Data Analysis in Pavement Engineering: Methodologies and Applications

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. 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. INDICE: 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

• ISBN: 978-0-443-15928-2
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 376
• Fecha Publicación: 09/11/2023
• Nº Volúmenes: 1
• Idioma: Inglés