Qiao Dong, Xueqin Chen, Baoshan Huang

Data Analysis in Pavement Engineering (eBook, ePUB)

Methodologies and Applications

• Format: ePub

Produktbeschreibung

Data Analysis in Pavement Engineering: Theory and Methodology offers a complete introduction to the basis of the finite element method, covering fundamental theory and worked examples in the detail required for readers to apply the knowledge to their own engineering problems and understand more advanced applications. This edition sees the significant addition of content addressing coupling problems, including Finite element analysis formulations for coupled problems; Details of algorithms for solving coupled problems; and Examples showing how algorithms can be used to solve for piezoelectricity and poroelasticity problems.

Focusing on the core knowledge, mathematical and analytical tools needed for successful application, this book represents the authoritative resource of choice for graduate-level students, researchers and professional engineers involved in finite element-based engineering analysis.

• 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.

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 205
• Erscheinungstermin: 6. November 2023
• Englisch
• ISBN-13: 9780443159299
• Artikelnr.: 69354680

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.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.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 MaterialsPavement Engineering: Pavement Design, Testing and Evaluation, Pavement Maintenance, Highway Asset ManagementGeotechnical Engineering: Soil Improvement, Slope Stability, Deep Excavations, Drill Shaft and other Deep Foundations

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