Data Analysis in Pavement Engineering (eBook)

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.

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.

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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.