ERDC/CHL TR-20-19 

Regional Sediment Management and Dredging Operation and Environment Research 

Physical Factors That Influence Muddy Bed 

Aggregate Production, Size, and Durability

Coastal and Hydraulics Laboratory 

David W. Perkey, Kelsey A. Fall, and S. Jarrell Smith September 2020

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Regional Sediment Management and 

Dredging Operation 

ERDC/CHL TR-20-19 

September 2020 

Physical Factors That Influence Muddy Bed 

Aggregate Production, Size, and Durability

David W. Perkey, Kelsey A. Fall, and S. Jarrell Smith 

Coastal and Hydraulics Laboratory

U.S. Army Engineer Research and Development Center

3909 Halls Ferry Road

Vicksburg, MS 39180-6199

Final report

Approved for public release; distribution is unlimited. 

Prepared for The Regional Sediment Management Program (RSM) and the Dredging 

Operations and Environmental Research Program (DOER) Vicksburg, MS 

39180

Under RSM: “James River Federal Navigation Channel, Lower Reaches” – Account 

Code D15945 AMSCO 008303

DOER: “Aggregate Transport” – Account Code B3209G AMSCO 089500
ERDC/CHL TR-20-19 ii

Abstract

Aggregation state significantly influences the transport characteristics of 

fine sediments. While research has documented the presence of mud 

aggregates in multiple coastal and estuarine environments, bed aggregates 

are largely absent from numerical models used to predict cohesive 

sediment transport. The U.S. Army Corps of Engineers (USACE) is

conducting studies to evaluate the impact muddy bed aggregates have on 

sediment management issues, and how to account for aggregates in 

numerical models. 

In this study, physical properties associated with cohesive behavior were 

evaluated to determine if they could be used as predictors for bed 

aggregate production, size, and durability. Results showed that aggregates 

were consistently produced in cohesive sediments, and that median 

aggregate size was ~10-450x larger than the disaggregated sediment. Clay 

content had strong correlation with relative aggregate size, though 

statistically significant correlations were also found with sand content, 

water content, and density. Durability testing indicated that aggregate

break-up followed exponential models, and that in limited instances, rates 

of break-up correlated with organic content.

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ERDC/CHL TR-20-19 iii

Contents

Abstract.................................................................................................................................... ii

Figures and Tables...................................................................................................................v

Preface.................................................................................................................................... ix

1 Introduction ...................................................................................................................... 1

1.1 Background........................................................................................................1

1.2 Objective ............................................................................................................2

1.3 Approach ............................................................................................................2

2 Methods ............................................................................................................................ 4

2.1 Erosion testing ...................................................................................................4

2.1.1 Sedflume ..................................................................................................................... 4

2.1.2 Eroded particle imaging.............................................................................................. 6

2.1.3 Core preparation ......................................................................................................... 7

2.2 Test sediments...................................................................................................8

2.2.1 Great Lakes Region .................................................................................................... 9

2.2.2 Mid-Atlantic Coast....................................................................................................... 9

2.2.3 Mississippi River .......................................................................................................10

2.2.4 Gulf Coast.................................................................................................................. 10

2.3 Physical properties ..........................................................................................11

2.3.1 Plasticity Index ..........................................................................................................11

2.3.2 Organic content.........................................................................................................11

2.3.3 Water content and bulk density ...............................................................................11

2.3.4 Grain size................................................................................................................... 12

2.3.5 Volume fraction mud.................................................................................................12

2.4 Aggregate durability........................................................................................ 13

3 Results.............................................................................................................................15

3.1 Physical properties of test materials ..............................................................15

3.1.1 Plasticity Index ..........................................................................................................15

3.1.2 Organic content.........................................................................................................16

3.1.3 Water content and bulk density ...............................................................................16

3.1.4 LDPSA grain size .......................................................................................................16

3.1.5 Volume fraction mud.................................................................................................19

3.2 Erosion tests ................................................................................................... 19

3.2.1 Eroded particle size..................................................................................................19

3.2.2 Regression analyses.................................................................................................23

3.3 Durability ......................................................................................................... 26

4 Summary, Conclusions and Recommendations ........................................................32

4.1 Summary and conclusions............................................................................. 32

4.2 Recommendations ......................................................................................... 35
ERDC/CHL TR-20-19 iv

References.............................................................................................................................36

Appendix A: D50F/D50L Linear Regression Model Results ................................................39

Appendix B: Aggregate Abrasion Linear Regression Model Results .............................46

Unit Conversion Factors.......................................................................................................52

Acronyms ...............................................................................................................................53

Report Documentation Page
ERDC/CHL TR-20-19 v

Figures and Tables

Figures

Figure 1. Regional map of test sediment locations. ............................................................... 3

Figure 2. Images of the Sedflume (upper left), erosion surface (lower left), and 

operator (lower center), along with operational test range of the flume (right). .................. 4

Figure 3. Diagram of sediment core erosion process. The brown arrow indicates 

advancement of sediment into the flume with erosion. The blue arrow indicates 

flow direction of water. An example erosion sequence is provided in the table to 

the right of the sediment core................................................................................................... 5

Figure 4. Schematic showing the FICS system mounted to the outflow end of the 

Sedflume..................................................................................................................................... 6

Figure 5. Prepared mud aggregates (a) placed in test drum, (b) and tumbled in 

water bath, (c) for durability testing. .......................................................................................14

Figure 6. LDPSA distribution plots of Great Lakes Region samples from Duluth 

and Ashtabula Harbors............................................................................................................ 17

Figure 7. LDPSA distribution plots of Mid Atlantic Coast samples from Seven Mile 

Island and James River............................................................................................................18

Figure 8. LDPSA distribution plots of Mississippi River sediment mixtures. ......................18

Figure 9. LDPSA distribution plots of Gulf Coast samples from Gulfport, Calcasieu 

Ship Channel, and Houston Ship Channel.............................................................................19

Figure 10. FICS median particle size (μm) from all erosion tests plotted against 

shear stress (Pa). Solid line presents robust linear regression best fit, with R2 and 

p values provided. The dashed lines present the 95% confidence intervals of the 

fit. ...............................................................................................................................................21

Figure 11. FICS distribution plots of Great Lakes Region samples from Duluth 

and Ashtabula Harbors............................................................................................................22

Figure 12. FICS distribution plots of Mid Atlantic Coast samples from Seven Mile 

Island and James River............................................................................................................22

Figure 13. FICS distribution plots of Mississippi River sediment mixtures.........................23

Figure 14. FICS distribution plots of Gulf Coast Samples from Gulfport, Calcasieu 

Ship Channel, and Houston Ship Channel.............................................................................23

Figure 15. Median particle size ratios plotted against clay content. Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. ............................................ 24

Figure 16. Aggregate durability plots of Great Lakes Region samples from Duluth 

and Ashtabula Harbors. Dotted lines indicate exponential fits with associated R2

values.........................................................................................................................................27

Figure 17. Aggregate durability plots of Mid Atlantic Coast samples from Seven 

Mile Island and James River. Dotted lines indicate exponential fits with 

associated R2 values................................................................................................................27

Figure 18. Aggregate durability plots of Mississippi River sediment mixtures. 

Dotted lines indicate exponential fits with associated R2 values. .......................................28
ERDC/CHL TR-20-19 vi

Figure 19. Aggregate durability plots of Gulf Coast samples from Gulfport, 

Calcasieu Ship Channel, and Houston Ship Channel. Dotted lines indicate 

exponential fits with associated R2 values.............................................................................28

Figure 20. Aggregate abrasion rate (δ) plotted against clay content. Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The

dashed lines present the 95% confidence intervals of the fit. ............................................30

Figure 21. Aggregate abrasion rate (δ) plotted against LOI. Solid line presents the 

robust linear regression best fit, with R2 and p values provided. The dashed lines 

present the 95% confidence intervals of the fit. Red circles indicate estimated 

MS029 and MS021 data points.............................................................................................31

Figure A1. Median particle size ratios plotted against clay content. Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. ............................................39

Figure A2. Median particle size ratios plotted against sand content. Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. ............................................40

Figure A3. Median particle size ratios plotted against water content. Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. ............................................40

Figure A4. Median particle size ratios plotted against bulk density (ρ). Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. ............................................ 41

Figure A5. Median particle size ratios plotted against Plasticity Index (PI). Solid 

line presents the robust linear regression best fit, with R2 and p values provided. 

The dashed lines present the 95% confidence intervals of the fit. Fit parameters 

are shown in red due to model p-values >0.05. ................................................................... 41

Figure A6. Median particle size ratios plotted against solid volume faction mud 

(φsm). Solid line presents the robust linear regression best fit, with R2 and p 

values provided. The dashed lines present the 95% confidence intervals of the 

fit. Fit parameters are shown in red due to model p-values >0.05.....................................42

Figure A7. Median particle size ratios plotted against LOI. Solid line presents the 

robust linear regression best fit, with R2 and p values provided. The dashed lines 

present the 95% confidence intervals of the fit. Fit parameters are shown in red 

due to model p-values >0.05..................................................................................................42

Figure B1. Aggregate abrasion rate (δ) plotted against sand content. Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. ............................................46

Figure B2. Aggregate abrasion rate (δ) plotted against water content (w). Solid 

line presents the robust linear regression best fit, with R2 and p values provided. 

The dashed lines present the 95% confidence intervals of the fit...................................... 47

Figure B3. Aggregate abrasion rate (δ) plotted against bulk density (ρ). Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. ............................................ 47

Figure B4. Aggregate abrasion rate (δ) plotted against plasticity index (PI). Solid 

line presents the robust linear regression best fit, with R2 and p values provided. 

The dashed lines present the 95% confidence intervals of the fit......................................48
ERDC/CHL TR-20-19 vii

Figure B5. Aggregate abrasion rate (δ) plotted against solid volume fraction mud 

(φsm). Solid line presents the robust linear regression best fit, with R2 and p 

values provided. The dashed lines present the 95% confidence intervals of the 

fit. ...............................................................................................................................................48

Tables

Table 1. Prepared test cores. .................................................................................................... 8

Table 2. Physical properties of tested sediments. Shaded cells identify samples 

with sand content >50%..........................................................................................................15

Table 3. Erosion test conditions and FICS median grain size (D50F)....................................20

Table 4. D50F/D50L Robust liner regression model outputs. Red font indicates 

variable p-values >0.05 and not statistically significant to the model. .............................. 24

Table 5. D50F/D50L Multiple liner regression model outputs presented in 

ascending R2 values. * indicates model with all p-values ≤0.05. Red font 

indicates variable p-values >0.05 and not statistically significant to the models.............25

Table 6. Aggregate durability tumbling results. Water content of tumbled 

aggregates (w) and fraction >250 μm from wet sieve tests are presented along 

with tumbling abrasion rate (δ). * indicates samples with reduced w values for 

aggregate testing......................................................................................................................29

Table 7. Aggregate abrasion rate robust liner regression model outputs. Red font 

indicates variable p-values >0.05 and not statistically significant to the model...............30

Table A1. D50F/D50L multiple linear regression model outputs (two parameters). 

Red font indicates variable p-values >0.05 and not statistically significant to the 

models.......................................................................................................................................43

Table A2. D50F/D50L multiple linear regression model outputs (three parameters). 

Red font indicates variable p-values >0.05 and not statistically significant to the 

models.......................................................................................................................................43

Table A3. D50F/D50L multiple linear regression model outputs (four variables). Red 

font indicates variable p-values >0.05 and not statistically significant to the 

models.......................................................................................................................................44

Table A4. D50F/D50L multiple linear regression model outputs (five parameters). 

Red font indicates variable p-values >0.05 and not statistically significant to the 

models.......................................................................................................................................45

Table A5. D50F/D50L multiple linear regression model outputs (six parameters). 

Red font indicates variable p-values >0.05 and not statistically significant to the 

models.......................................................................................................................................45

Table B1 Abrasion rate multiple linear regression model outputs (two 

parameters). Red font indicates variable p-values >0.05 and not statistically 

significant to the models. ........................................................................................................49

Table B2. Abrasion rate multiple linear regression model outputs (three 

parameters). Red font indicates variable p-values >0.05 and not statistically 

significant to the models. ........................................................................................................49

Table B3. Abrasion rate multiple linear regression model outputs (four 

parameters). Red font indicates variable p-values >0.05 and not statistically 

significant to the models. ........................................................................................................50
ERDC/CHL TR-20-19 viii

Table B4. Abrasion rate multiple linear regression model outputs (five 

parameters). Red font indicates variable p-values >0.05 and not statistically 

significant to the models. ........................................................................................................ 51

Table B5. Abrasion rate multiple linear regression model outputs (six 

parameters). Red font indicates variable p-values >0.05 and not statistically 

significant to the models. ........................................................................................................ 51
ERDC/CHL TR-20-19 ix

Preface

This study was conducted for the Regional Sediment Management (RSM) 

under “James River Federal Navigation Channel, Lower Reaches” and the

Dredging Operations and Environmental Research (DOER) Programs

under “Aggregate Transport.” 

The work was performed by the Field Data Collection and Analysis Branch

of the Navigation Division, U.S. Army Engineer Research and 

Development Center, Coastal and Hydraulics Laboratory (ERDC-CHL). At 

the time of publication, Mr. William Butler was Chief of the Field Data 

Collection and Analysis Branch; Dr. Jacqueline Pettway was Chief of the 

Navigation Division; and Mr. Charles E. Wiggins was the Technical 

Director for Navigation. The Deputy Director of ERDC-CHL was Mr. 

Jeffrey R. Eckstein, and the Director was Dr. Ty V. Wamsley.

The Commander of ERDC was COL Teresa A. Schlosser, and the Director 

was Dr. David W. Pittman.
ERDC/CHL TR-20-19 1

1 Introduction

1.1 Background

Effective sediment management within the nation’s navigable waterways 

and coastal areas is an important mission to the U.S. Army Corps of 

Engineers (USACE). In many instances, the sediments in these areas are 

mixtures of sand, silt, and clay; making their erosion and transport 

processes more complex than sand alone (Mehta et al. 1989; Mitchner and 

Torfs 1996). Research has shown that the presence of fine (<63 μm) 

material within the sediment matrix leads to cohesive behavior (Mitchner 

and Torfs 1996; Van Ledden et al. 2004; Barry et al. 2006; Jacobs et al. 

2011; Dickhudt et al. 2009, 2011; Wu et al. 2018) and the production of 

aggregated clasts upon mobilization from the bed (Mitchner and Torfs 

1996; Jepsen et al. 2010; Schieber et al. 2010; Mehta 2013, Winterwerp et 

al. 2012; Perkey et al. 2020). As summarized by Perkey and Smith (2019), 

muddy bed aggregates have been documented to occur both in the 

lithological record and in modern deposits across a wide range of 

environments. They have also been shown to frequently occur in 

conjunction with dredging of the consolidated sediment bed (e.g. Fettweis 

et al. 2009; Smith and Friedrichs 2011; Carey et al. 2013). Aggregated 

clasts have been shown to have settling velocities, densities, and transport 

pathways that are significantly different from their constituent particles 

(e.g. Jepsen et al. 2010; Schieber et al. 2010; Smith and Friedrichs 2011; 

Perkey et al. 2020). However, limited information is available regarding 

the physical properties that lead to the production, abundance, size, and 

durability of these muddy aggregates. This knowledge gap limits the 

capability for these types of aggregated clasts to be incorporated into 

sediment management studies. 

Differences in transport characteristics (e.g. initiation, mode, frequency) 

between bed aggregates and disaggregated constituent particles have 

significant implications for sediment transport management. At present,

many numerical models used in sediment management projects (e.g. AdH, 

SEDZLJ, Delft3D, ROMS) do not include aggregate properties and 

transport processes in their simulations, and limit the transport of fine 

sediment to suspended load (Brown et al. 2019; Thanh et al. 2008; Lesser 

et al. 2004; Delft Hydraulics 2007; Warner et al. 2008, 2010). Because 

these bed aggregates can significantly alter the transport and fate of fine 
ERDC/CHL TR-20-19 2

sediment, guidance is needed to inform the scientific and engineering 

community on the physical properties that result in bed aggregate 

production, and when it is appropriate to incorporate them into numerical 

sediment transport simulations.

1.2 Objective

The goal of this technical report is to identify commonly utilized physical 

properties of sediment that can be used to predict the production, size, and 

durability of muddy bed aggregates upon erosion. Predictive capabilities 

such as these may then be used to develop methods that allow for the

incorporation of muddy bed aggregates into numerical transport models. 

1.3 Approach

The production of aggregated particles upon erosion from the bed has 

been linked to the cohesive nature of sediment (Mitchner and Torfs 1996; 

Jepsen et al. 2010; Schieber et al. 2010; Mehta 2013, Winterwerp et al. 

2012; Perkey et al. 2020). Sediment cohesion is commonly impacted by 

sediment properties such as grain size, density, water content, plasticity, 

volume fraction mud, and organic content (Mitchner and Torfs 1996; 

Jacobs et al. 2011; Dickhudt et al. 2009, 2011; Wu et al. 2018; Grant and

Gust 1987; Yallop et al. 1994). Sediments from a variety of harbors, 

channels, and waterways from across the country were evaluated to 

determine if physical properties that impact cohesion could be used to 

predict the production of eroded bed aggregates (Figure 1). These 

sediments were selected due to their heterogeneous composition, wide 

geographic spread, and association with recently conducted USACE 

sediment management projects.
ERDC/CHL TR-20-19 3 

Figure 1. Regional map of test sediment locations. 

To test these materials for bed aggregate production, flume based 

experiments were performed with the USACE developed Sedflume 

(McNeil et al. 1996) coupled with the Flume Imaging Camera System 

(FICS) (Perkey et al. 2020; Fall et al., 2020). The occurrence and relative 

size of bed aggregates was determined by comparing the median grain size 

of eroded particles to the median grain size of the disaggregated sediment

test beds. Correlations between these median grain size ratios and the 

various physical properties measured for each sediment were examined to 

assess if certain parameters were viable predictors for the presence and 

relative size of bed aggregates. Additionally, the durability of aggregated 

clasts formed from each of the test materials was characterized with a 

modified Slake Durability tumbling device. 
ERDC/CHL TR-20-19 4

2 Methods

2.1 Erosion testing

2.1.1 Sedflume

All erosion testing was performed with the USACE- developed Sedflume, 

which is a derivative of the flume developed by researchers at the 

University of California at Santa Barbara (McNeil et al. 1996). The flume 

includes an 80-cm-long inlet section (Figure 2) with cross-sectional area 

of 2 × 10 cm for uniform, fully developed, smooth-turbulent flow, as 

described in McNeil et al. (1996). The inlet section is followed by a test 

section with a 10-cm diameter open bottom. Coring tubes and flume test 

section, inlet section, and exit sections are constructed of clear 

polycarbonate materials to permit observation of sediment-water 

interactions during the course of erosion experiments. The flume includes 

a port over the test section to provide access to the core surface for 

physical sampling. The flume accepts sediment cores up to 80-cm in 

length.

Figure 2. Images of the Sedflume (upper left), erosion surface (lower left), and 

operator (lower center), along with operational test range of the flume (right). 
ERDC/CHL TR-20-19 5 

Cores are inserted into the testing section of Sedflume and a screw jack is 

used to advance the plunger such that the core surface becomes flush with 

the bottom wall of the flume. Flow is directed over the sample by diverting 

water from a 5.5-hp trash-pump, through a 5-cm inner diameter hose, into 

the flume. The flow through the flume produces shear stress on the surface 

of the core. Numerical, analytical, and experimental analyses have been 

performed to relate flow rate to bottom shear stress (Figure 2). As 

sediment is eroded from the core surface, the operator advances the screw 

jack to maintain the sediment surface flush with the bottom wall of the 

erosion flume. Erosion experiments are performed by repeating a 

sequence of increasing shear stresses. Approximately 1-5 mm of sediment 

is eroded at each specified shear stress; thus, the duration of each test is 

dependent on the rate of erosion. A diagram depicting this erosion test 

process along with an example erosion sequence is shown in Figure 3. 

Figure 3. Diagram of sediment core erosion process. The brown arrow indicates 

advancement of sediment into the flume with erosion. The blue arrow indicates flow 

direction of water. An example erosion sequence is provided in the table to the right 

of the sediment core. 
ERDC/CHL TR-20-19 6 

2.1.2 Eroded particle imaging 

The FICS is an ERDC-developed system designed to characterize grain size 

distributions of sediment particles immediately following mobilization 

from the bed (Figure 4). The FICS consists of a clear polycarbonate 

channel, an Allied Vision, Manta G504B camera equipped with an Opto 

Engineering TC23056 bi-telecentric lens, and an Allied Vision LED back 

light paired with a Pulsar 320 strobe controller. The FICS channel is 

designed to attach directly to the outflow end of the Sedflume. It measures 

22.5 cm in length and has the same 2 x 10 cm cross-sectional area as the 

Sedflume channel. The camera and lens are centrally mounted 12.8 cm 

above the top of the channel. FICS images an area of 4.5 x 5.3 cm, with a 

focal depth of 2.7 cm. Magnification of the system is 0.157x, resulting in a 

subject pixel size of ~22 μm. Videos were collected at a rate of 3 frames per 

second (fps), with an exposure of 500 μs and gain set to 10. The backlight 

was pulsed with 24V, 50 A with a pulse width of 30 μs. FICS videos had a 

default length of 240 frames (80 s) and were collected at every erosion 

interval. In cases where the erosion interval duration was less than 80 s, 

video collection was terminated early. For longer erosion intervals 

(duration exceeding 5 min), multiple videos were collected approximately 

every 5 min. 

Figure 4. Schematic showing the FICS system mounted to the outflow end of the 

Sedflume. 

An automated image analysis routine was used to characterize the size of 

eroded particles in FICS images. Prior to erosion testing, a calibration grid 

was inserted into the FICS channel and photographed for the purpose of 
ERDC/CHL TR-20-19 7

transforming pixel space to length. The processing routine utilizes this 

gridded image and employs algorithms from the MATLAB Image 

Processing Toolbox. It combines local intensity thresholding with particle 

vetting to identify particles, while omitting unwanted features such as 

background objects, air bubbles, and out of focus particles. FICS image 

processing requires particles to appear in an area of at least 3 x 3 pixels. 

Therefore, the system can only accurately size particles with equivalent 

spherical diameters (esd) greater than approximately 66 μm. 

Prior to the start of erosion testing, background FICS videos of site water 

were recorded to account for any sediment particles present in the water 

not associated with bed erosion. To analyze the imaged particles, a total of 

19 equal logarithmically spaced (~1/3 φ) particle size bins were generated 

that spanned the size range of 63 μm – 16000 μm. These bin properties 

were used to generate a volume based particle size distribution for all the 

FICS videos. FICS distributions obtained from background videos were 

subtracted from videos recorded during erosion testing to produce a net 

distribution representative of eroded particles. These distributions were 

used to obtain the median size of eroded particles observed by the FICS 

(D50F). Further details on these processing techniques can be found in 

Smith and Friedrichs (2011) and Fall et al. (2020).

2.1.3 Core preparation

The general guidelines for preparing sediment mixtures is to sufficiently 

homogenize the sample to minimize down-core variability of sediment 

properties that would otherwise affect erosion behavior. To achieve this, 

all sediment samples were mixed for a minimum of 30 min before transfer 

into 10-cm diameter polycarbonate tubes. Prior to placing sediment in the 

tube, a plunger with bentonite paste (for sealing and lubrication) was 

inserted into the bottom of the core. 

Two methods were utilized to prepare cores for erosion testing. For 

mixtures that displayed fluid behavior, the sample was poured through a 

funnel and pipe, which was inserted into the core tube, allowing the 

sediment column to be filled from the bottom up and minimize gas 

entrapment. For samples with lower water contents that displayed more 

plastic and granular behavior, samples were tamped into the core tube in 

approximately 1 to 2 cm lifts. After filling the core tubes, overlying water 

was placed on top of the sediment column and the prepared cores were 

allowed to consolidate in a 4°C cooler for approximately 30 days prior to 
ERDC/CHL TR-20-19 8

erosion. Table 1 provides sample preparation method information for each 

of the cores. A brief description of each test material is provided in the 

following section.

Table 1. Prepared test cores. 

Sample 

Name 

Preparation 

Method 

Location Material Description 

DH Poured Slurry Duluth Harbor Dredged Sediment 

(Composite)

ARE45 Poured Slurry Inner Ashtabula Harbor Native Bottom Sediment

ARE60 Poured Slurry Outer Ashtabula Harbor Native Bottom Sediment

SMIIL Poured Slurry Seven Mile Island Native Bottom Sediment 

(Composite)

JR Poured Slurry James River Dredged Sediment

MS100 Poured Slurry Mississippi River Native Bottom Sediment

MS029 Tamped Lifts Mississippi River Lab Prepared Mixture

MS021 Tamped Lifts Mississippi River Lab Prepared Mixture

MS011 Tamped Lifts Mississippi River Lab Prepared Mixture

MS003 Tamped Lifts Mississippi River Lab Prepared Mixture

GP Poured Slurry Gulfport Entrance Channel Native Bottom Sediment 

CSC Poured Slurry Calcasieu Shipping 

Channel

Native Bottom Sediment 

(Composite)

HSC Poured Slurry Houston Shipping Channel Dredged Sediment

2.2 Test sediments

In this study, a series of bottom sediments collected from multiple federal 

navigation channels and were evaluated for their propensity to produce 

aggregated clasts upon erosion. All of these sediments were held in a 4°C 

cooler at the Engineer Research and Development Center-Coastal and 

Hydraulics Laboratory (ERDC-CHL) sediment properties facility in 

Vicksburg, MS prior to use in this study. A map indicating the regional 

locations of each of the sediment types is provided in Figure 1. Samples 

were grouped into four classifications based on geographical setting: 1) 

Great Lakes Region, 2) Mid-Atlantic Coast, 3) Mississippi River, and 4) 

Gulf Coast. A brief description of each sediment type is provided in this 

section.
ERDC/CHL TR-20-19 9

2.2.1 Great Lakes Region

2.2.1.1 Duluth Harbor, MN (DH)

In 2013, the USACE Detroit District (LRE) conducted a study concerning 

the transport potential of dredged material used for newly constructed 

shoals and islands within Duluth Harbor, MN. These sediments were 

muddy in texture with fines contents (<63 μm) ranging from 

approximately 55-90%. Notes from initial erosion testing indicated the 

presence of muddy bed aggregates produced upon erosion. Archived 

sediments from the 2013 experiments were composited for testing in this 

study. 

2.2.1.2 Ashtabula Harbor, OH

Beneficial use of dredge material projects are currently being conducted by 

the USACE Buffalo District (LRB) within Ashtabula Harbor, OH. To 

characterize the sediment properties and evaluate the transport processes 

of dredged material following placement, bottom grab samples were 

collected in 2018 from the inner (ARE-45) and outer (ARE-60) regions of 

the harbor. Inner harbor material was muddy in texture with a fines

content of ~95%, while outer harbor sediments were coarser in 

composition (54% sand). Both materials were used in this study.

2.2.2 Mid-Atlantic Coast

2.2.2.1 Seven Mile Island, NJ

In 2019, the USACE Philadelphia District (NAP) launched the Seven Mile 

Island Innovation Laboratory (SMIIL) to advance and improve dredge 

material management and marsh restoration techniques in coastal New 

Jersey. Sediments from the proposed dredging area within the New Jersey 

Intracoastal Waterway were collected. Samples from the channel were 

found to be composed of approximately 60% fines and 40% sand. 

2.2.2.2 James River, VA (JR)

The USACE Norfolk District (NAO) is conducting a series of sediment 

management studies in the James River, VA in an effort to improve the 

understanding of impacts associated with existing dredge material 

placement strategies within the system. In 2017, bottom sediments were 

collected from the Goose Hill Shoal placement area and shipped to the 
ERDC/CHL TR-20-19 10

ERDC-CHL for Sedflume erosion testing. Sediments from the upstream 

portion of the shoal had fines contents of approximately 80% and initial 

erosion testing notes indicated the presence of muddy bed aggregates 

upon erosion. Archived material from the upriver portions of the shoal 

were utilized for further erosion and bed aggregate testing in this study.

2.2.3 Mississippi River

Mud from a perched boat basin off the Mississippi River near Grand Gulf, 

MS was collected by ERDC-CHL in 2014 for use in a study to evaluate 

relationships between mud content and erosion thresholds. To achieve 

this, a series of sand-mud mixtures were prepared with known amounts of 

Mississippi River mud and fine silica sand (Wu et al. 2018). Five of the 

archived mixtures from this study had adequate remaining volumes for 

further testing in this study. These samples were mixtures composed of 

3%, 11%, 21%, 29%, and 100% Mississippi River bottom material (MS003, 

MS011, MS021, MS029, and MS100, respectively). 

2.2.4 Gulf Coast

2.2.4.1 Gulfport, MS (GP)

Bottom sediments were obtained from portions of the Gulfport, MS 

navigation channel in 2012 as part of an investigation to evaluate the 

impacts of fluid mud on nautical depth in the region. Collected samples 

had a fines content of 97%. Subsamples of these archived sediments were

used in this study.

2.2.4.2 Calcasieu Shipping Channel, LA

In 2019, the USACE New Orleans District (MVN) conducted a regional 

sediment management study in the Calcasieu Shipping Channel (CSC), LA 

to investigate the sources of channel shoaling. As part of that study, 

bottom drag samples were collected from stretches of the navigation 

channel. Channel sediments were found to be muddy in texture (~70-90% 

fines). A composite sample was prepared and tested in this study. 

2.2.4.3 Houston Shipping Channel, TX (HSC)

Newly placed dredged material from the Houston Shipping Channel was 

collected from a wetland restoration project being conducted by the 

USACE Galveston District (SWG) near Atkinson Island, TX in 2018. 
ERDC/CHL TR-20-19 11

Samples were found to be stiff muds composed of 99% fines. Composite 

slurries of this sediment were prepared and evaluated in this study.

2.3 Physical properties

Sub-samples of each sediment type were set aside for the purpose of 

Atterberg Limit and organic content analysis before the preparation of 

sediment cores. Additional physical samples for bulk sediment property 

measurements were taken at periodic intervals during Sedflume testing, 

generally at the end of an erosion cycle (Figure 3). These samples were 

collected by draining the flume channel, opening the port over the test 

section, and extracting a sample from the sediment bed. Samples collected 

in this manner were used to characterize the bulk density, disaggregated 

grain-size distribution, and volume fraction mud of the test bed. The 

methods used to determine these properties are presented in this section.

2.3.1 Plasticity Index

The Plasticity Index (PI) of each material was obtained through standard 

Atterberg Limit testing. Standard methods, described in ASTM D4318

(2015), were utilized to measure the liquid and plastic limits. To obtain the 

liquid limit, the multipoint method was utilized with a motorized device to 

maintain a constant drop rate. Plastic limit testing was conducted on a 

polished granite counter top using the hand method. The difference 

between the two limits yields the PI for each sample, which indicates the 

breadth of moisture content for which the material behaves plastically.

2.3.2 Organic content 

To evaluate the total, volatile organic content of the sediment samples, loss 

on ignition (LOI) techniques described in ASTM D2974, method C (2014)

were used. Following recommendations reported by Schumacher (2002) 

and Salehi et al. (2011), combustion temperature was reduced from 440o C 

down to 360o C.

2.3.3 Water content and bulk density

Water content (w) of each sample was measured through wet-dry weight

analysis following ASTM D2216-19 (2019) in which w is given by
ERDC/CHL TR-20-19 12

w d

d

m m

w

m

  − =    

(1)

where mw and md are the wet and dry weights, respectively. The total 

volume of sample was assumed to consist of both solid particles and water, 

with assumed densities of 2.65 g/cm3 and 1.0 g/cm3, respectively. The bulk 

density as a function of w and the densities of the sediment particles (ρs) 

and water (ρw) was calculated with equation 2, derived from Jepsen et al. 

(2010).

sw s ( ) s

w s

w

w

ρρ ρ

ρ ρ

ρ ρ

− = +

+

(2)

2.3.4 Grain size 

Grain size distributions and characterizations were obtained through 

Laser Diffraction Particle Size Analysis (LDPSA) with a Malvern 

Mastersizer 2000, which measures particle sizes in terms of equivalent 

spherical diameters (esd) between the range of 0.02 to 2000 μm. 

Sediments were homogenized and disaggregated overnight in a solution of 

sodium metaphosphate (40 g/L). To remove macro organic material, 

samples were passed through a 1000 μm sieve into the instrument’s 

reservoir and sonicated for 60 sec prior to analysis. Median grain size 

(D50L) was reported and the Wentworth scale (1929) was used for the 

classification of sand (>63 μm), silt (63-4 μm), and clay (<4 μm) sized 

particles. 

2.3.5 Volume fraction mud

The water content, bulk density, and LDPSA grain size methods previously 

described were utilized to calculate the volume fraction mud (φsm) for test 

material. The equation used for calculating φsm follows methods described 

in Dickhudt et al. (2009, 2011), 

1

stot s stot

sm

s stot

f

f

φ φ φ φ− = − (3)

in which φstot is the total solids volume fraction, and fs is the fraction sand 

of the sample.
ERDC/CHL TR-20-19 13

2.4 Aggregate durability

To evaluate the durability of mud aggregates, a Slake Durability tumbler 

was utilized. This tumbler is a device that consists of a steel mesh drum (14

cm dia.) that is partially immersed and rotated at a speed of 20 rpm within 

a water bath. The apparatus is designed for the testing of shales and 

similar weak rock fragments. A detailed description of the device can be 

found in ASTM D4644 (1998). For the purposes of evaluating bed 

aggregates in this study, the standard 2 mm wire mesh of the rotating 

drum was replaced with 250 μm mesh. 

For each sample, cube-like aggregates of ~1 cm3 volume were prepared by 

extruding and slicing test material from cores or molds (Figure 5a). When 

possible, this was done using the remaining material following erosion 

testing. In some instances, water content of the eroded material was too 

high to allow for slicing. In these cases, w was reduced through an iterative 

process until a content was reached that allowed for slicing and aggregate 

preparation. These samples are indicated with an asterisk (*) in Table 5. 

Typically 3-5 aggregates were utilized for each durability test (Figure 5b). 

Aggregates were tumbled at intervals of 2.5, 5, 10, and 20 min (Figure 5c), 

which roughly correspond to linear tumbling distances of 25 m, 50 m, 100 

m, and 200 m, respectively. At the completion of each test interval, the 

drum was removed from the water bath and the remaining contents >250 

μm were filtered through pre-weighed, 90-mm diameter glass fiber filters 

with retention rating of 0.7 μm. Similarly, the contents of the water basin 

(<250μm) were filtered through pre-weighed 142 mm diameter glass fiber 

filters with retention rating of 0.7 μm. Filters were dried in a 50° C oven 

overnight before obtaining final dry masses of fractions >250 μm and 

<250 μm.

Standard wet sieving techniques were used on an aliquot of each test 

material to assess the mass fraction >250 μm prior to aggregate durability 

testing (ASTM D6913 2009). This fraction was subtracted from the mass 

fraction retained within the tumbler basket to yield a net mass fraction 

>250 μm (FMa >250) due to the aggregated state of the sediment.
ERDC/CHL TR-20-19 14

Figure 5. Prepared mud aggregates (a) placed in test drum, (b) and tumbled in water 

bath, (c) for durability testing. 
ERDC/CHL TR-20-19 15

3 Results

3.1 Physical properties of test materials

The results of the physical parameters measured for each test material are 

presented in this section. A brief description of the findings obtained for 

each parameter is presented first, followed by the summarization of all 

physical property data in Table 2.

Table 2. Physical properties of tested sediments. Shaded cells identify samples with 

sand content >50%. 

Sample 

Name PI %  LOI 

ρ 

(g/cm3) w D50L 

(μm) 

% 

Sand 

% 

Silt 

% 

Clay φsm 

DH 30.5 3.4 1.52 0.83 24.6 31.3 60.7 8.0 0.2

ARE45 25.6 3.1 1.48 0.91 12.7 30.6 63.8 5.6 0.3

ARE60 N/A 1.0 1.80 0.40 77.2 85.1 13.5 1.4 0.3

SMIIL 36.6 3.9 1.40 1.18 39.2 40.9 52.7 6.4 0.2

JR 59.9 4.5 1.37 1.31 15.7 18.1 68.7 13.2 0.2

MS100 49.4 3.5 1.46 0.97 10.9 3.0 80.9 16.1 0.3

MS029 8.0 N/A 1.91 0.31 136.7 78.1 18.1 3.8 0.2

MS021 N/A N/A 2.01 0.24 146.4 85.1 12.3 2.6 0.2

MS011 N/A N/A 2.07 0.21 155.3 92.4 6.0 1.6 0.1

MS003 N/A N/A 2.01 0.24 161.5 99.9 0.1 0.0 0.0

GP 83.0 4.6 1.23 2.33 9.9 1.2 82.0 14.7 0.1

CSC 48.1 2.9 1.35 1.38 44.5 43.9 46.4 9.7 0.1

HSC 43.7 0.4 1.38 1.28 6.1 0.3 67.6 32.1 0.2

3.1.1 Plasticity Index

Atterberg limit testing showed that nine (9) of the thirteen (13) test 

samples displayed plastic behavior. The PI values of these samples ranged 

from 8.0-83.0, with the 29% Mississippi River sediment mixture (MS029)

having the lowest PI and the Gulfport entrance channel sediment (GP)

showing the highest value. Plastic behavior was not observed for the 

Mississippi River sediment mixtures with mud content below 29% 

(MS021, MS011, and MS003) or the outer Ashtabula Harbor sample 
ERDC/CHL TR-20-19 16

(ARE60). Therefore, no PI value could be reported for these materials. PI 

results for each material are presented in Table 2.

3.1.2 Organic content

Organic content measurements through LOI techniques were carried out 

on nine (9) of the thirteen (13) test samples. Due to limited amounts of 

archived test material, LOI measurements were not performed on the four 

laboratory prepared mixtures of Mississippi River mud and silica test sand 

(MS029, MS021, MS011, MS003). LOI values ranged from 0.4% -4.6%, 

with the Houston Ship Channel (HSC) sediment having the lowest LOI

and the GP sediment showing the highest organic content. A complete 

listing of the LOI results is presented in Table 2.

3.1.3 Water content and bulk density

Wet-dry analyses of the test sediments showed that w ranged from 0.2-2.3

and corresponding bulk densities ranged from 1.2 – 2.1 g/cm3. As shown 

in equation 2, ρ and w are inversely related, such that samples with the 

lowest w have the highest bulk density values. The GP sediment was found 

to have the highest w and lowest ρ, while MS011 had the lowest w and 

highest ρ. A complete listing of the water content and density results is 

presented in Table 2.

3.1.4 LDPSA grain size 

Grain size analysis performed on the disaggregated bed samples showed 

that most of the sediment beds in this study were muddy in texture, with a 

fines content greater than 50% (Table 2). The sandy samples in the study 

were limited to the four laboratory prepared sand-mud mixtures made 

with the Mississippi River sediment, and ARE60, as identified by shading 

in Table 2. Sand contents of these five samples ranged from 78.1% - 99.9%. 

It is worthwhile to note that while the ARE60 sediment was found to be 

sandy (54%) in texture prior to erosion testing, bed samples collected 

during erosion testing showed an elevated sand contents of 85% (Table 2).

This change is due to the separation of sand and fines that occurred during 

the 30 days of consolidation prior to erosion testing. A visible (~2 cm 

thick) layer of fine grain mud was present at the surface of the ARE60 

core. This layer was removed prior to erosion testing so that down core

changes in sediment texture did not influence erosion testing results. Bed 
ERDC/CHL TR-20-19 17

samples collected below the surface 2 cm showed a consistent sand 

content of ~85%.

LDPSA grain size distribution plots revealed that the sands in all samples 

were largely limited to grains <500 μm in size (Figures 6-9). MS003 was 

shown to have the highest sand content and coarsest median grain size 

(D50L) at 99.9% and 161.5 μm, respectively (Table 2). However, grain size 

distribution plots showed that ARE60 and CSC contained the coarsest 

sands, with 7.6% and 3.6% of these sediments ≥500 μm, respectively. 

Grains > 1 mm were rarely observed in any of the sediments (Figures 6-9).

Figure 6. LDPSA distribution plots of Great Lakes Region samples from Duluth and 

Ashtabula Harbors. 
ERDC/CHL TR-20-19 18

Figure 7. LDPSA distribution plots of Mid Atlantic Coast samples from Seven Mile 

Island and James River. 

Figure 8. LDPSA distribution plots of Mississippi River sediment mixtures. 
ERDC/CHL TR-20-19 19

Figure 9. LDPSA distribution plots of Gulf Coast samples from Gulfport, Calcasieu 

Ship Channel, and Houston Ship Channel. 

3.1.5 Volume fraction mud

Use of the LDPSA sand data, along with the wet-dry analyses data, allowed 

for the calculation of the φsm for each sediment type in the study. These 

values ranged from 0.0-0.3. Sample MS003 had a small mud content 

(0.1%) producing a very small φsm value reported as 0.0 in Table 2. The two 

Ashtabula Harbor samples (ARE45 and ARE60) along with the 100% 

Mississippi River sediment showed the highest φsm values of 0.3.

3.2 Erosion tests

3.2.1 Eroded particle size

Sedflume erosion tests were conducted within the upper 10 cm of each 

core at shear stresses that ranged from 0.3 Pa – 7.4 Pa (Table 3). Visual 

observations and notes consistently indicated the mobilization of 

aggregated clasts from the sediment beds. Descriptions of cloudy water 

downstream of the erosion surface and winnowing of fine sediments from 

the bed were limited to tests conducted on sample MS003. 
ERDC/CHL TR-20-19 20

Table 3. Erosion test conditions and FICS median grain size 

(D50F). 

Sample 

Name 

Depth Range 

(cm) 

Shear Stress 

Range (Pa) 

D50F 

(μm) D50F/D50L 

DH 0-3 1.0-2.5 2170 88

ARE45 3-8 0.2-1.3 4070 320

ARE60 2-7.5 0.5-1.9 260 3.4

SMIIL 0-4 0.4-4.6 1470 38

JR 0-7.5 1.2-4.8 3850 250

MS100 0-3 0.9-3.4 2990 270

MS029 0-5 3.5-7.4 3010 22

MS021 0-3 1.0-6.3 2790 19

MS011 0-2 0.7-3.4 1500 10

MS003 0.5-3 0.3-1.1 220 1.4

GP 0-4 1.0-3.6 4440 450

CSC 0-7.5 1.2-3.1 2180 49

HSC 0-4.5 0.9-3.2 1720 280

FICS data from each erosion test showed median particle sizes on the 

order of hundreds of microns to several millimeters (Figure 10). Robust 

linear regression analysis showed a weak (R2=0.22, p<0.001) positive 

correlation between D50F and shear stress. Similarly, weak correlations

were observed in FICS data collected in a field study within the James 

River Estuary, though statistical significance (p<0.05) of the correlation 

was limited to shear stresses ≤2 Pa (Perkey et al. 2020).
ERDC/CHL TR-20-19 21

Figure 10. FICS median particle size (μm) from all erosion tests plotted against shear 

stress (Pa). Solid line presents robust linear regression best fit, with R2 and p values 

provided. The dashed lines present the 95% confidence intervals of the fit. 

Because strong correlations were not observed between eroded particle 

size and shear stress, FICS videos across all shear stresses were compiled 

for each material, and corresponding grain size distributions were

generated (Figures 11-14). The abundance of millimeter sized particles 

observed by the FICS is in contrast to the LDPSA size distributions 

obtained from the disaggregated bed samples. The compiled FICS 

distributions for each material showed median size to range from 

approximately 200 μm to 4400 μm, and in all but two cases (ARE60 and 

MS003), D50F values were greater than 1000 μm (Table 3). A comparison 

of the median particle sizes obtained via FICS and LDPSA (D50F/D50L) 

revealed that the median size of eroded particles were 1.4-450x larger than 

median sizes of the disaggregated test beds (Table 3). In all but two 

instances (ARE60 and MS003), the D50F/D50L values in this study were 

≥10. This fact further supports the visual observations made during 

Sedflume testing; erosion of the sediment beds predominately occurred in 

the form of aggregated clasts. 
ERDC/CHL TR-20-19 22

Figure 11. FICS distribution plots of Great Lakes Region samples from Duluth and 

Ashtabula Harbors. 

Figure 12. FICS distribution plots of Mid Atlantic Coast samples from Seven Mile 

Island and James River. 
ERDC/CHL TR-20-19 23

Figure 13. FICS distribution plots of Mississippi River sediment mixtures. 

Figure 14. FICS distribution plots of Gulf Coast Samples from Gulfport, Calcasieu Ship 

Channel, and Houston Ship Channel. 

3.2.2 Regression analyses

To evaluate if physical parameters of the test sediments could be used as 

predictors of mud aggregate production and size, correlations between 

D50F/D50L and sediment physical properties were assessed. Robust linear 

regression models were determined to be statistically significant if pvalues were ≤0.05. Statistically significant correlations were identified 

between D50F/D50L and clay content, sand content, w, and ρ (Table 4).

Regression results showed that, of these parameters, clay content had the 
ERDC/CHL TR-20-19 24

highest correlation to D50F/D50L with R2 = 0.82 and p<0.001 (Figure 15 

and Table 4). No statistically significant correlation was found in models 

that plotted D50F/D50L against PI, φsm, or LOI (Table 4). Plots of these

regression models are presented in Appendix A.

Table 4. D50F/D50L Robust liner regression model outputs. 

Red font indicates variable p-values >0.05 and not 

statistically significant to the model. 

Physical Parameter R2 p-value(s) 

%Clay 0.82 2.2x10-5

%Sand 0.63 0.001

w 0.63 0.001

ρ 0.48 0.009

PI 0.40 0.07

φsm 0.10 0.30

LOI 0.07 0.49

Figure 15. Median particle size ratios plotted against clay content. Solid line presents 

the robust linear regression best fit, with R2 and p values provided. The dashed lines 

present the 95% confidence intervals of the fit. 

Numerous multivariate linear regression models were also run that 

examined correlations between D50F/D50L and multiple physical

parameters (Appendix A). It should be noted that because an inverse 

relationship exists between w and ρ, as seen in equation 2, regression 

models with both these parameters were not run. Results of the multi-
ERDC/CHL TR-20-19 25

variable linear regressions showed that only the model using PI and φsm

showed statistically significant correlation to D50F/D50L, as indicated by the 

asterisk in Table 5. However, with an R2 value of 0.7, this regression model 

does not account for as much variance in the D50F/D50L data as clay content

alone. Further, materials that displayed no plastic behavior such as MS021 

and MS011 both had D50F/D50L values ≥10 (Tables 2 and 3), suggesting 

that aggregated clasts were produced during erosion. Therefore, PI may 

not be the most appropriate parameter to use in predicting aggregate 

production and size. 

Table 5. D50F/D50L Multiple liner regression model outputs presented in ascending R2 

values. * indicates model with all p-values ≤0.05. Red font indicates variable pvalues >0.05 and not statistically significant to the models. 

Parameters R2 p-value(s) 

PI, φsm 0.70 0.01, 0.05 *

%Clay, %Sand, PI, LOI 0.82 0.20, 0.07, 0.31, 0.29

%Clay, %Sand, PI, φsm, LOI 0.82 0.41, 0.54, 0.61, 0.90, 0.41

w, PI, φsm 0.85 0.09, 0.97, 0.01

%Clay, %Sand, w, PI, LOI 0.85 0.43, 0.13, 0.59, 0.94, 0.63

%Clay, w, PI, φsm 0.86 0.99, 0.14, 0.97, 0.03

%Sand, w, PI, φsm 0.89 0.36, 0.08, 0.55, 0.04

w, φsm, LOI 0.91 0.001, 0.003, 0.48

%Sand, ρ, PI, φsm, LOI 0.94 0.97, 0.12, 0.92, 0.13, 0.59

ρ, φsm, LOI 0.95 0.001, 0.004, 0.40

ρ, PI, φsm, LOI 0.95 0.04, 0.86, 0.01, 0.31

%Clay, %Sand, w, φsm, LOI 0.95 0.88, 0.61, 0.03, 0.06, 0.98

%Clay, %Sand, ρ, PI, LOI 0.95 0.73, 0.04, 0.13, 0.84, 0.99

%Clay, w, PI, φsm, LOI 0.95 0.96, 0.12, 0.97, 0.04, 0.94

%Clay, ρ, PI, φsm, LOI 0.95 0.73, 0.13, 0.84, 0.04, 0.99

%Clay, %Sand, ρ, PI, φsm, LOI 0.95 0.71, 0.75, 0.37, 0.97, 0.49, 0.87

%Clay, %Sand, w, PI, φsm 0.96 0.12, 0.08, 0.03, 0.15, 0.02

w, PI, φsm, LOI 0.96 0.03, 0.83, 0.01, 0.92

%Sand, w, PI, φsm, LOI 0.96 0.74, 0.09, 0.71, 0.11, 0.82

%Clay, %Sand, w, PI, φsm, LOI 0.99 0.32, 0.31, 0.15, 0.84, 0.17, 0.36
ERDC/CHL TR-20-19 26

While several other multivariable regression models accounted for as 

much or more variance as clay (R2≥ 0.82), none of them had associated pvalues for each variable that was ≤0.05 (Table 5). This indicated that not 

all the properties were statistically significant to the model at a 95% 

confidence level, and therefore these results were not considered to be 

significant improvements over the single variable regression models. 

3.3 Durability

Tumbling testing showed that the durability of the aggregates varied 

considerably across the test materials, as shown in Figures 16-19. MS003 

was found to be the least durable with no detectable aggregated mass after 

2.5 minutes of testing, thus, it was not plotted in Figure 18. MS011 and 

ARE60 also broke apart quickly and had no detectable mass within the 

tumbler basket following 5 min of testing. Because of this, no trend lines 

are plotted for these samples in Figures 16 and 18. The remaining ten 

samples all showed break-up rates that closely followed an exponential 

model with R2 > 0.88 and p-values <0.02 (Table 6). In these models, the 

aggregated mass fraction >250 μm is related to tumbling time (t) through 

the abrasion rate (δ) shown in equation 4: 

250

t F Ae Ma

−δ

> = (4)

While aggregate abrasion closely matched an exponential model, the 

deterioration rate varied substantially, as seen in δ in Table 6. Only seven

of the test samples had measurable aggregated mass in the tumbler after 

20 min. Of these, only four had FMa>250 greater than 0.1 (MS029, JR, GP, 

and HSC; Figures 16-18). Aggregates from MS029 material were by far the 

most robust with FMa >250 = 0.44 after 20 min of tumbling (Figure 17) and a 

δ of 0.042 (Table 6).
ERDC/CHL TR-20-19 27

Figure 16. Aggregate durability plots of Great Lakes Region samples from Duluth and 

Ashtabula Harbors. Dotted lines indicate exponential fits with associated R2 values. 

Figure 17. Aggregate durability plots of Mid Atlantic Coast samples from Seven Mile Island and 

James River. Dotted lines indicate exponential fits with associated R2 values. 
ERDC/CHL TR-20-19 28

Figure 18. Aggregate durability plots of Mississippi River sediment mixtures. Dotted 

lines indicate exponential fits with associated R2 values. 

Figure 19. Aggregate durability plots of Gulf Coast samples from Gulfport, Calcasieu 

Ship Channel, and Houston Ship Channel. Dotted lines indicate exponential fits with 

associated R2 values. 
ERDC/CHL TR-20-19 29

Table 6. Aggregate durability tumbling results. Water content of tumbled aggregates 

(w) and fraction >250 μm from wet sieve tests are presented along with tumbling 

abrasion rate (δ). * indicates samples with reduced w values for aggregate testing. 

Sample 

Name w 

Wet Sieve 

(>250μm) δ 

DH 0.74 0.05 -0.195

ARE45 0.68* 0.01 -0.229

ARE60 0.25* 0.07 NA

SMIIL 1.02 0.04 -0.206

JR 1.20 0.03 -0.084

MS100 0.99 ND -0.217

MS029 0.19 0.01 -0.042

MS021 0.18 0.02 -0.129

MS011 0.13 0.02 NA

MS003 0.19 0.02 NA

GP 1.96 ND -0.093

CSC 1.27 0.14 -0.231

HSC 0.85* ND -0.107

While aggregate size was found to be strongly correlated with clay content, 

durability of aggregates was not. Figure 20 presents δ from the tumbling 

exponential decay models plotted against clay content. A statistically 

significant trend between these properties was not identified with R2

=0.03 and p=0.63. Abrasion rates were similarly plotted against the other 

tested physical properties of the sediments and results of robust linear 

regression models are presented in Table 7. Only LOI was found to have a 

statistically significant correlation with δ, as seen in Figure 21. Despite the

strong correlation shown in Figure 21, it is important to point out that 

MS029 and MS021 are not included in the model due to the absence of 

LOI data. However, inferences about the LOI of MS029 and MS021 can be 

made from the LOI of MS100. Since MS029 and MS021 were mixtures of 

laboratory test sand and naturally occurring sediment from the Mississippi 

River (MS100), nearly all organic material in the sediments would be 

expected to be associated with MS100. Therefore, it can estimated that the 

LOI% of these samples would be on the order of 29% and 21% of the 

MS100 LOI value. If accurate, this would result in LOI% values of 1.0 and 

0.7 for MS029 and MS021, respectively. When plotted, these data group in 

the upper left corner of Figure 21, and remove statistical significance of the 
ERDC/CHL TR-20-19 30

regression model (R2 =0.08, p=0.41). Regression plots between aggregate 

abrasion and the other physical parameters are presented in Appendix B. 

Figure 20. Aggregate abrasion rate (δ) plotted against clay content. Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. 

Table 7. Aggregate abrasion rate robust liner regression model outputs. Red font 

indicates variable p-values >0.05 and not statistically significant to the model. 

Physical Parameter R2 p-value 

%Clay 0.03 0.63

%Sand 0.02 0.37

w <0.001 0.99

ρ 0.07 0.45

PI 0.04 0.53

φsm 0.06 0.50

LOI 0.89, 0.08* 5.8x10-4 , 0.41*

* Indicates the inclusion of MS029 and MS021 data.
ERDC/CHL TR-20-19 31

Figure 21. Aggregate abrasion rate (δ) plotted against LOI. Solid line presents the 

robust linear regression best fit, with R2 and p values provided. The dashed lines 

present the 95% confidence intervals of the fit. Red circles indicate estimated 

MS029 and MS021 data points. 

As with aggregate size, combinations of multivariate linear regression 

models were also run with the tested parameters. However, none of the 

multivariate regression models were found to be statistically significant.

Tables of these regression model outputs can be found in Appendix B.
ERDC/CHL TR-20-19 32

4 Summary, Conclusions and 

Recommendations

4.1 Summary and conclusions

To evaluate if commonly measured physical properties of sediment (grain 

size, density, water content, plasticity, volume fraction mud, and organic 

content) could be used to predict the production, size, and durability of 

muddy bed aggregates during erosion, a series of 13 sediments from 

USACE project locations across the country were examined. These 

sediments showed a wide range in physical parameters that have 

previously been linked to sediment cohesion (e.g. clay, organic content, w, 

plasticity, density, etc.), as shown in Table 2. Erosion testing indicated that 

the majority of the eroded volume occurred in the form of aggregated 

clasts >1000 μm for most sediment types evaluated (Figures 9-11; Table

3). Median grain size of eroded particles was found to be 10-450x larger 

than the median size of disaggregated samples for eleven of the test 

sediments (Table 3). Similar results for D50F/D50L ratios were observed in 

field erosion test studies conducted within the James River Estuary, where 

eroded sediment from muddy cores was 50-270x the median size of the 

disaggregated test bed (Perkey et al. 2020). 

The D50F/D50L was found to have a strong positive correlation with clay

content (Figure 15), and that aggregated clasts 10x the size of the 

disaggregated bed could be observed in sediments with clay contents as

low as 2% (Table 3). Previously conducted studies have demonstrated that 

small amounts of clay (3-5%) can produce cohesive behavior and alter 

erosion processes of sediment (van Ledden 2004; Wu et al. 2018). Thus,

the observation of aggregated bed clasts in low clay content sediment 

mixtures in this study aligns with previous work. Further, the data 

generated in this study suggest that aggregated mud clasts should be 

anticipated to occur as a result of bed erosion in sediment beds with clay 

contents adequate to produce cohesive behavior.

While not as strong as the relationship to clay content, significant 

correlations were also found between D50F/D50L and sand content, water 

content, and bulk density (Table 4). Weak, but significant correlations to 

these parameters were also identified in field cores collected throughout 

the James River Estuary (Perkey et al. 2020). Additionally, limited 

laboratory studies (e.g. Smith 1972; Jepsen et al. 2010) have documented 
ERDC/CHL TR-20-19 33

results that indicate both aggregate size and durability can be influenced 

by sand and water content. The Jepsen et al. (2010) study evaluated 

sediments from Boston Harbor, MA, Savannah Harbor, GA, and the 

Canaveral open water dredged material disposal area, FL, with an erosion 

flume and bedload trap. Their results indicated that the presence of 

muddy aggregates in the bedload trap decreased as sand content of the test 

sediments increased. Smith (1972) evaluated mud aggregates composed of 

three different sand mud mixtures with varying mineralogies and water 

contents. His results indicated that aggregates prepared from sediments

with higher sand and lower water contents were less durable and resulted 

in smaller aggregated clasts after transport in flume testing. While these 

observations might be intuitive, it is important that the consistency of 

these trends be documented for the variety of test sediments that have 

been evaluated.

Though previous research has linked plasticity, organic content, and φsm

with the cohesive nature of sediments (e.g. Jacobs et al. 2011; Dickhudt et 

al. 2009, 2011; Grant and Gust 1987; Yallop et al. 1994), these properties 

did not show significant correlation to the production and relative size of 

aggregated clasts observed in this study (Table 4). A multivariate 

regression model involving both PI and φsm was found to show significant 

correlation with D50F/D50L ratios, but this model was not as strong as the 

relationship observed with clay content alone. It is also important to point 

out that aggregated clasts were observed in sediments that did not display 

plastic behavior. Thus, using this property as a predictor of aggregate 

production and size is not recommended over clay content.

Durability testing of the aggregate cubes showed that abrasion of the clasts 

occurred rapidly and at rates that were best explained by exponential 

decay models. Only four of the test materials showed Fma>250 greater than 

0.10 following 20 min of tumbling (~200 m of linear distance). Despite the 

rapid rate of abrasion, these findings align with Sternberg’s Law

(Sternberg 1875), which relates pebble size to downstream transport 

distance through

Lt D Deo−δ = (5)

in which Do is the particle diameter before transport, Lt is the length of 

travel, and δ is the abrasion rate. This suggests that commonly used 
ERDC/CHL TR-20-19 34

practices used to estimate the fining and break-up of gravel in streams 

may also be useful in estimating durability of eroded mud clasts. 

Unlike bed aggregate size, aggregate abrasion rates were not found to 

correlate with contents of clay, sand, or water. Instead, a limited 

correlation with organic content through LOI measurements was 

identified (Table 7). This trend was only valid in muddy sediments and did 

not align with approximated LOI vales when plotted against abrasion rates 

from MS029 and MS021. It should be pointed out that the clay and 

organic content testing performed in this study did not provide 

information about the variety of clays or organic compounds present in the 

sediment. The impact these parameters have on sediment cohesion has 

been shown to vary with the specific nature of the clays (e.g. Mitchner and 

Torfs 1996; Barry et al 2006; Jacobs et al. 2011) or organic material (Grant 

and Gust 1987; Yallop et al. 1994). Thus, testing that evaluates the nature 

of the clays and organics present may be of more value than tests that 

simply measure abundance. Abundance testing was performed in this 

study due to the fact that these types of evaluations are more commonly 

used in characterizing sediments due to their lower cost.

It is important that the presence and transport characteristics of muddy

bed aggregates be accounted for in models where transport dynamics are 

being simulated for cohesive sediments. Recent studies on muddy bed 

aggregates within the James River (Perkey and Smith 2019; Perkey et al. 

2020) have highlighted the potential impact that aggregated clasts have on 

the transport pathways of fine sediment. Due to their size, eroded bed 

aggregates were predicted to have reduced time in a mobile state when 

compared to their constituent particles. Further, when bed aggregates 

were mobile, they were largely limited to bedload or insipient suspension, 

in contrast to smaller primary particles that were fully suspended within 

the water column. These same trends would be expected for the bed 

aggregates observed in this study, and thus impact the sediment transport 

processes within their respective environments.

The consistent observation of coarse-sand to gravel sized (>1000 μm) mud 

aggregates that are 10’s-100’sx larger than their component particles based 

upon clay content provides guidance for size ranges of aggregated clasts 

produced following erosion of cohesive beds. Additionally, while tumbling 

testing performed in this study is not a direct simulation of bedload 

transport, it is reasonable to infer that the coarse mud aggregates would 
ERDC/CHL TR-20-19 35

likely degrade to smaller sized clasts during bedload transport, following 

exponential abrasion rates observed in this study. These trends may assist 

numerical modelers in developing sediment classifications representative 

of muddy bed aggregates for inclusion into numerical models as well as 

developing algorithms to describe their break-up and mass transfer to 

other sediment classes.

4.2 Recommendations

• Due to their abundant presence and impact on fine sediment transport 

pathways, it is recommended that numerical modeling in cohesive 

sediment environments incorporate particle classes that represent 

muddy bed aggregates.

• To incorporate these classes into model simulations, clay content in 

conjunction with the median disaggregated grain size could be used to 

estimate resultant bed aggregate sizes.

• While algorithms to address the weathering and breakdown of bed 

aggregates during transport do not currently exist within numerical 

model frameworks, in simulations where transport of sediment from a 

specified location is of focus (i.e. simulations evaluating dredge 

material placement areas) transport limits of bed aggregate classes 

could be set based upon break-up rates from tumbling tests. 

• To develop algorithms that would represent the breakdown rate of 

muddy aggregates, further testing is needed to evaluate the durability 

and degradation processes of muddy bed aggregates in a more natural 

transport setting.
ERDC/CHL TR-20-19 36

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ERDC/CHL TR-20-19 39

Appendix A: D50F/D50L Linear Regression 

Model Results

This appendix presents plots and tables of the robust linear regression 

models between D50F/D50L and the physical parameters evaluated in this 

study. Plots of the single parameter regressions found to be statistically 

significant are presented first in black, followed by models with p-values 

>0.05, presented in red. Multi-parameter regression model outputs are 

then presented in a series of tables, corresponding to the number of 

parameters used in the regression.

Figure A1. Median particle size ratios plotted against clay content. Solid line presents 

the robust linear regression best fit, with R2 and p values provided. The dashed lines 

present the 95% confidence intervals of the fit. 
ERDC/CHL TR-20-19 40

Figure A2. Median particle size ratios plotted against sand content. Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. 

Figure A3. Median particle size ratios plotted against water content. Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. 
ERDC/CHL TR-20-19 41

Figure A4. Median particle size ratios plotted against bulk density (ρ). Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. 

Figure A5. Median particle size ratios plotted against Plasticity Index (PI). Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. Fit parameters are 

shown in red due to model p-values >0.05. 
ERDC/CHL TR-20-19 42

Figure A6. Median particle size ratios plotted against solid volume faction mud (φsm). 

Solid line presents the robust linear regression best fit, with R2 and p values 

provided. The dashed lines present the 95% confidence intervals of the fit. Fit 

parameters are shown in red due to model p-values >0.05. 

Figure A7. Median particle size ratios plotted against LOI. Solid line presents the 

robust linear regression best fit, with R2 and p values provided. The dashed lines 

present the 95% confidence intervals of the fit. Fit parameters are shown in red due 

to model p-values >0.05. 
ERDC/CHL TR-20-19 43

Table A1. D50F/D50L multiple linear regression model outputs (two parameters). Red 

font indicates variable p-values >0.05 and not statistically significant to the models. 

Parameters R2 p-values Parameter(s) R2 p-values 

%Clay, %Sand 0.63 0.04, 0.95 %Sand, LOI 0.59 0.03, 0.89

%Clay, w 0.64 0.24, 0.04 w, PI 0.39 0.93, 0.56

%Clay, ρ 0.48 0.27, 0.23 w, φsm 0.62 0.003, 0.25

%Clay, PI 0.48 0.46, 0.15 w, LOI 0.37 0.14, 0.92

%Clay, φsm 0.47 0.02, 0.62 ρ, PI 0.41 0.27, 0.87

%Clay, LOI 0.44 0.09, 0.21 ρ, φsm 0.47 0.02, 0.75

%Sand, w 0.70 0.002, 0.08 ρ, LOI 0.31 0.20, 0.93

%Sand, ρ 0.71 0.013, 0.26 PI, φsm 0.7 0.01, 0.05

%Sand, PI 0.59 0.14, 0.72 PI, LOI 0.32 0.18, 0.64

%Sand, φsm 0.64 0.002, 0.89 φsm, LOI 0.08 0.82, 0.50

Table A2. D50F/D50L multiple linear regression model outputs (three parameters). Red 

font indicates variable p-values >0.05 and not statistically significant to the models. 

Parameters R2 p-values Parameter(s) R2 p-values 

%Clay, %Sand, w 0.68 0.91, 0.17, 0.64 %Sand, w, φsm 0.69 0.17, 0.60, 0.92

%Clay, %Sand, ρ 0.71 0.58, 0.05, 0.35 %Sand, w, LOI 0.62 0.13, 0.56, 0.90

%Clay, %Sand, PI 0.62 0.68, 0.20, 0.83 %Sand, ρ, PI 0.69 0.11, 0.35, 0.30

%Clay, %Sand, φsm 0.68 0.79, 0.03, 0.80 %Sand, ρ, φsm 0.70 0.02, 0.29, 0.90

%Clay, %Sand, LOI 0.65 0.41, 0.15, 0.50 %Sand, ρ, LOI 0.60 0.12, 0.81, 0.83

%Clay, w, PI 0.46 0.51, 0.89, 0.69 %Sand, PI, φsm 0.71 0.92, 0.24, 0.24

%Clay, w, φsm 0.65 0.41, 0.04, 0.41 %Sand, PI, LOI 0.69 0.12, 0.79, 0.68

%Clay, w, LOI 0.49 0.34, 0.58, 0.60 %Sand, φsm, LOI 0.63 0.04, 0.53, 0.71

%Clay, ρ, PI 0.48 0.32, 0.94, 0.46 w, PI, φsm 0.85 0.09, 0.97, 0.01

%Clay, ρ, φsm 0.53 0.31, 0.20, 0.81 w, PI, LOI 0.31 0.99, 0.58, 0.69

%Clay, ρ, LOI 0.77 0.17, 0.20, 0.05 w, φsm, LOI 0.91 0.001, 0.003, 0.48

%Clay, PI, φsm 0.71 0.89, 0.04, 0.09 ρ, PI, φsm 0.72 0.82, 0.08, 0.07

%Clay, PI, LOI 0.37 0.64, 0.77, 0.79 ρ, PI, LOI 0.38 0.58, 0.29, 0.57

%Clay, φsm, LOI 0.53 0.08, 0.34, 0.15 ρ, φsm, LOI 0.95 0.001, 0.004, 0.40

%Sand, w, PI 0.63 0.15, 0.62, 0.77 PI, φsm, LOI 0.73 0.04, 0.07, 0.67
ERDC/CHL TR-20-19 44

Table A3. D50F/D50L multiple linear regression model outputs 

(four variables). Red font indicates variable p-values >0.05 

and not statistically significant to the models. 

Parameters R2 p-values 

%Clay, %Sand, w, PI 0.65 0.65, 0.21, 0.61, 0.71

%Clay, %Sand, w, φsm 0.70 0.91, 0.31, 0.63, 0.95

%Clay, %Sand, w, LOI 0.77 0.18, 0.09, 0.22, 0.20

%Clay, %Sand, ρ, PI 0.67 0.62, 0.18, 0.46, 0.52

%Clay, %Sand, ρ, φsm 0.73 0.52, 0.06, 0.33, 0.62

%Clay, %Sand, ρ, LOI 0.65 0.48, 0.20, 0.82, 0.57

%Clay, %Sand, PI, φsm 0.73 0.75, 0.75, 0.30, 0.31

%Clay, %Sand, PI, LOI 0.82 0.20, 0.07, 0.31, 0.29

%Clay, w, PI, φsm 0.86 0.99, 0.14, 0.97, 0.03

%Clay, w, PI, LOI 0.39 0.58, 0.69, 0.80, 0.66

%Clay, ρ, PI, φsm 0.72 0.88, 0.82, 0.13, 0.13

%Clay, ρ, PI, LOI 0.37 0.89, 0.80, 0.74, 0.98

%Sand, w, PI, φsm 0.89 0.36, 0.08, 0.55, 0.04

%Sand, w, PI, LOI 0.78 0.09, 0.29, 0.35, 0.37

%Sand, ρ, PI, φsm 0.71 0.95, 0.83, 0.30, 0.42 

%Sand, ρ, PI, LOI 0.75 0.12, 0.36, 0.40, 0.33

%Sand, PI, φsm, LOI 0.72 0.94, 0.47, 0.46, 0.79

w, PI, φsm, LOI 0.96 0.03, 0.83, 0.01, 0.92

ρ, PI, φsm, LOI 0.95 0.04, 0.86, 0.01, 0.31
ERDC/CHL TR-20-19 45

Table A4. D50F/D50L multiple linear regression model outputs (five 

parameters). Red font indicates variable p-values >0.05 and not 

statistically significant to the models. 

Parameters R2 p-values 

%Clay, %Sand, w, PI, φsm 0.96 0.12, 0.08, 0.03, 0.15, 0.02

%Clay, %Sand, w, PI, LOI 0.85 0.43, 0.13, 0.59, 0.94, 0.63

%Clay, %Sand, w, φsm, LOI 0.95 0.88, 0.61, 0.03, 0.06, 0.98

%Clay, %Sand, ρ, PI, φsm 0.72 0.82, 0.85, 0.97, 0.45, 0.56

%Clay, %Sand, ρ, PI, LOI 0.95 0.73, 0.04, 0.13, 0.84, 0.99

%Clay, %Sand, ρ, φsm, LOI 0.73 0.94, 0.94, 0.49, 0.51, 0.91

%Clay, %Sand, PI, φsm, LOI 0.82 0.41, 0.54, 0.61, 0.90, 0.41

%Clay, w, PI, φsm, LOI 0.95 0.96, 0.12, 0.97, 0.04, 0.94

%Clay, ρ, PI, φsm, LOI 0.95 0.73, 0.13, 0.84, 0.04, 0.99

%Sand, w, PI, φsm, LOI 0.96 0.74, 0.09, 0.71, 0.11, 0.82

%Sand, ρ, PI, φsm, LOI 0.94 0.97, 0.12, 0.92, 0.13, 0.59

Table A5. D50F/D50L multiple linear regression model outputs (six 

parameters). Red font indicates variable p-values >0.05 and not 

statistically significant to the models. 

Parameters R2 p-values 

%Clay, %Sand, w, PI, φsm, LOI 0.99 0.32, 0.31, 0.15, 0.84, 0.17, 0.36

%Clay, %Sand, ρ, PI, φsm, LOI 0.95 0.71, 0.75, 0.37, 0.97, 0.49, 0.87
ERDC/CHL TR-20-19 46

Appendix B: Aggregate Abrasion Linear 

Regression Model Results

This appendix presents plots and tables of the robust linear regression 

models between aggregate abrasion and the physical parameters evaluated 

in this study. Plots of the single parameter regressions are presented first, 

multi-parameter regression model outputs are then presented in a series 

of tables, corresponding to the number of parameters used in the 

regression.

Figure B1. Aggregate abrasion rate (δ) plotted against sand content. Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. 
ERDC/CHL TR-20-19 47

Figure B2. Aggregate abrasion rate (δ) plotted against water content (w). Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. 

Figure B3. Aggregate abrasion rate (δ) plotted against bulk density (ρ). Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. 
ERDC/CHL TR-20-19 48

Figure B4. Aggregate abrasion rate (δ) plotted against plasticity index (PI). Solid line 

presents the robust linear regression best fit, with R2 and p values provided. The 

dashed lines present the 95% confidence intervals of the fit. 

Figure B5. Aggregate abrasion rate (δ) plotted against solid volume fraction mud 

(φsm). Solid line presents the robust linear regression best fit, with R2 and p values 

provided. The dashed lines present the 95% confidence intervals of the fit. 
ERDC/CHL TR-20-19 49

Table B1 Abrasion rate multiple linear regression model outputs (two parameters). 

Red font indicates variable p-values >0.05 and not statistically significant to the 

models. 

Parameters R2 p-values Parameter(s) R2 p-values 

%Clay, %Sand 0.19 0.26, 0.27 %Sand, LOI 0.39 0.62, 0.14

%Clay, w 0.04 0.63, 0.80 w, PI 0.02 0.89, 0.81

%Clay, ρ 0.21 0.31, 0.24 w, φsm 0.06 0.77, 0.51

%Clay, PI 0.08 0.61, 0.88 w, LOI 0.31 0.81, 0.20

%Clay, φsm 0.10 0.59, 0.49 ρ, PI 0.50 0.05, 0.07

%Clay, LOI 0.69 0.02, 0.06 ρ, φsm 0.15 0.39, 0.44

%Sand, w 0.04 0.61, 0.70 ρ, LOI 0.32 0.88, 0.19

%Sand, ρ 0.12 0.59, 0.41 PI, φsm 0.05 0.90, 0.70

%Sand, PI 0.06 0.71, 0.58 PI, LOI 0.48 0.49, 0.08

%Sand, φsm 0.06 0.82, 0.58 φsm, LOI 0.08 0.96, 0.54

Table B2. Abrasion rate multiple linear regression model outputs (three parameters). Red font 

indicates variable p-values >0.05 and not statistically significant to the models. 

Parameters R2 p-values Parameter(s) R2 p-values 

%Clay, %Sand, w 0.26 0.24, 0.23, 0.52 %Sand, w, LOI 0.54 0.29, 0.35, 0.98

%Clay, %Sand, ρ 0.23 0.39, 0.80,0.64 %Sand, ρ, PI 0.50 0.90, 0.89, 0.11

%Clay, %Sand, PI 0.32 0.23, 0.21, 0.35 %Sand, ρ, φsm 0.46 0.12, 0.08, 0.10

%Clay, %Sand, φsm 0.23 0.30, 0.33, 0.62 %Sand, ρ, LOI 0.56 0.23, 0.31, 0.89

%Clay, %Sand, LOI 0.68 0.15, 0.62, 0.14 %Sand, PI, φsm 0.06 0.81, 0.82, 0.99

%Clay, w, PI 0.05 0.68, 0.93, 0.92 %Sand, PI, LOI 0.54 0.58, 0.34, 0.79

%Clay, w, φsm 0.16 0.43, 0.47, 0.37 %Sand, φsm, LOI 0.60 0.09, 0.25, 0.90

%Clay, w, LOI 0.69 0.11, 0.63, 0.25 w, PI, φsm 0.07 0.73, 0.75, 0.61

%Clay, ρ, PI 0.60 0.33, 0.05, 0.09 w, PI, LOI 0.50 0.89, 0.36, 0.52

%Clay, ρ, φsm 0.37 0.21, 0.15, 0.27 w, φsm, LOI 0.38 0.24, 0.69, 0.80

%Clay, ρ, LOI 0.68 0.11, 0.69, 0.22 ρ, PI, φsm 0.52 0.08, 0.13, 0.66

%Clay, φsm, LOI 0.67 0.05, 0.71, 0.15 ρ, PI, LOI 0.49 0.85, 0.33, 0.52

%Sand, w, PI 0.07 0.62,0.82,0.67 ρ, φsm, LOI 0.40 0.22, 0.53, 0.98

%Sand, w, φsm 0.06 0.91, 0.84, 0.70 PI, φsm, LOI 0.50 0.14, 0.80, 0.54
ERDC/CHL TR-20-19 50

Table B3. Abrasion rate multiple linear regression 

model outputs (four parameters). Red font indicates 

variable p-values >0.05 and not statistically 

significant to the models. 

Parameters R2 p-values 

%Clay, %Sand, w, PI 0.35 0.27, 0.25, 0.70, 0.48

%Clay, %Sand, w, φsm 0.26 0.30, 0.46, 0.65, 0.79

%Clay, %Sand, w, LOI 0.70 0.30, 0.70, 0.75, 0.34

%Clay, %Sand, ρ, PI 0.68 0.22, 0.40, 0.10, 0.08

%Clay, %Sand, ρ, φsm 0.47 0.80, 0.41, 0.20, 0.20

%Clay, %Sand, ρ, LOI 0.68 0.36, 0.75, 0.86, 0.37

%Clay, %Sand, PI, φsm 0.48 0.15, 0.19, 0.25, 0.36

%Clay, %Sand, PI, LOI 0.68 0.34, 0.66, 0.97, 0.42

%Clay, w, PI, φsm 0.17 0.54, 0.69, 0.84, 0.50

%Clay, w, PI, LOI 0.75 0.19, 0.40, 0.47, 0.28

%Clay, ρ, PI, φsm 0.68 0.24, 0.06, 0.19, 0.39

%Clay, ρ, PI, LOI 0.72 0.23, 0.48, 0.55, 0.32

%Sand, w, PI, φsm 0.07 0.91, 0.80, 0.77, 0.88

%Sand, w, PI, LOI 0.54 0.63, 0.89, 0.80, 0.88

%Sand, ρ, PI, φsm 0.65 0.30, 0.06, 0.78, 0.27

%Sand, ρ, PI, LOI 0.55 0.60, 0.77, 0.99, 0.96

%Sand, PI, φsm, LOI 0.60 0.47, 0.79, 0.56, 0.79

w, PI, φsm, LOI 0.50 0.99, 0.48, 0.85, 0.61

ρ, PI, φsm, LOI 0.50 0.98, 0.53, 0.88, 0.68
ERDC/CHL TR-20-19 51

Table B4. Abrasion rate multiple linear regression model outputs 

(five parameters). Red font indicates variable p-values >0.05 and 

not statistically significant to the models. 

Parameters R2 p-values 

%Clay, %Sand, w, PI, φsm 0.48 0.23, 0.28, 0.85, 0.35, 0.46

%Clay, %Sand, w, PI, LOI 0.77 0.30, 0.64, 0.48, 0.51, 0.34

%Clay, %Sand, w, φsm, LOI 0.87 0.18, 0.24, 0.24, 0.26, 0.18

%Clay, %Sand, ρ, PI, φsm 0.68 0.65, 0.98, 0.27, 0.62, 0.81

%Clay, %Sand, ρ, PI, LOI 0.67 0.56, 0.95, 0.93, 0.93, 0.55

%Clay, %Sand, ρ, φsm, LOI 0.70 0.48, 0.70, 0.74, 0.76, 0.48

%Clay, %Sand, PI, φsm, LOI 0.67 0.56, 0.95, 0.93, 0.93, 0.55

%Clay, w, PI, φsm, LOI 0.75 0.30, 0.57, 0.54, 0.81, 0.38

%Clay, ρ, PI, φsm, LOI 0.71 0.36, 0.69, 0.66, 0.98, 0.45

%Sand, w, PI, φsm, LOI 0.60 0.57, 0.98, 0.86, 0.66, 0.84

%Sand, ρ, PI, φsm, LOI 0.59 0.57, 0.94, 0.83, 0.69, 0.83

Table B5. Abrasion rate multiple linear regression model outputs (six 

parameters). Red font indicates variable p-values >0.05 and not 

statistically significant to the models. 

Parameters R2 p-values 

%Clay, %Sand, w, PI, φsm, LOI 0.91 0.31, 0.41, 0.36, 0.60, 0.44, 0.31

%Clay, %Sand, ρ, PI, φsm, LOI 0.68 0.66, 0.90, 0.79, 0.86, 0.91, 0.65
ERDC/CHL TR-20-19 52

Unit Conversion Factors

Multiply By To Obtain 

microns 1.0 E-06 meters
ERDC/CHL TR-20-19 53

Acronyms

USACE U.S. Army Corps of Engineers

FICS Flume Imaging Camera System

ERDC-CHL Engineer Research and Development Center-Coastal and 

Hydraulics Laboratory

LRE USACE Detroit District

LRB USACE Buffalo District

NAP USACE Philadelphia District

SMIIL Seven Mile Island Living Laboratory

NAO USACE Norfolk District

MVN USACE New Orleans District

CSC Calcasieu Shipping Channel

RSM Regional sediment management

SWG USACE Galveston District

PI Plasticity Index

LOI loss on ignition

LDPSA Laser Diffraction Particle Size Analysis

esd equivalent spherical diameters

HSC Houston Ship Channel

GP Gulfport entrance channel
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1. REPORT DATE (DD-MM-YYYY)

September 2020

2. REPORT TYPE

Final

3. DATES COVERED (From - To)

4. TITLE AND SUBTITLE

Physical Factors That Influence Muddy Bed Aggregate Production, Size, and Durability

5a. CONTRACT NUMBER

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

David W. Perkey, Kelsey A. Fall, and S. Jarrell Smith

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT

 NUMBER

Coastal and Hydraulics Laboratory

U.S. Army Engineer Research and Development Center

3909 Halls Ferry Road

Vicksburg, MS 39180-6199

ERDC/CHL TR-20-19

9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR’S ACRONYM(S)

The Regional Sediment Management Program (RSM) and the Dredging Operations and 

Environmental Research Program (DOER) Vicksburg, MS 39180 11. SPONSOR/MONITOR’S REPORT 

 NUMBER(S)

12. DISTRIBUTION / AVAILABILITY STATEMENT

Approved for public release; distribution is unlimited.

13. SUPPLEMENTARY NOTES

RSM: “James River Federal Navigation Channel, Lower Reaches” – Account Code D15945 AMSCO 008303

DOER: “Aggregate Transport” – Account Code B3209G AMSCO 089500

14. ABSTRACT

Aggregation state significantly influences the transport characteristics of fine sediments. While research has documented the presence of 

mud aggregates in multiple coastal and estuarine environments, bed aggregates are largely absent from numerical models used to predict 

cohesive sediment transport. The U.S. Army Corps of Engineers (USACE) is conducting studies to evaluate the impact muddy bed 

aggregates have on sediment management issues, and how to account for aggregates in numerical models. 

In this study, physical properties associated with cohesive behavior were evaluated to determine if they could be used as predictors for 

bed aggregate production, size, and durability. Results showed that aggregates were consistently produced in cohesive sediments, and 

that median aggregate size was ~10-450x larger than the disaggregated sediment. Clay content had strong correlation with relative 

aggregate size, though statistically significant correlations were also found with sand content, water content, and density. Durability 

testing indicated that aggregate break-up followed exponential models, and that in limited instances, rates of break-up correlated with 

organic content.

15. SUBJECT TERMS

Sediment transport--Numerical analysis

Cohesive sediments

Suspended sediments

Bed load

Soil structure

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