Data Analysis in Pavement Engineering
Methodologies and Applications
- 1st Edition - November 6, 2023
- Imprint: Elsevier
- Authors: Qiao Dong, Xueqin Chen, Baoshan Huang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 5 9 2 8 - 2
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 5 9 2 9 - 9
Data Analysis in Pavement Engineering: Methodologies and Applications introduces thetheories and methods as well as definitions, principles, and algorithms of data analysis… Read more
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Request a sales quoteData 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.
- This book is the first comprehensive resource to cover all potential scenarios of data analysis in pavement and transportation infrastructure research, including areas such as materials testing, performance modeling, distress detection, and pavement evaluation.
- It provides coverage of significance tests, design of experiments, data mining, data modeling, and supervised and unsupervised machine learning techniques.
- It summarizes the latest research in data analysis within pavement engineering, encompassing over 300 research papers.
- It delves into the fundamental concepts, elements, and parameters of data analysis, empowering pavement engineers to undertake tasks typically reserved for statisticians and data scientists.
- The book presents 21 step-by-step case studies, showcasing the application of the data analysis method to address various problems in pavement engineering and draw meaningful conclusions.
for use as a textbook for undergraduate and graduate students in pavement engineering, bridge engineering, civil engineering, and other related majors of transportation infrastructure or as a reference book for engineers in the field of pavement and transportation infrastructure maintenance and operation.
- 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
- Edition: 1
- Published: November 6, 2023
- No. of pages (Paperback): 376
- No. of pages (eBook): 205
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780443159282
- eBook ISBN: 9780443159299
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, ChinaXC
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, ChinaBH
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, USARead Data Analysis in Pavement Engineering on ScienceDirect