Qiao Dong (Southeast University, Department of Roadway Engineering,, Xueqin Chen (Nanjing University of Science and Departme Technology, Baoshan Huang (Department of Civil The Edwin G. Burdette Professor

Data Analysis in Pavement Engineering

Methodologies and Applications

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Woodhead Publishing Series in Civil and Structural Engineering
• Verlag: Elsevier - Health Sciences Division
• Seitenzahl: 376
• Erscheinungstermin: 9. November 2023
• Englisch
• Abmessung: 228mm x 151mm x 21mm
• Gewicht: 602g
• ISBN-13: 9780443159282
• ISBN-10: 0443159289
• Artikelnr.: 67367879

Autorenporträt

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.

Inhaltsangabe

Preface
Chapter 1 Pavement Performance Data
Chapter 2 Fundamentals of statistics
Chapter 3 Design of experiments
Chapter 4 Regression
Chapter 5 Logistic regression
Chapter 6 Count data models
Chapter 7 Survival analysis
Chapter 8 Time series
Chapter 9 Stochastic process
Chapter 10 Decision trees and ensemble learning
Chapter 11 Neural networks
Chapter 12 Support vector machine and k-nearest neighbors
Chapter 13 Principal component analysis
Chapter 14 Factor analysis
Chapter 15 Cluster analysis
Chapter 16 Discriminant analysis
Chapter 17 Structural equation model
Chapter 18 Markov chain Monte Carlo