Artificial Intelligence Applications for Sustainable Construction
- 1st Edition - February 13, 2024
- Editors: Moncef L. Nehdi, Harish Chandra Arora, Krishna Kumar, Robertas Damaševičius, Aman Kumar
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 1 9 1 - 2
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 1 9 2 - 9
Artificial Intelligence Applications for Sustainable Construction presents the latest developments in AI and ML technologies applied to real-world civil engineering concerns.… Read more
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Request a sales quoteArtificial 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
Academics and researchers, postgraduate and undergraduate students in the fields of civil/structural/construction/environmental/chemical/materials engineering and architecture, R&D specialists in industry, professional engineers, data scientists, materials scientists
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- About the editors
- Foreword
- Preface
- Acknowledgments
- 1. Artificial intelligence in civil engineering: An immersive view
- 1.1. Introduction
- 1.2. Background of artificial intelligence
- 1.3. AI in civil engineering
- 1.4. Challenges of artificial intelligence in civil engineering
- 1.5. AI in civil engineering: A way forward
- 1.6. Conclusion
- Notations
- 2. Application of artificial intelligence in sustainable construction: A secret eye toward the latest civil engineering techniques
- 2.1. Introduction
- 2.2. Conclusions
- 3. Machine learning applications in the development of sustainable building materials to reduce carbon emission
- 3.1. Introduction
- 3.2. Steps involved in developing an ML model
- 3.3. ML-based prediction mimickers for sustainable materials
- 3.4. ML-based mimickers for optimized mix design
- 3.5. System health monitoring for damage detection
- 3.6. Multiobjective optimal design
- 3.7. Conclusion
- 4. Application of machine learning models for the compressive strength prediction of concrete with glass waste powder
- 4.1. Introduction
- 4.2. Methods
- 4.3. Experimental dataset and model accuracy criteria
- 4.4. Results and discussion
- 4.5. Conclusions
- 5. AI-based structural health monitoring systems
- 5.1. Introduction
- 5.2. Structural health monitoring applications
- 5.3. Artificial intelligence
- 5.4. Hierarchy of ML algorithms
- 5.5. ML and DL applications in SHM
- 5.6. Future trends
- 5.7. Conclusion
- 6. Application of ensemble learning in rock mass rating for tunnel construction
- 6.1. Introduction
- 6.2. Methodology
- 6.3. Establishment of a multisource database
- 6.4. Test results
- 6.5. Discussion
- 6.6. Case study
- 6.7. Conclusions
- 7. AI-based framework for Construction 4.0: A case study for structural health monitoring
- 7.1. Introduction
- 7.2. Construction 4.0 definition and characteristics
- 7.3. Construction 4.0 enabling technologies
- 7.4. Construction 4.0 use cases and applications
- 7.5. Modeling frameworks and tools
- 7.6. Artificial intelligence in construction 4.0
- 7.7. AI-based concrete column base cover localization and degradation detection: A case study
- 7.8. Conclusion
- 8. Practical prediction of ultimate axial strain and peak axial stress of FRP-confined concrete using hybrid ANFIS-PSO models
- 8.1. Introduction
- 8.2. FRP-confined concrete
- 8.3. Experimental database
- 8.4. ANFIS
- 8.5. PSO
- 8.6. Hybrid ANFIS-PSO models
- 8.7. Conclusions
- 9. Prediction of long-term dynamic responses of a heritage masonry building under thermal effects by automated kernel-based regression modeling
- 9.1. Introduction
- 9.2. Kernelized regression models
- 9.3. Proposed prediction process
- 9.4. Application: The Consoli Palazzo
- 9.5. Conclusions
- 10. A comprehensive review on application of artificial intelligence in construction management using a science mapping approach
- 10.1. Introduction
- 10.2. Research methodology
- 10.3. Assessment and observations
- 10.4. Conclusion and recommendations
- 11. Textile-reinforced mortar-masonry bond strength calibration using machine learning methods
- 11.1. Introduction
- 11.2. Research significance
- 11.3. Existing TRM-masonry bond strength models
- 11.4. Experimental direct shear test specimens
- 11.5. Performance of existing models
- 11.6. Calibration of the best existing models
- 11.7. Conclusions
- 12. Forecasting the compressive strength of FRCM-strengthened RC columns with machine learning algorithms
- 12.1. Introduction
- 12.2. Literature review
- 12.3. Methodology
- 12.4. Results and discussion
- 12.5. Conclusions and future recommendations
- Nomenclature
- 13. Assessment of shear capacity of a FRP-reinforced concrete beam without stirrup: A machine learning approach
- 13.1. Introduction
- 13.2. Literature review
- 13.3. Research significance
- 13.4. Artificial intelligence (AI)
- 13.5. Methodology
- 13.6. Results and discussion
- 13.7. Conclusions
- Appendix A
- Nomenclatures
- 14. Estimating the load carrying capacity of reinforced concrete beam-column joints via soft computing techniques
- 14.1. Introduction
- 14.2. Research significance
- 14.3. Experimental database
- 14.4. Machine learning methods
- 14.5. Performance evaluation of the proposed and existing models
- 14.6. Sensitivity analysis
- 14.7. Conclusions
- 15. Global seismic damage assessment of RC framed buildings using machine learning techniques
- 15.1. Introduction
- 15.2. Methodology
- 15.3. Modeling and analysis of a building
- 15.4. Results and discussion
- 15.5. Analysis of seismic reliability
- 15.6. Machine learning algorithms for classification of damage level
- 15.7. Conclusions
- Index
- No. of pages: 450
- Language: English
- Edition: 1
- Published: February 13, 2024
- Imprint: Woodhead Publishing
- Paperback ISBN: 9780443131912
- eBook ISBN: 9780443131929
MN
Moncef L. Nehdi
Dr. Nehdi joined the University of Guelph as Dean, College of Engineering and Physical Sciences (CEPS) in September 2024. An experienced academic leader, Nehdi served as Chair of the Department of Civil Engineering, McMaster University. Prior, Nehdi was a Professor in the Department of Civil and Environmental Engineering at Western University from 2007 to 2021. Nehdi is an award-winning researcher and educator in sustainable civil engineering, particularly cement and concrete research, sustainable construction and the application of AI in materials and structures research. He has written more than 500 research publications and was listed by Elsevier and the Shanghai Global Ranking in the world’s most impactful civil engineers. Nehdi received his B.A.Sc. from Laval University and M.A.Sc. from Sherbrooke University, and holds a PhD from the University of British Columbia, all in civil engineering. He is a fellow of the Canadian Academy of Engineering, the Engineering Institute of Canada, the American Concrete Institute, the Canadian Society for Civil Engineering, and the Asia Pacific Artificial Intelligence Association.
Affiliations and expertise
Dean, College of Engineering and Physical Sciences, University of Guelph, Guelph, ON CanadaHA
Harish Chandra Arora
Dr. Harish Chandra Arora currently holds the esteemed position of Principal Scientist in the Structural Engineering Group at CSIR-Central Building Research Institute in Roorkee, India. With a distinguished career spanning more than 29 years, Dr. Arora is a renowned figure in the field of structural engineering. Dr. Arora is also functioning as an Associate Professor in the Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India. His contemporary research areas include structural composites, structural corrosion, distress diagnosis, seismic evaluation, repair and retrofitting of structures and machine learning applications in structural engineering, etc. Dr. Arora’s exceptional contributions to the field have garnered recognition in both national and international academic journals. Beyond his scholarly achievements, he has made a significant impact on the education and development of future engineers, having supervised and guided over 100þ students in their pursuit of bachelor of technology and master of technology degrees. Additionally, he continues to mentor and support research scholars pursuing doctoral programs at the Central Building Research Institute in Roorkee, India.
Furthermore, Dr. Arora actively contributes to the scholarly community as a reviewer for journals published by Springer Nature and Elsevier. His commitment to maintain the quality and rigor of academic publications is highly regarded. Beyond his academic pursuits, Dr. Arora has undertaken numerous consultancy and research and development projects within the field of structural engineering, further showing his dedication to advancing the science and practice of sustainable construction.
Affiliations and expertise
Principal Scientist, Dept. of Structural Engineering, CSIR-Central Building Research Institute, Roorkee, Uttarakhand, India; Associate Professor, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, IndiaKK
Krishna Kumar
Dr. Krishna Kumar received his BE degree in Electronics and Communication Engineering from Govind Ballabh Pant Engineering College, Pauri Garhwal, Uttarakhand, India, MTech degree in Digital Systems from Motilal Nehru NIT, Allahabad, India, in 2006 and 2012, respectively, and PhD degree in the Department of Hydro and Renewable Energy at the Indian Institute of Technology Roorkee, India, in 2023.
He is currently working as an Assistant Engineer at UJVN Ltd. (a State Government PSU of Uttarakhand) since January 2013. Before joining UJVNL, he worked as an Assistant Professor at BTKIT, Dwarahat (a Government of Uttarakhand Institution). He has published numerous research papers in international journals and conferences, including IEEE, Elsevier, Springer, MDPI, Hindawi, and Wiley. He has also edited and written books for Taylor & Francis, Elsevier, Springer, River Press, and Wiley. His current research interests include IoT, AI, and renewable energy.
Affiliations and expertise
Research and Development Engineer, UJVN Ltd. (A Govt. of Uttarakhand Enterprises), IndiaRD
Robertas Damaševičius
Prof. Robertas Damaševičius received his PhD degree in Informatics Engineering from the Kaunas University of Technology, Lithuania, in 2005. He is currently a Professor in the Department of Applied Informatics, Vytautas Magnus University, Lithuania, and the Department of Software Engineering, Kaunas University of Technology, Lithuania, as well as an Adjunct Professor at the Faculty of Applied Mathematics, Silesian University of Technology (Poland). He lectures courses on human-computer interaction design, robot programming, and software maintenance.
He is the author of more than 500 peer-reviewed articles and a monograph published by Springer. His research interests include assisted living, medical imaging, and medical diagnostics using explainable artificial intelligence and robotics. He is also the Editor-in-Chief of Information Technology and Control journal. He has been a Guest Editor of several invited issues of international journals, such as BioMed Research International, Computational Intelligence and Neuroscience, the Journal of Healthcare Engineering, IEEE Access, IEEE Sensors, and Electronics.
Affiliations and expertise
Professor, Department of Software Engineering, Kaunas University of Technology, Kaunas, LithuaniaAK
Aman Kumar
Aman Kumar holds a Master’s degree in Construction Technology and Management from the National Institute of Technical Teachers’ Training and Research, Chandigarh, and a Bachelor’s degree in Civil Engineering from I.K. Gujral Punjab Technical University, Jalandhar. He is currently pursuing a PhD at McMaster University, Hamilton, Canada. Before that, Kumar was a Project Associate at the CSIR – Central Building Research Institute, Roorkee, India. He also accrued practical experience by covering a variety of roles in industry, including as a Structural Health Monitoring Engineer with Aimil Ltd., New Delhi, and as a Quality Control Engineer with Ambuja Cements Ltd., Chandigarh. Aman has also served as an Assistant Professor at the Indo-Global College, Punjab, where he taught earthquake engineering, estimation & costing, and concrete technology. His professional expertise spans non-destructive testing (NDT), structural health monitoring, fiber-reinforced polymers, fiber-reinforced cementitious matrices, and AI and ML applications for structural engineering.
Affiliations and expertise
PhD Candidate, Department of Civil Engineering, McMaster University, Hamilton, ON, CanadaRead Artificial Intelligence Applications for Sustainable Construction on ScienceDirect