In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address many challenges in civil engineering.

Artificial Intelligence and Machine Learning Techniques for Civil Engineering highlights the latest technologies and applications of AI in structural engineering, transportation engineering, geotechnical engineering, and more. It features a collection of innovative research on the methods and implementation of AI and machine learning in multiple facets of civil engineering. Covering topics such as damage inspection, safety risk management, and information modeling, this premier reference source is an essential resource for engineers, government officials, business leaders and executives, construction managers, students and faculty of higher education, librarians, researchers, and academicians.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

Artificial Intelligence
Closed-Form Formulae
Concrete Structures
Construction Management
Damage Inspection
Data-Driven Damage Identification
Deep Learning
Information Modelling
Machine Learning
Safety Risk Management
Structural Engineering Design
Transverse Reinforcement

Reviews & Statements

Explainable Safety Risk Management in Construction With Unsupervised Learning is an essential compendium for professionals seeking to enhance construction risk management through advanced unsupervised learning methods. This chapter demonstrates the profound importance of unsupervised machine learning approaches, showcasing practical examples of how these techniques can be applied to distill complex, unlabeled data into actionable insights, elevating construction risk management to new levels of precision and efficiency.

– Vedat Togan, Professor of Civil Engineering, Karadeniz Technical University, Turkey

In our AI and ML-driven world, "Artificial Intelligence and Machine Learning Techniques for Civil Engineering" emerges as a guiding beacon, navigating readers through the transformative landscape of Artificial Intelligence (AI) and Machine Learning (ML) applications. The book encapsulates the remarkable journey of AI, from its roots in data handling to its pervasive influence in fields beyond.

AI and ML harness data, rapid processing, and intelligent algorithms, shaping our daily lives with personalized ads, virtual assistants, advancements like autonomous driving, and more. In civil engineering, they usher in a paradigm shift, empowering decision-makers to enhance efficiency and sustainability. These techniques prove invaluable in predicting structural performance, optimizing construction processes, and elevating project management.

The book unites industry leaders, sharing their insights and best practices, providing a comprehensive view of AI and ML's role in civil engineering. Comprising 13 chapters, each exploring unique facets, the book inspires readers to explore and contribute to the ever-evolving landscape of civil engineering. "Artificial Intelligence and Machine Learning Techniques for Civil Engineering" isn't merely a book; it is a gateway to a future shaped by the boundless possibilities AI and ML bring to civil engineering

– Vagelis Plevris, Associate Professor, Department of Civil and Environmental Engineering, Qatar University, Doha, Qatar

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Editor/Author Biographies

Vagelis Plevris is an Associate Professor at the Department of Civil and Environmental Engineering of Qatar University in Doha, Qatar. He serves as Chief Editor for “Computational Methods in Structural Engineering”, a section of the journal Frontiers in Built Environment, by Frontiers in Switzerland. He holds a Diploma in Civil Engineering (5-year studies) from the National Technical University of Athens (NTUA) with specialization in Structural Engineering. He also holds an MSc from NTUA on “Structural Design and Analysis of Structures”, a Master in Business Administration (MBA) from Athens University of Economics and Business (AUEB) and a PhD from the School of Civil Engineering of NTUA. His research interests include (i) Finite Element Method (FEM); (ii) Static and Dynamic Analysis of Structures with FEM; (iii) Earthquake Engineering; (iv) Optimum Design of Structures; (v) Reliability and Probabilistic Analysis of Structures; and (vi) Neural Networks and their Applications in Engineering. His published research work includes more than 150 contributions in journal papers, peer reviewed conference papers and book chapters.

Afaq Ahmad is a highly ambitious, innovative, and hardworking professional engineer with over 13 years of combined experience in design and research. Currently, he is working as Associate Professor at the University of Engineering & Technology Taxila. Founder of Scotant Engineer Consultant and member of Opti-Structure International. Currently, his research group is working on structural health monitoring through CNN and Image processing, prediction of reinforced concrete and composite material through Artificial Neural Networks. His published research work contains 45 peer-reviewed journal papers and 13 papers in international and national conferences, with a cumulative impact factor of 69+ and a ResearchGate score of 24. His research group is working with international research groups (Prof. Vagelis Plevris, Qatar University, Prof. Mohamed Elchalakani, University of Western Australia, Prof. Dr. Farid Abed, American University of Sharjah and Dr. Omar Alajarmeh, University of Southern Queensland).

Nikos Lagaros is the Dean of the School of Civil Engineering at NTUA and Professor of Structural Optimization at the School of Civil Engineering. Prior moving to NTUA, he was Assistant Professor at the Civil Engineering Department of the University of Thessaly, Greece. In the past he also served as Visiting Professor at Department of Biological Engineering, Laboratory for Computational Biology & Biophysics, Massachusetts Institute of Technology, Boston, USA and the Department of Mechanical Engineering, Faculty of Engineering, McGill University, Montreal, Canada. Dr. Lagaros obtained his BEng and PhD, both in Civil Engineering, from NTUA and he teaches classes on Structural Analysis, Optimization and Computer Programming. Dr. Lagaros is an active member of the computational mechanics research community. Among others, his publication track record includes more than 150 peer reviewed journal paper, 10 books and 25 book chapters while he has presented his work in numerous international venues. His h-index is 45 according to Google Scholar, with more than 6500 citations on his work.

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