Artificial Intelligence in Civil Engineering: Recent Advances (Studies in Systems, Decision and Control, 643)

Artificial Intelligence in Civil Engineering: Recent Advances provides a comprehensive overview of the latest developments in applying artificial intelligence (AI) techniques to solve complex civil engineering problems. Civil engineers today face challenges that demand not only safety and cost-effectiveness, but also sustainability and resilience against natural hazards. This volume demonstrates how AI offers powerful tools to meet these demands by transforming traditional engineering methodologies.

The chapters present a broad spectrum of applications, including predicting shear wave velocity, slope stability, estimating uniaxial compressive strength of soils, optimization-based design of reinforced concrete retaining walls, structural control strategies for seismic resilience, and the integration of Building Information Modeling (BIM) with AI-driven solutions. Machine learning, deep learning, and metaheuristic algorithms are explored in detail, supported by real-world case studies and experimental data.

By bridging theory with practice, the book highlights how AI enhances predictive accuracy, optimizes design processes, and reduces computational effort in engineering tasks. Researchers, graduate students, and practicing engineers will find this volume a timely reference, offering both methodological foundations and practical insights into AI-powered approaches that are reshaping the future of civil engineering.

Artificial Intelligence in Civil Engineering: Recent Advances provides a comprehensive overview of the latest developments in applying artificial intelligence (AI) techniques to solve complex civil engineering problems. Civil engineers today face challenges that demand not only safety and cost-effectiveness, but also sustainability and resilience against natural hazards. This volume demonstrates how AI offers powerful tools to meet these demands by transforming traditional engineering methodologies.

The chapters present a broad spectrum of applications, including predicting shear wave velocity, slope stability, estimating uniaxial compressive strength of soils, optimization-based design of reinforced concrete retaining walls, structural control strategies for seismic resilience, and the integration of Building Information Modeling (BIM) with AI-driven solutions. Machine learning, deep learning, and metaheuristic algorithms are explored in detail, supported by real-world case studies and experimental data.

By bridging theory with practice, the book highlights how AI enhances predictive accuracy, optimizes design processes, and reduces computational effort in engineering tasks. Researchers, graduate students, and practicing engineers will find this volume a timely reference, offering both methodological foundations and practical insights into AI-powered approaches that are reshaping the future of civil engineering.