Machine Learning Applications in Structural Engineering

Erscheinungsjahr 2026
ISBN: 978-0-443-44035-9
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 152 mm x 229 mm

Resilient Structures: Machine Learning Applications in Structural Engineering is a practical guide to machine learning in structural engineering. It is aimed at engineers, researchers and students with an interest in integrating new, machine learning technologies into daily practice; the book provides a balance of foundational theory with hands-on, data-driven solutions tailored to meet real-world demands. With first-hand examples of machine learning applications, this book is a vital reference for both entry-level readers and advanced professionals. For experts, the book offers insights into emerging applications that are actively shaping the future of the discipline, making it a compelling choice for engineers looking to leverage machine learning for smarter, more resilient structural solutions. Enables experienced professionals to explore new applications and approaches. An accessible style makes complex concepts manageable; the book offers clear explanations while showcasing the potential of machine learning as a versatile tool for advancing structural engineering practices.

Autoren/Hrsg.

Biswas, Rahul

Samui, Pijush

Asteris, Panagiotis G.

Inhaltsverzeichnis

1. Concrete Technology and Machine Learning Applications
2. Earthquake Engineering Models with Machine Learning
3. Wind Engineering
4. Steel Structure
5. Structural Health Monitoring and Predictive Maintenance
6. Data Integration and Model Optimization in Structural Engineering
7. Case Studies in Machine Learning for Structural Engineering

Über Autor(innen)

Biswas, Rahul
Dr Rahul Biswas is an Assistant Professor in the Applied Mechanics Department at Visvesvaraya National Institute of Technology (VNIT) Nagpur, India. Dr Biswas's primary research interests centre around concrete technology and the utilization of sustainable materials in concrete. Additionally, he is actively involved in exploring the application of machine learning in the field of structural engineering

Samui, Pijush
Dr. Samui is an Associate Professor in the Department of Civil Engineering at NIT Patna, India. He received his PhD in Geotechnical Engineering from the Indian Institute of Science Bangalore, India, in 2008. His research interests include geohazard, earthquake engineering, concrete technology, pile foundation and slope stability, and application of AI for solving different problems in civil engineering. Dr. Samui is a repeat Elsevier editor but also a prolific contributor to journal papers, book chapters, and peer-reviewed conference proceedings.

Asteris, Panagiotis G.
Professor Asteris received his B.S., M.S., and PhD in Civil Engineering from the National Technical University of Athens, Greece. He is currently a Full Professor and the Head of the Computational Mechanics Laboratory, and the Head of the Civil Engineering Department of the School of Pedagogical and Technological Education, Athens. Prof. Asteris is a trailblazer in the field of computational structural engineering. His research spans diverse areas, including artificial neural networks, soft computing, applied and computational mathematics, and masonry materials and structures. He is also the editor-in-chief of two international scientific journals and a member of the editorial board of more than ten international journals.