Data-Centric Civil Engineering
Data Science for Advanced Civil Engineering Applications
- 1st Edition - January 1, 2026
- Imprint: Woodhead Publishing
- Author: Nima Gerami-Seresht
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 1 9 2 5 - 2
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 1 9 2 6 - 9
Despite this rising enthusiasm among post-graduate students and R&D practitioners for new data-driven techniques in civil engineering, no comprehensive references book exist th… Read more
Purchase options
Despite this rising enthusiasm among post-graduate students and R&D practitioners for new data-driven techniques in civil engineering, no comprehensive references book exist that introduces such techniques to the civil engineering audience and explores the applications of these techniques in civil engineering. Consequently, this enthusiastic audience often refers to textbooks published in other engineering disciplines to learn the fundamentals of data science techniques; and then refers to journal and conference articles to learn the civil engineering applications of these techniques.
This approach often leads to familiarizing the readers with a set of techniques that suit other engineering disciplines and using irrelevant engineering examples. Additionally, this approach ignores the unique characteristics of civil engineering problems (e.g., empirical data limitation).
This book addresses these limitations by providing the first reference book on data-centric civil engineering to introduce advanced data science theories to the civil engineering audience. This book uses a variety of problems from the different fields of civil engineering (e.g., transportation, structure, and construction) and considers the unique limitations that civil engineers deal with in their research and practices.
- Provides a reliable source of information for engineers and researchers on data science techniques as applied to civil engineering problems
- Closes a gap in civil engineering knowledge by introducing the fundamentals of data science techniques for civil engineering audiences and their civil engineering applications
- Provides practical information for both researchers and working construction project managers/civil engineers
Graduate-Level Civil Engineering Students who pursue research in the different fields of civil engineering, including structural, construction, and transportation; thise involved in civil engineering R&D. Construction Project Managers
1. Introduction to Data-Centric Civil Engineering
2. Machine Learning and Pattern Recognition
3. Approximate Reasoning
4. Decision-Making
5. Simulation
2. Machine Learning and Pattern Recognition
3. Approximate Reasoning
4. Decision-Making
5. Simulation
- Edition: 1
- Published: January 1, 2026
- Imprint: Woodhead Publishing
- Language: English
NG
Nima Gerami-Seresht
Dr Gerami-Seresht is an assistant professor of sustainable and resilient infrastructure at Durham University. He completed his undergraduate studies in Civil Engineering and pursued his MSc and PhD in construction engineering and management. For over a decade, Dr Gerami-Seresht has researched the theory of advanced data science techniques. He also has a successful track record of implementing these theories to solve complex civil engineering problems, including construction productivity, construction fleet management, indoor air quality assessment, and sustainable and resilient infrastructure. He has 30+ publications on “data-centric civil engineering”, including award-winning journal articles, conference papers, and book chapters. Dr Gerami-Seresht taught multiple undergraduate- and graduate-level modules focused on data-centric civil engineering, including “CIV E654 - Artificial Intelligence and Automation in Construction” at the University of Alberta, Canada, and “Construction Project Management and Digital Engineering”, “Project Change, Risk and Opportunities Management”, and “Integrated Building Information Modelling Project” at Northumbria University, UK.
Affiliations and expertise
Assistant Professor of Sustainable and Resilient Infrastructure, Durham University, UK