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Book synopsis

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.

• A domain-specific resource combining machine learning with structural engineering
• Describes advanced machine learning techniques for a wide range of structural engineering applications
• Clearly demonstrates how data-driven approaches are reshaping decision-making, enhancing resilience and providing valuable predictive insights, of particular importance in structural health monitoring and disaster resilience
• Theoretical approaches illustrated by extensive use of practical case studies and real-world examples

Book details

Book category Books in English Technology, engineering, agriculture Civil engineering, surveying & building Building construction & materials

• Full title: Machine Learning Applications in Structural Engineering
• Author: Rahul Biswas, Pijush Samui, Panagiotis G. Asteris
• Language: English
• Binding: Paperback
• Number of pages: 450
• EAN: 9780443440359
• ISBN: 0443440352
• ID: 50236916
• Publisher: Elsevier Science
• Dimensions: 229 × 152 mm
• Date of publishing: 01. August 2026

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