Federal Aviation 

Administration 

Presented to: REDAC Briefing to Sub-committee 

on Airports 

By: David R. Brill, P.E., Ph.D. 

Date: March 3, 2021 

40-Year Pavement 

Life 

Machine Learning and 

PA40 Data 
Level 

Branch 

-

·----

Data Type/Fi le 

I Chme!e Zena 

Profile Data 

Le,er Images 

Index 

Annual Rain a11 

Mean HJgM.ow Temp&1atures 

Freezln fThilwin De re-a Da Ii 

---------,----~----__,-------,~~e~~ca~l~~g~o~:,-------------------1 

F=--------i..[=~,s~MC::::J 

Section 

SECTION OF RUNWAY OR 

SHOULDER 

FWOFl e 

I Flekl Core Oa!a I 

I As~Buill Structure 

Work HlstOI')' 

lnopectlon Oata}---------i..c=~P~CIC::::J 

 

 

 

Extended Airport Pavement Life 

Database (PA40) 

• Database of construction and 

performance data on 28 

runways at 22 large- and 

medium-hub airports in the U.S. 

• Built on FAA PAVEAIR 3.0 code 

base. 

• Structured like PMS, but with 

additional fields for: 

– Surface friction, profile roughness, 

groove condition, HWD data. 

– Historical runway usage and 

weather data. 

– Structural design & as-built 

section data. 

– Field core test data. 

Federal Aviation 

Administration March 3, 2021 Machine Learning and PA40 Data 2 
Cond1t1on Analysis M&R Reports Maps Tools Extended Life Logout Member Area Help 

Current Database: BWI 

Current Database: BWI 

BWl BWl RW10.. RW10/10 8/15/20 11 05 50000 ft' 48 L &TCR 500 28 120000AM 

BWl BWl RW10.. RW10/10 8/15/2011 05 R 50000 ft' 48 L &TCR M 750 28 12-ooooAM 

BWl BWl RW10.. RW10/10 8/1512011 05 R 50000 ft' 57 WEATHERING M 50000 ft' 28 120000AM 

 

PA40 Recent Updates 

• Runway traffic data updated 

through October 2020. 

• Added GIS map labels for 

pavement data. 

• Added ability to 

search/display distress data 

on the sample unit level. 

• Completed advanced query 

tool enhancements. Now 

displays traffic/weather totals 

between any two dates. 

• New modeling features: 

PCI/SCI/Anti-SCI vs. Age/Traffic 

Federal Aviation 

Administration March 3, 2021 Machine Learning and PA40 Data 3 
 

 

Federal Aviation 

Administration 

Performance Models 

• Serviceability Index (SL) 

– Combination of multiple performance indexes. 

• Structural: SCI, FWD-derived index. 

• Functional: Runway roughness (RRI), friction, “anti-SCI” (nonstructural components of PCI). 

– Predictors may include: Pavement age, traffic, environmental 

cycles. 

• Initial FAA study found that conventional regression 

analysis is inadequate to predict pavement 

performance as a function of multiple predictors. 

• Decision to use machine learning (ML) methods to 

predict SL from multiple predictors. 

March 3, 2021 Machine Learning and PA40 Data 4 
 

 

 

 

Federal Aviation 

Administration 

Machine Learning Goals 

(from BAA ARAP0004, Unsupervised Learning and Database Analysis) 

• Identify the key variables that most influence 

pavement longevity, and eliminate variables that have 

little or no influence. 

• Identify key performance indexes or combinations of 

indexes tied to pavement failure or a decision to 

rehabilitate/reconstruct/replace. 

• Perform clustering, or find trends/correlations in the 

EAPL data that may not be obvious. 

• Develop data-based models for predicting long-term 

pavement condition, employing a mix of inputs such 

as traffic cycles, weather cycles, age, maintenance 

data, and structural or material properties. 

March 3, 2021 Machine Learning and PA40 Data 5 
Flexible Runways Studied 

Airport Runway 

Boston Logan Airport (BOS) 4L-22R 

Columbus International Airport (CMH) 10L-28R; 10R-28L 

Greensboro International Airport (GSO) 5L-23R 

Kansas City International Airport (MCI) 9-27 

LaGuardia Airport (LGA) 4-22 

Miami International Airport (MIA) 12-30 

San Francisco International Airport (SFO) 10R-28L 

Tucson International Airport (TUS) 11L-29R; 3-21 

Federal Aviation 

Administration March 3, 2021 Machine Learning and PA40 Data 6 
 

 

 

 

 

 

Federal Aviation 

Administration 

Feature Selection 

• Use ML to identify the key environmental variables 

that affect runway pavement performance. 

• Problem is to assess a set of candidate predictors 

against a target value (in this case, anti-SCI). 

• There are more independent variables than the 

number of climate conditions in the database. 

• Potential issues: 

– Collinearity. Variables that are highly correlated are redundant 

and can affect the prediction performance negatively. 

– On the other hand, the PA40 database does not cover all 

geographic/climate scenarios. Models may be underspecified, 

or significant features could be wrongly eliminated. 

March 3, 2021 Machine Learning and PA40 Data 7 
 

 

Weather Variables Considered 

Environmental Variables Unit 

Freezing Degree Days (FDD) oF 

Freeze Thaw Cycles (FThC) cycles 

Days Temperature Over 90 ̊F (Temp90) days 

Days Precipitation (DPrec) days 

Total Precipitation (TPrec) inches 

Freeze Precipitation Days (FPD) days 

Hydration Days (HD) days 

Average Daily Temperature (Avg Temp) oF 

Average Daily Temperature Difference (Temp Diff) oF 

RHumidity Avg % 

Avg Wind Speed mph 

Thornthwaite Index % 

Sky Cover oktas 

• Approach 1 – Weather variables are treated as cumulative values from date of 

construction/rehabilitation to date of inspection. 

– Target is measured anti-SCI. 

– Include pavement age and previous measured anti-SCI as predictors (auto-regressive approach) 

• Approach 2 – Weather variables are treated as average values. 

Federal Aviation 

Administration March 3, 2021 Machine Learning and PA40 Data 8 
Feature Selection using Ranking 

Algorithms in Approach 1 

Filter Methods Wrapper Methods 

Pearson SVM RRelieF Linear Regression Correlation (Gaussian Kernel) 

Input R Input Score Input Score Input Score 

Variable Variable Variable Variable 

Previous Previous Previous 0.77 0.040 Age 12.3 5.7 anti-SCI anti-SCI anti-SCI 

Previous Age -0.77 Temp90 0.022 10.5 Age 5.6 anti-SCI 

DPrec -0.63 Age 0.016 Temp90 9.3 DPrec 3.5 

Temp90 -0.59 TPrec 0.010 DPrec 9.1 Temp90 2.9 

TPrec -0.57 HD 0.009 HD 8.0 TPrec 2.8 

HD -0.57 DPrec 0.009 TPrec 7.6 HD 2.7 

FThC -0.54 FDD 0.006 FDD 7.1 FThC 2.4 

FDD -0.50 FThC 0.005 FThC 6.7 FDD 2.0 

Federal Aviation 

Administration March 3, 2021 Machine Learning and PA40 Data 9 
 

Federal Aviation 

Administration 

Modeling Approaches 

• Three modeling approaches were used for initial 

model development: 

– Subset of variables 

– Principal component analysis (PCA) 

– Features from k-means cluster analysis 

• All approaches based on autoregressive random 

forest (RF) learning algorithm. 

• Used “Weka” (freeware) for data analysis and 

model implementation. 

• Used 278 data records from 10 flexible runways to 

train the RF model. 

March 3, 2021 Machine Learning and PA40 Data 10 
100 100 

Training Subset Testing Subset 

90 90 

R2 = 0.89 R2 = 0.90 

u 80 u II) II) 80 I I 

~ ~ C: 70 C: IV 70 IV 

"C 00 "C cu cu ..... 60 0 00 ..... 60 

-~ -~ "C "C cu 50 oO cu ... ... 50 

Q. 0 Q. 0 

40 --Line of equality --Line of equality 40 •••••••• Linear (Fit) •· • • • •· • Linear (Fit) 

30 30 

30 40 50 60 70 80 90 100 30 40 50 60 70 80 90 100 

Actual anti-SCI Actual anti-SCI 

 

 

Principal Component Analysis (PCA) 

• Unsupervised method for reducing dimensionality 

of feature space. 

– Map data onto a set of new uncorrelated variables (the PCs). 

– Unlike original features, PCs possess no physical meaning. 

• Only 4 PCs explain more than 95% of the variance. 

Comparison of predicted and actual anti-SCI - Modeling Approach 2: PCA 

March 3, 2021 Machine Learning and PA40 Data Federal Aviation 

Administration 

11 
 

Performance of Trained RF Model 

• Take-away: Pavement age and previous anti-SCI remain the most 

significant predictors of current anti-SCI. 

• Upcoming research will explore ML methods for predicting other 

components of SL (related to FOD, roughness and low friction). 

Performance 

Measure 

1. Subset of 

Variables 2. PCA 3. Cluster Analysis [No Climate] 

Training Testing Training Testing Training Testing Training Testing 

R2 0.89 0.9 0.89 0.9 0.88 0.93 0.8 0.84 

RMSE 5.16 4.55 5.11 4.81 5.41 3.86 6.8 5.9 

RRSE 9.5% 9.0% 9.0% 7.3% 10.2% 6.3% 13.4% 8.1% 

Accuracy 

(5% error) 69% 68% 74% 67% 69% 68% 67% 68% 

Accuracy 

(10% error) 90% 95% 88% 90% 90% 92% 87% 89% 

Federal Aviation 

Administration March 3, 2021 Machine Learning and PA40 Data 12 
 

 

 

 

Federal Aviation 

Administration 

Technical Products 

• Technical Report (in editing): 

Application of Machine Learning Techniques to 

Pavement Performance Modeling, Sept. 2020 

• Two papers accepted for presentation: 

– BCRRA 2021: “Machine Learning Solutions for 

Development of Performance Deterioration Models 

of Flexible Airfield Pavements” (Conference delayed 

until 2022 due to COVID-19) 

– ASCE T&DI Pavements 2021: “Machine Learning 

Approach to Identifying Key Environmental Factors 

for Airfield Asphalt Pavement Performance” 

March 3, 2021 Machine Learning and PA40 Data 13 
 

 

Federal Aviation 

Administration 

Thank You! 

http://www.airporttech.tc.faa.gov/ 

david.brill@faa.gov 

Acknowledgments: 

FAA Airport Technology R&D Branch: 

Dr. Michel Hovan, Branch Manager; 

Jeff Gagnon, Airport Pavement Section Manager; 

FAA Airport Engineering Division: 

Doug Johnson (retired); Harold Honey 

ARA: 

Dr. Ali Z. Ashtiani; Scott Murrell; Timothy Parsons; Rich Speir 

March 3, 2021 Machine Learning and PA40 Data 14