Data Analysis in Pavement Engineering 1st Edition

Data Analysis in Pavement Engineering — 1st Edition by Qiao Dong, Xueqin Chen, and Baoshan Huang

Discover a practical, research-driven guide that bridges advanced data methods and real-world pavement practice. This first edition equips transportation professionals, researchers, and graduate students with the analytical tools needed to understand, predict, and optimize pavement performance across urban streets, regional highways, and large-scale infrastructure networks.

Start with clear explanations of statistical fundamentals and progress to contemporary computational approaches tailored for pavement engineering. Through accessible examples and case-based workflows, the authors demonstrate how to extract meaningful insights from field measurements, materials testing, and monitoring systems—transforming raw data into reliable models for performance prediction, maintenance planning, and lifecycle cost analysis.

Engineered for application, the book emphasizes reproducible methods, rigorous interpretation, and practical decision-making. Readers will learn how to handle uncertainty, evaluate model fit, and apply regression, time-series, and predictive modeling techniques that improve pavement design and asset management outcomes. Whether you work on new construction, rehabilitation design, or network-level maintenance, this volume offers actionable guidance to support data-driven strategies.

International in perspective yet attentive to regional needs, the text is an essential resource for professionals across North America, Europe, Asia, and beyond seeking to modernize roadway analytics and extend pavement life.

Add this authoritative reference to your library to sharpen analytical skills, strengthen project proposals, and deliver smarter pavement solutions. Ideal for civil engineers, transportation planners, and academic programs focused on pavement engineering.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.