Machine Learning Applications in Structural Engineering is a practical guide to machine learning in structural engineering. 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 shaping the future of the discipline, making it a compelling choice for engineers looking to leverage machine learning for smarter, more resilient structural solutions. This accessible style makes complex concepts manageable, and the book offers clear explanations while showcasing the potential of machine learning as a versatile tool for advancing structural engineering practices.
It is aimed at engineers, researchers, and students with an interest in integrating new, machine learning technologies into daily practice. Readers will find a balance of foundational theory with hands-on, data-driven solutions tailored to meet real-world demands.
- Provides a domain-specific resource that combines machine learning with structural engineering
- Describes advanced machine learning techniques for a wide range of structural engineering applications
- Demonstrates how data-driven approaches are reshaping decision-making, enhancing resilience, and providing valuable predictive insights that are of particular importance in structural health monitoring and disaster resilience
- Includes theoretical approaches that are illustrated with extensive use of practical case studies and real-world examples
