snow removal activities carried out

by many northern State highway agencies use de-icing salts on the pavement,

which eventually penetrate the soil and affect frost penetration.

In conclusion, the comparison with historical data showed reasonable agreement in most

cases. In other cases, large discrepancies were found; however, such discrepancies were not

surprising, given the approximate nature ofthe historical estimates and the length ofthe

measurement intervals (approximately 1 month long).

Comparison With TDR Data

The Time-Domain Reflectometry (TDR) technique is being used in the SMP program to

measure the dielectric constant ofsoils, which can be used to compute the in-situ moisture

content. The moisture content computed using TDR data does not reflect the frozen water (ice

content). Hence, when a soil freezes, its TDR-computed moisture content drops, since its

unfrozen moisture content decreases. TDR data [15] were used to confirm the freezing events as

determined by FROST.

In almost all sections, the existence offrost corresponded to a drop in unfrozen moisture

content. Figure 11a shows a plot offrost penetration, and figure 11b is a plot of volumetric

moisture content at section 274040 in Minnesota (adapted from reference 15). Comparing the

two graphs, it is clear that in three winters (1993/1994, 1994/1995, and 1996/1997) frost

penetration coincided with a sharp drop in unfrozen volumetric moisture content.

Similarly, figures 12a and 12b show the seasonal variation offrost penetration and

unfrozen volumetric moisture content in section 041024 in Arizona. As shown, frost was

determined and no sharp drops were observed in the unfrozen moisture content. The above

comparisons show that although the two measurements implement different techniques, there

was a good correspondence between TDR data and the freeze state as determined by FROST.

Reliability of Results

In theory, the overall system reliability (R*) of a serial system that implements a number

of components, each with item reliability Ri> may be computed by the following formula [16]:

n

R * = IIR;

;=1

(5)

In serial systems, the components feed from one to another, and any failure in one item

will affect all other items. Hence, the reliability ofthe system is less than the smallest reliability

of all items, and the system will fail if any component fails. Ifthe components are connected in a

parallel fashion, then the system reliability may be computed by:

43
Frost Penetration at Section 274040: Minnesota

C'? C'? "<t "<t "J' "<t LO LO LO LO CD CD CD CD "" "" "" "" 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0)

0- U .... t: 0- U .... t: 0- U .... t: 0- U .... t: 0- > Q) Q) «l ::J Q) Q) «l ::J Q) Q) «l ::J Q) Q) «l ::J Q) 0

CJ) 0 ::2: ...., CJ) 0 ...., CJ) 0 ...., CJ) 0 ...., CJ) z

0.0

• Freeze

-0.5

-

-.c

E

-1.0 ~~ -

Q.

Q)

C

-1.5

D No Freeze

-2.

l-j

tJ

-2.51

a. Frost Penetration

r-+- TOR_No 1 l

---TOR_No 2

~ToR_No3 .

---+E- TOR_No 4

:~ToR_No5

-+-ToR_No6

-+-ToR_No 7

i--ToR_No8

1--ToR_No9

1-<>- TOR_No 10

Jul-98

.-------- -1

I

Oct-95 Mar-97

Test date

Jun-94

-----_.. _------------- ------------

0+---- -,.----'L-.----,-------,(>-------1

Jan-93

60

ft.

c 50

c

8 40

f

., 30 .

"0

20

i

:::I 10 +-----.....+tlF

b. Volumetric Moisture Content

Figure 11. Comparison between the seasonal variation offrost and moisture content,

section 274040.

44
Frost Penetration at Section 041024: Arizona

co

0>

t>

co

0>

::J

....,

co

0>

...

a.

«

co

0>

c:

ro....,

10

0>

t>

o

0.0 ,.------1--------.1------+-------1

• Freeze

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-

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- -1 . .c

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o No Freeze

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a. Frost Penetration

-+- TOR_N-;;1--1

---- TOR_No 2 I ~TOR_No3

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-+-TOR_No 7

-TOR_No 8

-TOR_No9

--¢-TOR_No 10

--------------------------------------1

I

!

O+--__.-------.----,----.---.------.----.-----J

Sep-95 Det-95 Dee-95 Feb-96 Mar-96 May-96 Jul-96 Aug-96 Det-96

Test date

b. Volumetric Moisture Content

60

Figure 12. Comparison between the seasonal variation offrost and moisture content,

section 041024.

45
n

R * = 1- ( II (1 - R) )

i=1

(6)

The procedure implemented in the determination ofthe soil freeze state uses a number of

measures to arrive at a decision. Each measure can be used independently to determine the

freeze state ofthe soil. For instance, soil temperature has long been used to determine freeze

state. Local inhabitants of an area can estimate the frost penetration, given the time ofthe year,

based on historical observations. Since each measure in the procedure (component ofthe

system) can independently produce the final output, then the system reliability is that of a

parallel system. Figure 13 is a schematic showing the system arrangement from the reliability

analysis point ofview.

Electrical Resistivity

Data Collection

Soil Temperature Freeze-State

Determination

Figure 13. System arrangement for reliability analysis.

It is assumed that there are two measures connected in parallel: the ER measurements

(including voltage, resistivity, and contact resistance) and soil temperature. Although the time of

the year (date) was also used in the decision process, it was only used when temperature data

were not available, amounting to only 7 percent ofthe observations.

Based on the diagnostic study ofER data presented in chapter 3, the reliability ofER

measures is roughly estimated at 60 percent. Soil temperature is very reliable in determining the

freeze state when the temperature is above freezing (unfrozen conditions). When the temperature

is at or below freezing, the reliability is reduced because offactors such as depressed freezing

temperature due to salinity, availability ofmoisture to freeze, and the isotherm conditions

discussed earlier. The reliability ofsoil temperature to determine the freeze state may be roughly

estimated as follows:

• Assumed reliability for determining the freeze state, when temperature is above

freezing, is 90 percent.

• Percentage ofrecords with temperature above freezing = 79 percent.

• Assumed reliability for determining the freeze state when temperature is at or

below freezing is 50 percent.

46
• Percentage ofrecords with temperature at or below freezing = 21 percent.

• Then the estimated average reliability is the weighted average ofthe two

conditions, where the weights are based on the number ofrecords. This amounts

to an average reliability of 82 percent.

Substituting in equation 6 an ER reliability of 0.6 and a temperature reliability of 0.82,

the overall reliability ofthe freeze-state determination is 93 percent. It should be noted that the

reliability analysis presented here is based on estimates of component reliability. The latter

encompasses factors such as measurement accuracy.

Interpretation Results

Two sets ofresults were obtained from program FROST. The first set ofresults

identifies the freeze-state at all electrical resistivity sensor locations. The second set ofresults

identifies the frost depth for each site per test visit to the site. These results are available in the

LTPP IMS as part ofthe SMP_FREEZE_STATE and SMP_FROST_PENETRATION tables.

The table SMP_FROST_PENETRATION is included as appendix C.

47

CHAPTER 6 - CONCLUSIONS AND RECOMMENDATIONS

This chapter summarizes the findings ofthis study and presents recommendations for

future ER and related data collection and analysis practices. Although the recommendations are

specific to the LTPP data collection and analysis practice, they should be of use to any user of

electrical resistivity data.

Conclusions

The diagnostic study ofsoil temperature data revealed the following:

• The general temperature trends were reasonable and as expected. Comparisons

between various temperature measures, taken at various times and locations,

showed that the majority oftemperature data appear reasonable and agree with

expected trends. Missing, erroneous, and suspicious data were identified so they

could be isolated from the database and their sources investigated.

• The use ofthe latent heat offusion phenomenon to identify the timing ofthe soil

phase change appears promising. The conditions used to identify a phase change

event are negative temperature (below O°C) and constant temperature over a

period of2 days. These conditions identified a phase change in 20 of 45 SMP

sections, with a plausible profile and reasonable timing for a phase change.

However, there is a limitation dictated by the arbitrary assumption that the period

oftime over which constant temperature would indicate a phase change is 2 days.

In actuality, this period is variable, depending on the rate oftemperature change

and the thermal properties ofthe soil. This limitation makes the latent heat of

fusion method incomplete for identifying the timing ofthe soil phase change.

Supporting measures from other sources (such as resistivity, resistance, and

voltage data) are needed to complement the latent heat offusion method.

The diagnostic study ofER data revealed the following:

• The three ER measurements (voltage, resistivity, and resistance) were consistent

with each other in about 60 percent ofthe cases (including partial and total

agreements). In the remaining 40 percent ofthe cases, it is conceivable that

different results can be obtained using different resistivity measures.

• Each resistivity measure was found to have some advantages over other measures.

Contact resistance seemed to be more in agreement with other resistivity measures

than were resistivity or voltage. Resistivity data had lower diurnal variability and

had more sections with larger winter to non-winter variability ratios than contact

resistance. Voltage data seemed to be more stable (low diurnal variation) and had

no zero value records.

Preceding Page Blank

49
• Since resistivity measures are not fully redundant and each has some advantage

over the others, more reliable results can be obtained by using all measures

simultaneously.

• Some or all resistivity measures were successful in identifying freezing conditions

in about 60 percent ofthe observations. In the remaining 40 percent ofthe

observations, none ofthe resistivity measures was successful in identifying

freezing conditions. The freezing-triggered ER peaks are masked by large nonwinter, seasonal, and diurnal variabilities. In addition, more noise may be

introduced by data collection problems. With the noise inherent in ER data, fully

automated interpretation procedures are not considered reliable at the present

time.

• The fact that very large resistivity values were observed in unfrozen conditions

suggests that temperature data must play an impOl:tant role in the interpretation of

ER data. Therefore, the analysis ofER should be based on resistivity, resistance,

voltage, temperature, and other supporting data, such as time ofthe year, location

ofmeasurement, and knowledge of established freezing patterns in any given

location.

The quest for an optimum and practical method ofER data analysis yielded the

following:

• An interactive computer program has been developed to process ER data to

determine the frost penetration in SMP sections. The program, called FROST,

determines the freeze state of a soil based on the values ofthree ER measures

(voltage, contact resistance, resistivity) and soil temperature. Based on timeseries plots of all ER variables at each depth and temperature data, the user

determines the threshold ER value above which the soil is potentially frozen. If

the soil temperature is negative (below O°C) and the ER value is larger than the

established threshold, the soil is considered frozen.

• The procedure implemented in FROST has the following advantages.

Uses five variables simultaneously (three ER measures, soil temperature,

and date) to improve reliability.

Uses normalized ER parameters based on relative values at each electrode,

thereby reducing the noise due to material variability.

Uses average ER values for a given date to stabilize diurnal variability.

Software assists the user with warning messages and a variety of graphical

reports.

• FROST has been used to process ER data and produce the tables of computed

parameters (freeze state and frost penetration) for inclusion in the LTPP IMS

database. The frost penetration profiles were compared with historic values and

50
were found to be in reasonable agreement with expectations. Furthermore, an

excellent correlation between freezing events, as determined by FROST, and a

sharp decrease in the unfrozen moisture content (as measured by TDR) was found

for most sites where such data were available.

Recommendations

Based on the findings ofthis research effort, the following are recommended to improve

future collection and analysis ofER data:

• Although the theory behind the ER technique is sound, the noise inherent in ER

data made the task of data analysis laborious. It appears that the ER probes are

sensitive to exogenous variables, some of which are not easily controlled. One

example is the quality of contact between the electrodes and the soil, which may

change with time. Other factors that contribute to the noise in the data include

durability ofthe ER probes and data collection errors. It appears that the quality

ofER data can be improved by the following means:

Development ofmore durable and robust hardware to avoid current

problems.

Use of an automated data collection system to avoid human errors.

Adoption of a more rigorous instrumentation and data collection protocol.

Conduct oflaboratory testing to further understand the characteristics of

ERdata.

• Other immediate steps can also be taken to improve the ER data resolution. These

include:

More frequent ER data collection to help screen out outliers. Currently, in

SMP sections, ER data are collected approximately once a month (with

multiple readings on the testing day). The long time lapse between the

readings makes it difficult to conclude whether the large change in

resistivity is caused by equipment malfunction or by a change in the soil

state.

Inspecting ER data as they are collected and comparing them to previous

readings to identify erroneous data. Atkins [19] recommends the

following:

After the readings have been recorded, they should be graphed or

otherwise analyzed to ensure that the measurements are reasonable and

that they agree with any other available data (such as temperature probes

for example). This data reduction process should not be leftfor too long a

period oftime after the data has been taken. For example, it is not a good

idea to process and review the data just once a month since after a thirty

day period it will be too late to discover the cause ofany extraneous

51
readings!! The best approach is to graph or otherwise analyze the data

the same day it is recorded Ifit is not possible to process the data the

same day that it is taken, then at the very least, a weekly analysis ofthe

data is highly recommended!!

• It is recommended that other techniques be explored to supplement, complement,

or be substituted for the ER technique. For instance, the Mn/Road program relies

on WatermarkTM plugs for frost penetration determination. The WatermarkTM

plugs, which are based on measurement ofthe soil suction, were reported to have

a reliable, identifiable, sharp increase in suction when the soil freezes. Once the

frost penetration is determined in the locations ofthe WatermarkTM plugs, ER data

are then used cautiously to fill in the gaps in two-dimensional graphs.

• For the short- and mid-term analysis ofER data, the interactive procedure

implemented in FROST (described in chapter 4) is recommended. In the long

term, enhancements to the procedure may evolve to a fully automated method

when better understanding and control ofER data are achieved.

52
REFERENCES

1. R.L. Lytton et aI., An Integrated Model ofthe Climatic Effects on Pavements, Report No.

FHWA-RD-90-033, FHWA, November 1993.

2. FHWA, LTPP Seasonal Monitoring Program: Instrumentation Installation and Data

Collection Guidelines, Report No. FHWA-RD-94-11 0, April1994.

3. L. Bendel, Ingenieurgeologie, Vol. 2, Springer Verlag, Wien, 1948, p. 656.

4. C.A. Hogento1ger, Engineering Properties ofSoil, McGraw-Hill Book Company, Inc.,

NY, 1937, p. 283.

5. A.R. Jumikis, The Frost Penetration Problem in Highway Engineering, Rutgers

University Press, New Brunswick, NJ, 1955, pp. 91-92.

6. J. Stefan, "Prob1eme der Theorie der Warmeleitung," Sitzungsberichte der MathematischNaturwissenschaftlichen Classe der Kaiserlichen Akademie der Wissenschaften (Vienna),

XCVIII,2a, 1890, p. 474.

7. A.R Jumikis, "Vapor Diffusion in Freezing Soil Systems of Very Large Porosities,"

Highway Research Board Bulletin, No. 331, on Soil Behavior Associated with Freezing,

National Academy of Sciences/National Research Council Publication 1013,

Washington, DC, 1962, pp. 28-45.

8. C.C. Ryerson, "A Moisture-Based Model for Calculating Daily Changes in Seasonal Soil

Frost Depth," Climatology, Vol. XXXII, No.2, University ofDelaware, 1979.

9. G.L. Guymon, RL. Berg, and T.e. Johnston, Mathematical Model ofFrost Heave and

Thaw Settlement in Pavements, U.S. Army Cold Regions Research and Engineering

Laboratory, NH, 1986.

10. J.G. Mason, e.W. Ollayos, G.L. Guymon, and RL. Berg, User's Guidefor the

Mathematical Model ofFrost Heave and Thaw Settlement in Pavement, U.S. Army Cold

Regions Research and Engineering Laboratory, NH, 1986.

11. G.L. Guymon, T.V. Hromadka II, and RL. Berg, "A One-Dimensional Frost Heave

Model Based Upon Simulation of Simultaneous Heat and Water Flux," Cold Region

Science and Technology, Vol. 3, pp. 253-262, Elsevier Amsterdam, 1980.

12. G.L. Guymon, T.V. Hromadka II, T.C. Johnston, and RL. Berg, "Results of

Mathematical Model ofFrost Heave," Transportation Research Record 809,

Transportation Research Board, Washington, DC, 1981, pp. 1-6.

53
13. G.L. Guymon, M.E. Harr, T.V. Hromadka II, and R.L. Berg, "A Probabilistic

Deterministic Analysis ofOne-Dimensional Ice Segregation in a Freezing Soil Column,"

Cold Region Science and Technology, Vol. 5, pp. 127-140, Elsevier Amsterdam, 1981.

14. S.S. Visher, "Climatic Maps ofGeologic Interest," Bulletin ofthe Geological Society of

America, Vol. 56, 1945, pp. 713-736.

15. Y.J. Jiang, and S.D. Tayabji, Analysis ofTime Domain Rejlectometry Measurements,

Report No. FHWA-RD-99-115, FHWA, August 1998.

16. M. Lindeburg, Civil Engineering Reference Manual, Professional Publications, 1994.

17. R.T. Atkins, Resistivity Probes Installation and Readout Techniques, unpublished report

prepared for the MnRoad Research Project, Minnesota Department ofTransportation,

St. Paul, MN, 1998.

54
APPENDIX A - FROST PENETRATION PLOTS

Frost penetration plots are provided for the following sections:

Number Section ID State Name

1 041024 Arizona

2 081053 Colorado

3 091803 Connecticut

4 161010 Idaho

5 183002 Indiana

6 204054 Kansas

7 231026 Maine

8 241634 Maryland

9 251002 Massachusetts

10 271018 Minnesota

11 271028 Minnesota

12 276251 Minnesota

13 274040 Minnesota

14 308129 Montana

15 313018 Nebraska

16 331001 New Hampshire

17 364018 New York

18 421606 Pennsylvania

19 460804 South Dakota

20 493011 Utah

21 501002 Vermont

22 561007 Wyoming

23 831801 Manitoba

24 833802 Manitoba

25 871622 Ontario

26 893015 Quebec

27 906405 Saskatchewan

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APPENDIX B - SAMPLE TIME-SERIES ER PLOTS USED IN PROGRAM FROST

Time-series plots are provided for section 231026 in Maine for the following depths:

Number Depth, m

1 0.27

2 0.32

3 0.37

4 0.42

5 0.47

6 0.52

7 0.58

8 0.63

9 0.68

10 0.73

11 0.78

12 0.83

13 0.88

14 0.93

15 0.98

16 1.03

17 1.08

18 1.13

19 1.18

20 1.24

21 1.29

22 1.34

23 1.39

24 1.44

25 1.49

26 1.54

27 1.59

Number Depth, m

28 1.64

29 1.69

30 1.74

31 1.79

32 1.85

33 1.90

34 1.95

35 2.00

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APPENDIX C - TABLE SMP_FROST_PENETRATION

State Code SHRP ID SMP Date Frozen Layer No Construction No Record_Status Freeze From Freeze To

4 1024 10/12/1995 0 1 A

4 1024 11/9/1995 0 1 A

4 1024 12/7/1995 0 1 A

4 1024 1/11/1996 0 1 A

4 1024 2/8/1996 0 1 A

4 1024 6/13/1996 0 1 A

4 1024 7/11/1996 0 1 A

4 1024 8/15/1996 0 1 A

8 1053 11/9/1993 0 1 A

8 1053 12/6/1993 0 1 A

8 1053 1/18/1994 0 1 A

8 1053 2/14/1994 0 1 A

8 1053 3/14/1994 0 1 A

8 1053 3/28/1994 0 1 A

8 1053 4/11/1994 0 1 A

8 1053 5/2/1994 0 1 A

8 1053 6/16/1994 0 1 A

8 1053 7/13/1994 0 1 A

8 1053 8/7/1994 0 1 A

8 1053 8/8/1994 0 1 A

8 1053 9/12/1994 0 1 A

8 1053 10/21/1994 0 1 A

8 1053 11/10/1994 0 1 A

8 1053 1/17/1995 0 1 A

8 1053 2/13/1995 0 1 A

8 1053 11/14/1996 0 1 A

8 1053 12/11/1996 0 1 A

8 1053 1/16/1997 0 1 A

8 1053 2/13/1997 0 1 A

8 1053 3/7/1997 0 1 A

8 1053 3/20/1997 0 1 A

8 1053 4/8/1997 0 1 A

8 1053 4/23/1997 0 1 A

8 1053 6/13/1997 0 1 A

9 1803 8/19/1993 0 1 A

9 1803 9/2/1993 0 1 A

9 1803 11/15/1993 0 1 A

9 1803 2/17/1994 0 1 A

9 1803 3/10/1994 0 1 A

9 1803 3/30/1994 0 1 A

9 1803 4/21/1994 0 1 A

9 1803 6/9/1994 0 1 A

9 1803 6/30/1994 0 1 A

9 1803 7/28/1994 0 1 A

9 1803 8/25/1994 0 1 A

9 1803 9/29/1994 0 1 A

9 1803 10/27/1994 0 1 A

9 1803 11/30/1994 0 1 A

9 1803 1/5/1995 0 1 A

9 1803 1/26/1995 0 1 A

9 1803 3/2/1995 0 1 A

9 1803 3/15/1995 0 1 A

9 1803 3/29/1995 0 1 A

9 1803 4/12/1995 0 1 A

9 1803 5/25/1995 0 1 A

9 1803 6/22/1995 0 1 A

9 1803 10/8/1996 0 1 A

9 1803 11/7/1996 0 1 A

9 1803 12/5/1996 0 1 A

9 1803 1/9/1997 0 1 A

119
State Code SHRP 10 SMP Date Frozen Layer No Construction No Record Status Freeze From Freeze To

9 1803 2/6/1997 0 1 A

9 1803 3/6/1997 0 1 A

9 1803 4/3/1997 0 1 A

9 1803 4/17/1997 0 1 A

9 1803 5/8/1997 0 1 A

9 1803 6/5/1997 0 1 A

9 1803 7/10/1997 0 1 A

9 1803 8/7/1997 0 1 A

9 1803 9/11/1997 0 1 A

9 1803 10/16/1997 0 1 A

16 1010 10/1/1993 0 1 A

16 1010 11/18/1993 0 1 A

16 1010 12/16/1993 1 1 A 0.359 0.410

16 1010 1/27/1994 1 1 A 0.359 0.459

16 1010 2/25/1994 1 1 A 0.561 0.763

16 1010 3/21/1994 0 1 A

16 1010 4/4/1994 0 1 A

16 1010 4/22/1994 0 1 A

16 1010 5/9/1994 0 1 A

16 1010 6/1/1994 0 1 A

16 1010 7/27/1994 0 1 A

16 1010 8/25/1994 0 1 A

16 1010 9/27/1994 0 1 A

16 1010 11/2/1994 0 1 A

16 1010 11/17/1994 0 1 A

16 1010 12/13/1994 1 1 A 0.359 0.662

16 1010 1/25/1995 1 1 A 0.359 0.763

16 1010 2/21/1995 0 1 A

16 1010 11/25/1996 0 1 A

16 1010 12/19/1996 1 1 A 0.359 0.612

16 1010 1/27/1997 1 1 A 0.359 0.509

16 1010 2/25/1997 0 1 A

16 1010 3/14/1997 0 1 A

16 1010 3/28/1997 0 1 A

16 1010 4/16/1997 0 1 A

16 1010 4/30/1997 0 1 A

16 1010 6/2/1997 0 1 A

16 1010 6/26/1997 0 1 A

18 3002 9/8/1995 0 1 A

18 3002 10/13/1995 0 1 A

18 3002 11/20/1995 0 1 A

18 3002 12/8/1995 0 1 A

18 3002 1/26/1996 0 1 A

18 3002 3/11/1996 0 1 A

18 3002 4/30/1996 0 1 A

18 3002 8/2/1996 0 1 A

20 4054 8/25/1995 0 1 A

20 4054 9/21/1995 0 1 A

20 4054 10/18/1995 0 1 A

20 4054 11/16/1995 0 1 A

20 4054 12/11/1995 0 1 A

20 4054 1/24/1996 0 1 A

20 4054 3/8/1996 0 1 A

20 4054 4/25/1996 0 1 A

23 1026 9/16/1993 0 1 A

23 1026 11/10/1993 0 1 A

23 1026 1/10/1994 1 1 A 0.245 0.600

23 1026 2/7/1994 1 1 A 0.245 1.515

23 1026 2/28/1994 1 1 A 0.245 1.770

23 1026 3/24/1994 1 1 A 1.312 1.618

120
State Code SHRP 10 SMP Date Frozen Layer No Construction No Record Status Freeze From Freeze To

23 1026 4/11/1994 0 1 A

23 1026 5/23/1994 0 1 A

23 1026 6/20/1994 0 1 A

23 1026 7/18/1994 0 1 A

23 1026 8/15/1994 0 1 A

23 1026 9/19/1994 0 1 A

23 1026 10/17/1994 0 1 A

23 1026 11/14/1994 0 1 A

23 1026 12/12/1994 0 1 A

23 1026 1/17/1995 1 1 A 0.499 0.907

23 1026 2/14/1995 1 1 A 0.245 1.008

23 1026 3/6/1995 0 1 A

23 1026 3/20/1995 0 1 A

23 1026 4/3/1995 0 1 A

23 1026 5/1/1995 0 1 A

23 1026 6/26/1995 0 1 A

24 1634 5/13/1995 0 1 A

24 1634 7/6/1995 0 1 A

24 1634 9/19/1995 0 1 A

24 1634 10/19/1995 0 1 A

24 1634 11/15/1995 0 1 A

24 1634 12/13/1995 0 1 A

24 1634 1/17/1996 0 1 A

24 1634 2/14/1996 0 1 A

24 1634 2/28/1996 0 1 A

24 1634 3/13/1996 0 1 A

24 1634 4/17/1996 0 1 A

24 1634 5/8/1996 0 1 A

24 1634 7/17/1996 0 1 A

24 1634 8/14/1996 0 1 A

24 1634 9/25/1996 0 1 A

24 1634 10/30/1996 0 1 A

24 1634 9/4/1997 0 1 A

24 1634 10/1/1997 0 1 A

24 1634 11/12/1997 0 1 A

25 1002 9/1/1993 0 1 A

25 1002 11/16/1993 0 1 A

25 1002 2/16/1994 1 1 A 0.252 0.762

25 1002 3/9/1994 0 1 A

25 1002 3/29/1994 0 1 A

25 1002 4/20/1994 0 1 A

25 1002 6/8/1994 0 1 A

25 1002 6/29/1994 0 1 A

25 1002 7/27/1994 0 1 A

25 1002 8/24/1994 0 1 A

25 1002 9/28/1994 0 1 A

25 1002 10/26/1994 0 1 A

25 1002 11/29/1994 0 1 A

25 1002 1/4/1995 0 1 A

25 1002 2/1/1995 0 1 A

25 1002 3/1/1995 0 1 A

25 1002 3/14/1995 0 1 A

25 1002 3/28/1995 0 1 A

25 1002 4/11/1995 0 1 A

25 1002 5/24/1995 0 1 A

25 1002 6/21/1995 0 1 A

25 1002 10/9/1996 0 1 A

25 1002 11/6/1996 0 1 A

25 1002 12/4/1996 0 1 A

25 1002 1/8/1997 0 1 A

121
State Code SHRP ID SMP Date Frozen Layer No Construction No Record Status Freeze From Freeze To

25 1002 2/5/1997 0 1 A

25 1002 3/5/1997 0 1 A

25 1002 4/2/1997 0 1 A

25 1002 4/16/1997 0 1 A

25 1002 5/7/1997 0 1 A

25 1002 6/4/1997 0 1 A

25 1002 7/9/1997 0 1 A

25 1002 8/6/1997 0 1 A

25 1002 9/10/1997 0 1 A

25 1002 10/15/1997 0 1 A

27 1018 8/24/1993 0 1 A

27 1018 9/23/1993 0 1 A

27 1018 10/20/1993 0 1 A

27 1018 11/19/1993 0 1 A

27 1018 12/7/1993 1 1 A 0.404 0.506

27 1018 1/11/1994 1 1 A 0.404 1.422

27 1018 2/8/1994 1 1 A 0.404 2.030

27 1018 3/8/1994 1 1 A 0.456 2.182

27 1018 3/22/1994 1 1 A 1.321 2.131

27 1018 4/4/1994 1 1 A 1.574 1.828

27 1018 4/25/1994 1 1 A 1.625 1.777

27 1018 5/9/1994 0 1 A

27 1018 6/13/1994 0 1 A

27 1018 7/11/1994 0 1 A

27 1018 8/8/1994 0 1 A

27 1028 9/9/1993 0 1 A

27 1028 10/20/1993 0 1 A

27 1028 11/18/1993 0 1 A

27 1028 12/8/1993 1 1 A 0.610 0.968

27 1028 1/12/1994 1 1 A 0.610 2.084

27 1028 3/9/1994 1 1 A 0.610 2.387

27 1028 3/23/1994 1 1 A 1.173 2.387

27 1028 4/5/1994 1 1 A 1.983 2.286

27 1028 4/26/1994 0 1 A

27 1028 5/10/1994 0 1 A

27 1028 6/14/1994 0 1 A

27 1028 7/12/1994 0 1 A

27 1028 8/9/1994 0 1 A -----..

27 1028 9/19/1994 0 1 A

27 1028 10/11/1994 0 1 A

27 1028 11/8/1994 0 1 A

27 1028 12/6/1994 1 1 A 0.610 0.865

27 1028 1/10/1995 1 1 A 0.610 1.782

27 1028 2/7/1995 1 1 A 0.610 2.084

27 1028 3/8/1995 1 1 A 0.610 2.387

27 1028 3/21/1995 1 1 A 1.070 2.387

27 1028 5/9/1995 0 1 A

27 1028 5/15/1995 0 1 A

27 1028 6/14/1995 0 1 A

27 1028 10/8/1996 0 1 A

27 1028 11/5/1996 0 1 A

27 1028 12/5/1996 1 1 A 0.660 1.478

27 1028 4/10/1997 1 1 A 2.084 2.387

27 1028 4/24/1997 0 1 A

27 1028 5/8/1997 0 1 A

27 1028 5/30/1997 0 1 A

27 1028 6/12/1997 0 1 A

27 1028 8/14/1997 0 1 A

27 1028 9/10/1997 0 1 A

27 4040 9/22/1993 0 1 A

122
State Code SHRP 10 SMP Date Frozen Layer No Construction No Record Status Freeze From Freeze To

27 4040 10/18/1993 0 1 A

27 4040 11/16/1993 0 1 A

27 4040 12/16/1993 1 1 A 0.639 1.047

27 4040 1/19/1994 1 1 A 0.538 1.708

27 4040 2/17/1994 1 1 A 0.588 1.758

27 4040 3/17/1994 1 1 A 0.845 2.012

27 4040 4/14/1994 0 1 A

27 4040 5/4/1994 0 1 A

27 4040 5/18/1994 0 1 A

27 4040 6/23/1994 0 1 A

27 4040 7/28/1994 0 1 A

27 4040 8/25/1994 0 1 A

27 4040 9/30/1994 0 1 A

27 4040 10/13/1994 0 1 A

27 4040 11/10/1994 0 1 A

27 4040 12/8/1994 1 1 A 0.588 0.793

27 4040 1/12/1995 1 1 A 0.538 1.402

27 4040 2/9/1995 1 1 A 0.538 1.708

27 4040 3/31/1995 1 1 A 0.997 1.961

27 4040 5/11/1995 0 1 A

27 4040 6/16/1995 0 1 A

27 4040 10/9/1996 0 1 A

27 4040 2/5/1997 1 1 A 0.538 1.961

27 4040 3/17/1997 1 1 A 1.251 2.217

27 4040 3/18/1997 1 1 A 0.639 1.911

27 4040 4/8/1997 1 1 A 1.098 1.708

27 4040 4/22/1997 1 1 A 1.251 2.064

27 4040 5/6/1997 1 1 A 1.758 2.012

27 4040 5/28/1997 0 1 A

27 4040 6/10/1997 0 1 A

27 4040 7/11/1997 0 1 A

27 4040 8/13/1997 0 1 A

27 4040 9/9/1997 0 1 A

27 6251 9/15/1993 0 1 A

27 6251 10/19/1993 0 1 A

27 6251 11/17/1993 0 1 A

27 6251 12/15/1993 0 1 A

27 6251 1/19/1994 1 1 A 0.479 2.107

27 6251 2/16/1994 1 1 A 0.580 2.259

27 6251 3/16/1994 1 1 A 1.191 2.208

27 6251 3/30/1994 0 1 A

27 6251 4/13/1994 0 1 A

27 6251 5/3/1994 0 1 A

27 6251 5/17/1994 0 1 A

27 6251 6/22/1994 0 1 A

27 6251 7/27/1994 0 1 A

27 6251 8/24/1994 0 1 A

27 6251 9/29/1994 0 1 A

27 6251 10/12/1994 0 1 A

27 6251 11/9/1994 0 1 A

27 6251 12/7/1994 1 1 A 0.479 0.834

27 6251 2/8/1995 1 1 A 0.479 2.157

27 6251 3/16/1995 1 1 A 1.141 2.259

27 6251 3/30/1995 1 1 A 1.548 2.208

27 6251 4/27/1995 0 1 A

27 6251 5/10/1995 0 1 A

27 6251 6/15/1995 0 1 A

27 6251 10/10/1996 0 1 A

27 6251 11/8/1996 0 1 A

27 6251 12/4/1996 1 1 A 0.479 1.446

123
State Code SHRP ID SMP Date Frozen Layer No Construction No Record Status Freeze From Freeze To

27 6251 2/6/1997 1 1 A 0.479 2.259

27 6251 3/19/1997 1 1 A 0.529 0.937

27 6251 3/19/1997 2 1 A 1.039 2.259

27 6251 4/9/1997 1 1 A 1.191 1.446

27 6251 4/23/1997 0 1 A

27 6251 5/7/1997 0 1 A

27 6251 5/29/1997 0 1 A

27 6251 6/11/1997 0 1 A

27 6251 7/10/1997 0 1 A

27 6251 8/14/1997 0 1 A

27 6251 9/10/1997 0 1 A

30 8129 11/15/1993 0 1 A

30 8129 12/14/1993 0 1 A

30 8129 1/25/1994 1 1 A 0.320 0.624

30 8129 2/22/1994 1 1 A 0.523 0.929

30 8129 3/17/1994 0 1 A

30 8129 3/31/1994 0 1 A

30 8129 4/20/1994 0 1 A

30 8129 5/5/1994 0 1 A

30 8129 6/6/1994 0 1 A

30 8129 7/22/1994 0 1 A

30 8129 8/22/1994 0 1 A

30 8129 9/23/1994 0 1 A

30 8129 10/31/1994 0 1 A

30 8129 11/15/1994 0 1 A

30 8129 12/9/1994 1 1 A 0.165 0.470

30 8129 1/23/1995 1 1 A 0.165 0.829

30 8129 2/17/1995 1 1 A 0.165 0.573

30 8129 11/21/1996 1 1 A 0.165 1.032

30 8129 12/17/1996 1 1 A 0.165 1.032

30 8129 1/23/1997 1 1 A 0.320 0.470

30 8129 3/12/1997 0 1 A

30 8129 3/25/1997 0 1 A

30 8129 4/11/1997 0 1 A

30 8129 4/28/1997 0 1 A

30 8129 5/28/1997 0 1 A

30 8129 6/18/1997 0 1 A

30 8129 8/11/1997 0 1 A

30 8129 10/1/1997 0 1 A

31 3018 8/11/1995 0 1 A

31 3018 9/19/1995 0 1 A

31 3018 10/16/1995 0 1 A

31 3018 11/14/1995 0 1 A

31 3018 12/13/1995 0 1 A

31 3018 1/22/1996 1 1 A 1.019 1.222

31 3018 3/6/1996 0 1 A

31 3018 4/23/1996 0 1 A

33 1001 10/14/1993 0 1 A

33 1001 3/21/1994 0 1 A

33 1001 4/14/1994 0 1 A

33 1001 5/26/1994 0 1 A

33 1001 6/23/1994 0 1 A

33 1001 7/21/1994 0 1 A

33 1001 8/16/1994 0 1 A

33 1001 9/22/1994 0 1 A

33 1001 10/20/1994 0 1 A

33 1001 11/17/1994 0 1 A

33 1001 12/15/1994 0 1 A

33 1001 1/24/1995 0 1 A

33 1001 2/21/1995 0 1 A

124
State Code SHRP ID SMP Date Frozen Layer No Construction No Record Status Freeze From Freeze To

33 1001 3/16/1995 0 1 A

33 1001 3/30/1995 0 1 A

33 1001 4/27/1995 0 1 A

33 1001 6/1/1995 0 1 A

33 1001 6/29/1995 0 1 A

33 1001 10/10/1996 0 1 A

33 1001 11/13/1996 0 1 A

33 1001 12/11/1996 0 1 A

33 1001 1/22/1997 0 1 A

33 1001 2/12/1997 0 1 A

33 1001 3/26/1997 0 1 A

33 1001 4/9/1997 0 1 A

33 1001 4/23/1997 0 1 A

33 1001 5/14/1997 0 1 A

33 1001 6/11/1997 0 1 A

33 1001 7/16/1997 0 1 A

33 1001 8/13/1997 0 1 A

33 1001 9/17/1997 0 1 A

33 1001 10/22/1997 0 1 A

36 4018 10/28/1993 0 1 A

36 4018 2/15/1994 1 1 A 0.334 1.951

36 4018 3/8/1994 1 1 A 0.787 2.052

36 4018 3/28/1994 1 1 A 1.192 1.293

36 4018 4/19/1994 0 1 A

36 4018 6/7/1994 0 1 A

36 4018 6/28/1994 0 1 A

36 4018 7/29/1994 0 1 A

36 4018 8/23/1994 0 1 A

36 4018 9/27/1994 0 1 A

36 4018 10/25/1994 0 1 A

36 4018 11/22/1994 0 1 A

36 4018 12/20/1994 0 1 A

36 4018 1/31/1995 0 1 A

36 4018 2/28/1995 0 1 A

36 4018 3/13/1995 0 1 A

36 4018 3/27/1995 0 1 A

36 4018 4/10/1995 0 1 A

36 4018 4/24/1995 0 1 A

36 4018 5/23/1995 0 1 A

36 4018 6/20/1995 0 1 A

36 4018 9/19/1996 0 1 A

36 4018 12/3/1996 0 1 A

36 4018 1/7/1997 0 1 A

36 4018 2/4/1997 1 1 A 0.484 1.040

36 4018 3/4/1997 0 1 A

36 4018 4/15/1997 0 1 A

36 4018 5/6/1997 0 1 A

36 4018 6/3/1997 0 1 A

36 4018 7/8/1997 0 1 A

36 4018 8/5/1997 0 1 A

36 4018 9/9/1997 0 1 A

36 4018 10/14/1997 0 1 A

42 1606 8/10/1995 0 1 A

42 1606 9/28/1995 0 1 A

42 1606 10/12/1995 0 1 A

42 1606 11/9/1995 0 1 A

42 1606 12/7/1995 0 1 A

42 1606 1/15/1996 0 1 A

42 1606 3/7/1996 0 1 A

42 1606 4/11/1996 0 1 A

125
State Code SHRP ID SMP Date Frozen Layer No Construction No Record Status Freeze From Freeze To

42 1606 5/2/1996 0 1 A

42 1606 6/6/1996 0 1 A

42 1606 7/11/1996 0 1 A

42 1606 8/8/1996 0 1 A

46 804 7/15/1994 0 1 A

46 804 8/12/1994 0 1 A

46 804 9/27/1994 0 1 A

46 804 10/25/1994 0 1 A

46 804 11/22/1994 0 1 A

46 804 12/20/1994 1 1 A 0.526 1.135

46 804 3/23/1995 0 1 A

46 804 5/23/1995 0 1 A

46 804 6/27/1995 0 1 A

46 804 10/24/1996 0 1 A

46 804 2/18/1997 1 1 A 0.526 0.982

46 804 4/15/1997 0 1 A

46 804 4/29/1997 0 1 A

46 804 5/15/1997 0 1 A

46 804 6/5/1997 0 1 A

46 804 6/24/1997 0 1 A

46 804 7/22/1997 0 1 A

46 804 8/24/1997 0 1 A

46 804 9/22/1997 0 1 A

49 1001 11/4/1993 0 1 A

49 1001 12/2/1993 0 1 A

49 1001 1/14/1994 0 1 A

49 1001 2/11/1994 0 1 A

49 1001 3/11/1994 0 1 A

49 1001 3/25/1994 0 1 A

49 1001 4/8/1994 0 1 A

49 1001 4/28/1994 0 1 A

49 1001 6/17/1994 0 1 A

49 1001 7/15/1994 0 1 A

49 1001 8/5/1994 0 1 A

49 1001 9/9/1994 0 1 A

49 1001 10/20/1994 0 1 A

49 1001 11/9/1994 0 1 A

49 1001 12/1/1994 0 1 A

49 1001 1/13/1995 0 1 A

49 1001 11/12/1996 0 1 A

49 1001 12/9/1996 0 1 A

49 1001 2/11/1997 0 1 A

49 1001 3/6/1997 0 1 A

49 1001 4/7/1997 0 1 A

49 1001 4/22/1997 0 1 A

49 1001 5/20/1997 0 1 A

49 1001 6/12/1997 0 1 A

49 1001 8/4/1997 0 1 A

49 1001 9/24/1997 0 1 A

49 3011 11/2/1993 0 1 A

49 3011 11/30/1993 0 1 A

49 3011 1/12/1994 0 1 A

49 3011 1/13/1994 0 1 A

49 3011 2/9/1994 0 1 A

49 3011 3/9/1994 0 1 A

49 3011 3/23/1994 0 1 A

49 3011 4/26/1994 0 1 A

49 3011 6/20/1994 0 1 A

49 3011 7/11/1994 0 1 A

49 3011 8/2/1994 0 1 A

126
State Code SHRP 10 SMP Date Frozen Layer No Construction No Record Status Freeze From Freeze To

49 3011 9/7/1994 0 1 A

49 3011 10/18/1994 0 1 A

49 3011 11/4/1994 0 1 A

49 3011 11/29/1994 0 1 A

49 3011 1/12/1995 0 1 A

49 3011 2/10/1995 0 1 A

49 3011 11/8/1996 0 1 A

49 3011 12/5/1996 0 1 A

49 3011 1/10/1997 0 1 A

49 3011 2/7/1997 0 1 A

49 3011 3/5/1997 0 1 A

49 3011 3/17/1997 0 1 A

49 3011 4/3/1997 0 1 A

49 3011 4/21/1997 0 1 A

49 3011 5/16/1997 0 1 A

49 3011 6/10/1997 0 1 A

49 3011 7/31/1997 0 1 A

49 3011 9/22/1997 0 1 A

50 1002 10/7/1993 0 1 A

50 1002 11/8/1993 0 1 A

50 1002 1/12/1994 1 1 A 0.284 0.638

50 1002 3/2/1994 1 1 A 0.284 0.385

50 1002 3/22/1994 0 1 A

50 1002 4/13/1994 0 1 A

50 1002 5/25/1994 0 1 A

50 1002 6/22/1994 0 1 A

50 1002 7/20/1994 0 1 A

50 1002 8/17/1994 0 1 A

50 1002 9/21/1994 0 1 A

50 1002 10/19/1994 0 1 A

50 1002 11/16/1994 0 1 A

50 1002 12/14/1994 0 1 A

50 1002 1/19/1995 0 1 A

50 1002 2/15/1995 1 1 A 0.284 0.791

50 1002 3/17/1995 0 1 A

50 1002 3/31/1995 0 1 A

50 1002 4/13/1995 0 1 A

50 1002 4/28/1995 0 1 A

50 1002 5/31/1995 0 1 A

50 1002 6/28/1995 0 1 A

50 1002 10/17/1996 0 1 A

50 1002 11/14/1996 0 1 A

50 1002 12/12/1996 0 1 A

50 1002 1/23/1997 0 1 A

50 1002 2/13/1997 0 1 A

50 1002 3/27/1997 0 1 A

50 1002 4/10/1997 0 1 A

50 1002 4/24/1997 0 1 A

50 1002 5/15/1997 0 1 A

50 1002 6/12/1997 0 1 A

50 1002 7/17/1997 0 1 A

50 1002 8/14/1997 0 1 A

50 1002 9/16/1997 0 1 A

50 1002 10/23/1997 0 1 A

56 1007 8/7/1997 0 1 A

56 1007 9/30/1997 0 1 A

83 1801 10/13/1993 0 1 A

83 1801 11/12/1993 0 1 A

83 1801 12/13/1993 0 1 A

83 1801 1/17/1994 1 1 A 0.359 1.168

127
State Code SHRP ID SMP Date Frozen Layer No Construction No Record Status Freeze From Freeze To

83 1801 2/14/1994 1 1 A 0.408 1.117

83 1801 3/14/1994 0 1 A

83 1801 3/28/1994 0 1 A

83 1801 4/11/1994 0 1 A

83 1801 4/29/1994 0 1 A

83 1801 5/13/1994 0 1 A

83 1801 6/17/1994 0 1 A

83 1801 7/25/1994 0 1 A

83 1801 8/18/1994 0 1 A

83 1801 9/21/1994 0 1 A

83 1801 10/19/1994 0 1 A

83 1801 11/16/1994 0 1 A

83 1801 1/25/1995 1 1 A 0.359 0.864

83 1801 2/15/1995 1 1 A 0.359 1.472

83 1801 3/14/1995 1 1 A 0.660 0.915

83 1801 4/25/1995 0 1 A

83 1801 5/17/1995 0 1 A

83 1801 6/21/1995 0 1 A

83 1801 10/16/1996 0 1 A

83 1801 12/11/1996 1 1 A 0.359 1.066

83 1801 12/11/1996 2 1 A 1.370 1.472

83 1801 2/1111997 1 1 A 0.359 1.421

83 1801 3/25/1997 1 1 A 0.813 1.117

83 1801 3/25/1997 2 1 A 1.218 1.371

83 1801 4/17/1997 1 1 A 1.016 1.371

83 1801 5/1/1997 0 1 A

83 1801 6/2/1997 0 1 A

83 1801 6/18/1997 0 1 A

83 1801 7/16/1997 0 1 A

83 1801 8/19/1997 0 1 A

83 1801 9/16/1997 0 1 A

83 3802 10/15/1993 0 1 A

83 3802 11/12/1993 0 1 A

83 3802 11/15/1993 0 1 A

83 3802 12/14/1993 1 1 A 0.700 1.460

83 3802 1/18/1994 1 1 A 0.647 1.510

83 3802 2/15/1994 1 1 A 0.647 1.510

83 3802 3/15/1994 1 1 A 0.801 1.510

83 3802 3/29/1994 1 1 A 0.902 1.460

83 3802 4/12/1994 1 1 A 1.002 1.104

83 3802 5/2/1994 0 1 A

83 3802 5/16/1994 0 1 A

83 3802 6/20/1994 0 1 A

83 3802 7/26/1994 0 1 A

83 3802 8/22/1994 0 1 A

83 3802 9/20/1994 0 1 A

83 3802 10/18/1994 0 1 A

83 3802 11/15/1994 0 1 A

83 3802 1/26/1995 1 1 A 0.647 1.411

83 3802 3/15/1995 1 1 A 0.647 1.510

83 3802 3/29/1995 1 1 A 0.902 1.510

83 3802 4/26/1995 0 1 A

83 3802 5/16/1995 0 1 A

83 3802 6/20/1995 0 1 A

83 3802 10/15/1996 0 1 A

83 3802 12/10/1996 1 1 A 0.647 1.207

83 3802 4/16/1997 1 1 A 0.751 1.510

83 3802 5/31/1997 0 1 A

83 3802 6/20/1997 0 1 A

83 3802 7/17/1997 0 1 A

128
State Code SHRP ID SMP Date Frozen Layer No Construction No Record Status Freeze From Freeze To

83 3802 8/18/1997 0 1 A

83 3802 9/15/1997 0 1 A

87 1622 9/23/1993 0 1 A

87 1622 12/13/1993 1 1 A 0.222 0.374

87 1622 12/13/1993 2 1 A 1.032 1.133

87 1622 1/4/1994 1 1 A 0.222 0.273

87 1622 1/4/1994 2 1 A 0.373 0.981

87 1622 1/25/1994 1 1 A 0.222 1.183

87 1622 2/23/1994 1 1 A 0.222 1.234

87 1622 4/6/1994 0 1 A

87 1622 5/17/1994 0 1 A

87 1622 6/14/1994 0 1 A

87 1622 7/12/1994 0 1 A

87 1622 9/13/1994 0 1 A

87 1622 10/13/1994 0 1 A

87 1622 11/1/1994 0 1 A

87 1622 12/22/1994 0 1 A

87 1622 1/12/1995 1 1 A 0.475 0.930

87 1622 2/9/1995 1 1 A 0.273 1.031

87 1622 2/23/1995 1 1 A 0.527 1.031

87 1622 3/9/1995 1 1 A 0.779 0.879

87 1622 3/23/1995 1 1 A 0.930 1.031

87 1622 4/6/1995 0 1 A

87 1622 4/20/1995 0 1 A

87 1622 5/4/1995 0 1 A

87 1622 6/15/1995 0 1 A

87 1622 9/11/1996 0 1 A

87 1622 11/28/1996 0 1 A

87 1622 12/19/1996 0 1 A

87 1622 1/30/1997 1 1 A 0.578 0.879

87 1622 2/20/1997 1 1 A 0.628 1.031

87 1622 3/18/1997 0 1 A

87 1622 5/1/1997 0 1 A

87 1622 5/22/1997 0 1 A

87 1622 6/19/1997 0 1 A

87 1622 7/24/1997 0 1 A

87 1622 8/21/1997 0 1 A

87 1622 9/25/1997 0 1 A

87 1622 10/30/1997 0 1 A

89 3015 9/30/1993 0 1 A

89 3015 2/3/1994 1 1 A 0.271 1.384

89 3015 4/8/1994 0 1 A

89 3015 5/19/1994 0 1 A

89 3015 6/16/1994 0 1 A

89 3015 7/14/1994 0 1 A

89 3015 8/11/1994 0 1 A

89 3015 9/15/1994 0 1 A

89 3015 10/6/1994 0 1 A

89 3015 11/3/1994 0 1 A

89 3015 12/8/1994 0 1 A

89 3015 1/10/1995 1 1 A 0.271 0.978

89 3015 2/16/1995 1 1 A 0.371 0.574

89 3015 3/7/1995 0 1 A

89 3015 3/21/1995 0 1 A

89 3015 4/4/1995 0 1 A

89 3015 4/18/1995 0 1 A

89 3015 5/3/1995 0 1 A

89 3015 6/13/1995 0 1 A

89 3015 9/12/1996 0 1 A

89 3015 11/19/1996 0 1 A

129
State Code SHRP ID SMP Date Frozen Layer No Construction No Record Status Freeze From Freeze To

89 3015 12/17/1996 0 1 A

89 3015 1/28/1997 1 1 A 0.271 0.625

89 3015 2/18/1997 1 1 A 0.271 1.232

89 3015 3/20/1997 1 1 A 0.321 0.522

89 3015 4/29/1997 0 A

89 3015 5/20/1997 0 1 A

89 3015 6/17/1997 0 1 A

89 3015 8/19/1997 0 1 A

89 3015 9/23/1997 0 1 A

89 3015 11/6/1997 1 1 A 0.928 1.537

90 6405 10/6/1993 1 1 A 0.993 1.144

90 6405 10/7/1993 1 1 A 1.044 1.144

90 6405 11/10/1993 0 1 A

90 6405 1/14/1994 1 1 A 0.284 1.754

90 6405 2/11/1994 1 1 A 0.284 2.059

90 6405 3/11/1994 1 1 A 0.534 1.094

90 6405 3/11/1994 2 1 A 1.449 2.059

90 6405 3/25/1994 1 1 A 0.943 1.044

90 6405 3/25/1994 2 1 A 1.856 2.059

90 6405 4/8/1994 1 1 A 1.856 2.059

90 6405 4/28/1994 0 1 A

90 6405 5/12/1994 0 1 A

90 6405 6/16/1994 0 1 A

90 6405 7/21/1994 0 1 A

90 6405 8/16/1994 0 1 A

90 6405 9/22/1994 0 1 A

90 6405 10/20/1994 0 1 A

90 6405 11/17/1994 0 1 A

90 6405 12/16/1994 1 1 A 0.284 1.346

90 6405 1/23/1995 1 1 A 0.284 1.144

90 6405 3/13/1995 1 1 A 0.638 2.059

90 6405 3/27/1995 1 1 A 0.638 2.059

90 6405 4/24/1995 1 1 A 1.703 2.007

90 6405 5/18/1995 0 1 A

90 6405 6/23/1995 0 1 A

90 6405 10/18/1996 0 1 A

90 6405 12/12/1996 1 1 A 0.284 0.534

90 6405 2/10/1997 1 1 A 0.284 0.942

90 6405 2/10/1997 2 1 A 1.094 2.059

90 6405 4/18/1997 1 1 A 1.044 2.059

90 6405 5/2/1997 0 1 A

90 6405 6/3/1997 0 1 A

90 6405 6/16/1997 0 1 A

90 6405 8/21/1997 0 1 A

90 6405 9/19/1997 0 1 A

130