Artificial Intelligence in 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.

Focus on AI and optimization in civil engineering, with chapters dedicated to machine learning within that context Includes an introduction to Artificial Intelligence in Civil Engineering Provides a broad spectrum of civil engineering applications, introducing fundamental concepts and advanced adaptations

Inhalt

Recent Advences of Artificial Intelligence in Civil Engineering.- Metaheuristic-based optimization and application of metaheuristics on reinforced concrete structure.- Artificial neural networks model for natural frequency prediction in reinforced concrete slabs.- Human-inspired optimization algorithms for cost minimization in reinforced concrete column design.-Multioutput Regression for Reinforced Retaining Wall Optimum Design with Machine Learning.- Advanced Regression Strategies for Concrete Strength Estimation: A Comparative Ensemble Approach.- Optimization of Triangular Reinforced Concrete Beam with Various Metaheuristic Algorithms.- Metaheuristic-based Detailed Optimization of Reinforced Concrete T-beams and Evaluation of the Effect of Concrete Class.- Artificial Intelligence for Structural Vibration Control: A Review of Previous Studies and Methodologies.- Isolator Damping Capacity with Catboost Algorithm Hyperparameter Optimization in Classification.- Optimum Multiple Tuned Mass Dampers for Soft Story Structure.- Artificial Intelligence for Structural Health Monitoring: Techniques, Applications, and Future Directions.- Surface Crack Detection Using Deep Learning and Stacking Ensemble.- Optimization of Time-Cost-Environmental Impact Problems in Construction Projects with Grey Wolf Algorithm.- Automated Machine Learning for the Prediction of Compression Index of Soils.- Shear Wave Velocity Prediction Using Machine Learning.- Geospatial LULC and LST Change Analysis with Future Growth Prediction Using Random-Forest and MLP-MCA Algorithms.- Precipitation Forecasting in the Konya Closed Basin Using LSTM: A Comparative Analysis of Optimization Algorithms.- Machine Learning for Water Quality Monitoring: Comparative Analysis of AI Models in River Assessment.- A deep learning application to predict reservoir operations.- Global Impact of Artificial Intelligence on the Sustainability of Civil Engineering Infrastructure.- An Analysis of Rolling Horizon based Route Optimization for the Pickup-and-Delivery Problem with Time Windows in the Context of Paratransit Applications.- Development of Feature Selection Based Preventive Railway Track Maintenance Decision System.- Development of Machine Learning Algorithms Based Prediction of Power Efficient Railway Track Maintenance Model.- Impact Of Artificial Intelligence in Civil Engineering Education.