Description
Artificial Intelligence Applications for Sustainable Construction presents the latest developments in AI and ML technologies applied to real-world civil engineering concerns. With an increasing amount of attention on the environmental impact of every industry, more construction projects are going to require sustainable construction practices. This volume offers research evidence, simulation results, and case studies to support this change. Sustainable construction, in fact, not only uses renewable and recyclable materials when building new structures or repairing deteriorating ones, but also adopts all possible methods to reduce energy consumption and waste.The concisely written but comprehensive, practical knowledge put forward by this international group of highly specialized editors and contributors will prove to be beneficial to engineering students and professionals alike.- Presents convincing "success stories that encourage application of AI-powered tools to civil engineering- Provides a wealth of valuable technical information to address and resolve many challenging construction problems- Illustrates the most recent shifts in thinking and practice for sustainable construction
Table of Contents
1. Artificial Intelligence in Civil Engineering: An Immersive View2. Application of Artificial Intelligence in Sustainable Construction: Secret Eye towards Latest Civil Engineering Techniques3. Machine Learning (ML) in Sustainable Composite Building Materials to Reduce Carbon Emission4. Application of Machine Learning Models for the Compressive Strength Prediction of Concrete with Glass Waste Powder5. AI-based Structural Health Monitoring Systems6. Application of Ensemble Learning in Rock Mass Rating for Tunnel Construction7. AI-based Framework for Construction 4.0: A Case Study for Structural Health Monitoring8. Practical Prediction of Ultimate Axial Strain and Peak Axial Stress of FRP-Confined Concrete using Hybrid ANFIS-PSO Models9. Prediction of Long-Term Dynamic Responses of a Heritage Masonry Building under Thermal Effects by Automated Kernel-Based Regression Modeling10. A Comprehensive Review on Application of Artificial Intelligence in Construction Management using Science Mapping Approach11. Textile Reinforced Mortar-Masonry Bond Strength Calibration Using Machine Learning Methods12. Forecasting the compressive strength of FRCM-strengthened RC columns with Machine learning algorithms13. Assessment of Shear Capacity of FRP-Reinforced Concrete Beam Without Stirrup: Machine Learning Approach14. Estimating the Load Carrying Capacity of Reinforced Concrete Beam-Column Joints via Soft Computing Techniques15. Global Seismic Damage Assessment of RC Framed Buildings using Machine Learning Techniques
