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Full 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.
Contents
1. Artificial Intelligence in Civil Engineering: An Immersive View
2. Application of Artificial Intelligence in Sustainable Construction: Secret Eye towards Latest Civil Engineering Techniques
3. Machine Learning (ML) in Sustainable Composite Building Materials to Reduce Carbon Emission
4. Application of Machine Learning Models for the Compressive Strength Prediction of Concrete with Glass Waste Powder
5. AI-based Structural Health Monitoring Systems
6. Application of Ensemble Learning in Rock Mass Rating for Tunnel Construction
7. AI-based Framework for Construction 4.0: A Case Study for Structural Health Monitoring
8. Practical Prediction of Ultimate Axial Strain and Peak Axial Stress of FRP-Confined Concrete using Hybrid ANFIS-PSO Models
9. Prediction of Long-Term Dynamic Responses of a Heritage Masonry Building under Thermal Effects by Automated Kernel-Based Regression Modeling
10. A Comprehensive Review on Application of Artificial Intelligence in Construction Management using Science Mapping Approach
11. Textile Reinforced Mortar-Masonry Bond Strength Calibration Using Machine Learning Methods
12. Forecasting the compressive strength of FRCM-strengthened RC columns with Machine learning algorithms
13. Assessment of Shear Capacity of FRP-Reinforced Concrete Beam Without Stirrup: Machine Learning Approach
14. Estimating the Load Carrying Capacity of Reinforced Concrete Beam-Column Joints via Soft Computing Techniques
15. Global Seismic Damage Assessment of RC Framed Buildings using Machine Learning Techniques
