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.
1. Artificial Intelligence Applications for Sustainable Construction - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. List of contributors - p. xi 7. About the editors - p. xv 8. Foreword - p. xix 9. Preface - p. xix 10. Acknowledgments - p. xxiii 11. 1 Artificial intelligence in civil engineering: An immersive view - p. 1 12. 2 Application of artificial intelligence in sustainable construction: A secret eye toward the latest civil engineering techniques - p. 75 13. 3 Machine learning applications in the development of sustainable building materials to reduce carbon emission - p. 93 14. 4 Application of machine learning models for the compressive strength prediction of concrete with glass waste powder - p. 123 15. 5 AI-based structural health monitoring systems - p. 151 16. 6 Application of ensemble learning in rock mass rating for tunnel construction - p. 171 17. 7 AI-based framework for Construction 4.0: A case study for structural health monitoring - p. 193 18. 8 Practical prediction of ultimate axial strain and peak axial stress of FRP-confined concrete using hybrid ANFIS-PSO models - p. 225 19. 9 Prediction of long-term dynamic responses of a heritage masonry building under thermal effects by automated kernel-based regression modeling - p. 257 20. 10 A comprehensive review on application of artificial intelligence in construction management using a science mapping approach - p. 285 21. 11 Textile-reinforced mortar-masonry bond strength calibration using machine learning methods - p. 301 22. 12 Forecasting the compressive strength of FRCM-strengthened RC columns with machine learning algorithms - p. 317 23. 13 Assessment of shear capacity of a FRP-reinforced concrete beam without stirrup: A machine learning approach - p. 341 24. 14 Estimating the load carrying capacity of reinforced concrete beam-column joints via soft computing techniques - p. 367 25. 15 Global seismic damage assessment of RC framed buildings using machine learning techniques - p. 389 26. Index - p. 405
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.
1. Artificial Intelligence Applications for Sustainable Construction - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. List of contributors - p. xi 7. About the editors - p. xv 8. Foreword - p. xix 9. Preface - p. xix 10. Acknowledgments - p. xxiii 11. 1 Artificial intelligence in civil engineering: An immersive view - p. 1 12. 2 Application of artificial intelligence in sustainable construction: A secret eye toward the latest civil engineering techniques - p. 75 13. 3 Machine learning applications in the development of sustainable building materials to reduce carbon emission - p. 93 14. 4 Application of machine learning models for the compressive strength prediction of concrete with glass waste powder - p. 123 15. 5 AI-based structural health monitoring systems - p. 151 16. 6 Application of ensemble learning in rock mass rating for tunnel construction - p. 171 17. 7 AI-based framework for Construction 4.0: A case study for structural health monitoring - p. 193 18. 8 Practical prediction of ultimate axial strain and peak axial stress of FRP-confined concrete using hybrid ANFIS-PSO models - p. 225 19. 9 Prediction of long-term dynamic responses of a heritage masonry building under thermal effects by automated kernel-based regression modeling - p. 257 20. 10 A comprehensive review on application of artificial intelligence in construction management using a science mapping approach - p. 285 21. 11 Textile-reinforced mortar-masonry bond strength calibration using machine learning methods - p. 301 22. 12 Forecasting the compressive strength of FRCM-strengthened RC columns with machine learning algorithms - p. 317 23. 13 Assessment of shear capacity of a FRP-reinforced concrete beam without stirrup: A machine learning approach - p. 341 24. 14 Estimating the load carrying capacity of reinforced concrete beam-column joints via soft computing techniques - p. 367 25. 15 Global seismic damage assessment of RC framed buildings using machine learning techniques - p. 389 26. Index - p. 405
