Data Analysis in Pavement Engineering

Data Analysis in Pavement Engineering: Methodologies and Applications introduces the
theories and methods as well as definitions, principles, and algorithms of data analysis applied
in pavement and transportation infrastructure analysis, tests, maintenance, and operation.
This book provides case studies that demonstrate how these methods can be applied to
solve problems in pavement engineering. Through these real-life examples, readers can gain
a better understanding of how to utilize these data analysis techniques effectively.
Data Analysis in Pavement Engineering: Methodologies and Applications serves as a
reference for engineers or a textbook for graduate and senior undergraduate students in
disciplines related to transportation infrastructure.

Cover image
Title page
Table of Contents
Copyright
Preface
Introduction
Chapter 1 Pavement performance data
Abstract
1.1 Introduction
1.2 Pavement performance indices
1.3 Pavement management system
1.4 Pavement performance models
1.5 The LTPP database
1.6 Data analysis in pavement engineering
Questions
References
Chapter 2 Fundamentals of statistics
Abstract
2.1 Introduction
2.2 Random variables
2.3 Statistical descriptions of data
2.4 Functions of normal distributions
2.5 Statistical inference
2.6 Hypothesis tests
2.7 Case 1: Significance test of concrete strength
Questions
References
Chapter 3 Design of experiments
Abstract
3.1 Introduction
3.2 Design of experiments
3.3 Analysis of variance
3.4 One-way ANOVA
3.5 Two-way ANOVA
3.6 Orthogonal design
3.7 Case 1: Concrete strength test analysis through the two-way ANOVA with interaction
3.8 Case 2: Pavement treatment evaluation through orthogonal design with interaction
Questions
References
Chapter 4 Regression
Abstract
4.1 Introduction
4.2 SLR
4.3 MLR
4.4 Linear regression diagnostics
4.5 Stepwise regression
4.6 Polynomial regression
4.7 Nonlinear regression
4.8 Case study 1: Pavement maintenance effectiveness evaluation
4.9 Case 2: Pavement roughness prediction using linear regression with interaction
4.10 Case 3: Pavement roughness prediction using nonlinear regression
Questions
References
Chapter 5 Logistic regression
Abstract
5.1 Introduction
5.2 Binary logistic regression
5.3 Multinomial logistic regression
5.4 Ordinal logistic regression
5.5 Generalized linear model
5.6 Case: Cracking probability of pavement maintenance treatments
Questions
References
Chapter 6 Count data models
Abstract
6.1 Introduction
6.2 Poisson regression
6.3 Negative binomial model
6.4 Zero-inflated model
6.5 Case: Development of pavement transverse cracks
Questions
References
Chapter 7 Survival analysis
Abstract
7.1 Introduction
7.2 Data censoring
7.3 Survival functions
7.4 Nonparametric model
7.5 Semiparametric models
7.6 Parametric models
7.7 Case: The parametric survival model of pavement patching
Questions
References
Chapter 8 Time series
Abstract
8.1 Introduction
8.2 Definition and decomposition
8.3 Moving average smoothing
8.4 Exponential smoothing
8.5 Parametric models
8.6 Multivariate time series models
8.7 Case: Time series models for pavement roughness prediction
Questions
References
Chapter 9 Stochastic process
Abstract
9.1 Introduction
9.2 Stochastic process
9.3 Markov chain
9.4 Homogeneity
9.5 Stationary distribution
9.6 Finite state Markov chain
9.7 Transition probability matrix based on an ordered probit model
9.8 Case 1: Pavement performance prediction using a homogeneous Markov chain
9.9 Case 2: Pavement performance prediction based on a dynamic Markov chain
Questions
References
Chapter 10 Decision trees and ensemble learning
Abstract
10.1 Introduction
10.2 Decision tree models
10.3 ID3
10.4 C4.5
10.5 CART
10.6 Ensemble learning
10.7 Case: Classifying pavement patching serviceability
Questions
References
Chapter 11 Neural networks
Abstract
11.1 Introduction
11.2 Single-layer neural network
11.3 Two-layer neural network
11.4 Multilayer neural network
11.5 Deep learning
11.6 Convolutional neural network
11.7 Computation of neural network
11.8 Case 1: Pavement roughness prediction
11.9 Case 2: Climatic region classification
Questions
References
Chapter 12 Support vector machine and k-nearest neighbors
Abstract
12.1 Introduction
12.2 Algorithm
12.3 k-nearest neighbors
12.4 Performance metrics of classification models
12.5 Case study: Pavement roughness prediction based on SVM and k-NN
Questions
References
Chapter 13 Principal component analysis
Abstract
13.1 Introduction
13.2 Definition
13.3 Method
13.4 Procedure
13.5 Case: PCA of climatic data
Questions
References
Chapter 14 Factor analysis
Abstract
14.1 Introduction
14.2 Model
14.3 Calculation of factors
14.4 Factor rotation
14.5 Factor score
14.6 Case 1: Climatic data analysis
14.7 Case 2: Pavement performance indicator analysis
Questions
References
Chapter 15 Cluster analysis
Abstract
15.1 Introduction
15.2 Definition
15.3 Distance metrics
15.4 Hierarchical clustering
15.5 k-Means clustering
15.6 Variable clustering
15.7 Case 1: Pavement climatic regionalization
Questions
References
Chapter 16 Discriminant analysis
Abstract
16.1 Introduction
16.2 Definition
16.3 Distance-based discriminant analysis
16.4 Bayesian discriminant analysis
16.5 Fisher’s discriminant analysis
16.6 Case: Discriminant analysis of pavement climatic regions
Questions
References
Chapter 17 Structural equation model
Abstract
17.1 Introduction
17.2 SEM model
17.3 MIMIC model
17.4 Parameter estimates
17.5 Model goodness of fit
17.6 Explicit expression of latent variables
17.7 Case 1: Performance of pavement treatments
17.8 Case 2: Development of a latent pavement condition index
Questions
References
Chapter 18 Markov chain Monte Carlo
Abstract
18.1 Introduction
18.2 Bayesian model
18.3 Monte Carlo method
18.4 Markov chain Monte Carlo method
18.5 Case: Logistic regression with MCMC
Questions
References
Index

QD

Qiao Dong

Dr. Qiao Dong received the B.S. in civil engineering in 2003 and M.S. degree in roadway and railway engineering in 2006 from Southeast University, Nanjing, China and Ph.D. degree in civil and environmental engineering in 2011 from the University of Tennessee, Knoxville, USA. From 2011 to 2016, he was a research associate in the University of Tennessee. He joint Southeast University since 2016 as a Professor. His research interests include pavement asset management based on data analysis and artificial intelligence, pavement distress non-destructive evaluation, pavement materials multiscale characterization and simulation. Dr. Dong has worked on data driven pavement evaluation and management since 2006 and selected pavement data modelling and mining as the topic of his Ph.D. dissertation. He won the first prize in the American Society of Civil Engineers (ASCE) Long-Term Pavement Performance (LTPP) data analysis contest in 2010. He was the PI or co-PI of several related research projects. He has published more than 100 research papers, and more than 30 of them focus the field of pavement data analysis. He is currently an active member of the Bituminous Materials Committee (BMC) of American Society of Civil Engineers, the Pavement Maintenance Committee (AHD20) of Transportation Research Board (TRB) and the Pavement Performance Evaluation Committee of the World Transportation Congress. He served as a young editor for the Journal of Infrastructure Preservation and Resilience and an editor of Coatings.

Affiliations and expertise

Southeast University, Department of Roadway Engineering, School of Transportation, Nanjing, Jiangsu, China

XC

Xueqin Chen

Xueqin Chen received B.S. and Ph.D. degrees in civil engineering from Tongji University, Shanghai, China in 2012 and 2018. From 2014-2016, She was a visiting scholar at the University of Tennessee, Knoxville, USA. She is currently an assistant professor in the Department of Civil Engineering in Nanjing University of Science and Technology, Nanjing, China. Her research interest include performance evaluation and prediction of the life cycle of infrastructure, infrastructure maintenance decisions and asset management, smart Infrastructure, and pavement management system. Dr. Chen was the first prize winner of the ASCE LTPP data analysis contest in 2015. The topic of her Ph.D. dissertation is data modelling and mining on transportation infrastructure including tunnel and pavement. She has published more than 10 papers on related topics. She serves as a reviewer for Journal of Transportation Engineering, Journal of Cleaner Production, Tunneling and Underground Space Technology.

Affiliations and expertise

Nanjing University of Science and Technology, Department of Civil Engineering, Nanjing, Jiangsu, China

BH

Baoshan Huang

Baoshan Huang is the Edwin G. Burdette Professor, at the Department of Civil and Environmental Engineering at the University of Tennessee. Over the last fifteen years of his professional career, Huang has secured over 6.5 million dollars of research funding to support his research activities and published over 100 peer reviewed journal papers. He is registered as a professional engineer in Louisiana and his research includes: Infrastructural Materials:Engineering Characterization and Multi-Scale Modeling of Asphalt Cement, Asphalt Paving Mixtures, Portland Cement Concrete, other Infrastructural Materials Pavement Engineering: Pavement Design, Testing and Evaluation, Pavement Maintenance, Highway Asset Management Geotechnical Engineering: Soil Improvement, Slope Stability, Deep Excavations, Drill Shaft and other Deep Foundations

Affiliations and expertise

The Edwin G. Burdette Professor, Department of Civil and Environmental Engineering, University of Tennessee, USA

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Data Analysis in Pavement Engineering

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Qiao Dong

Xueqin Chen

Baoshan Huang