Data Analysis in Pavement Engineering

Qiao Dong , Xueqin Chen , Baoshan Huang 지음

Elsevier 2023년 11월 06일 출간

작품소개

책소개

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.

목차

PrefaceChapter 1 Pavement Performance DataChapter 2 Fundamentals of statisticsChapter 3 Design of experimentsChapter 4 RegressionChapter 5 Logistic regressionChapter 6 Count data modelsChapter 7 Survival analysisChapter 8 Time seriesChapter 9 Stochastic processChapter 10 Decision trees and ensemble learningChapter 11 Neural networksChapter 12 Support vector machine and k-nearest neighborsChapter 13 Principal component analysisChapter 14 Factor analysisChapter 15 Cluster analysisChapter 16 Discriminant analysisChapter 17 Structural equation modelChapter 18 Markov chain Monte Carlo

출판사 서평

작가정보

저자(글) Qiao Dong

저자(글) Xueqin Chen

저자(글) Baoshan Huang