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Computational Intelligence for Analysis, Design and Assessment of Civil Infrastructure

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Author:   M. Z. Naser (Assistant Professor, Department of Civil and Environmental Engineering and Earth Sciences, College of Engineering, Computing and Applied Sciences, Clemson University, Clemson, SC, USA)
Publisher:   Elsevier Science Publishing Co Inc
Imprint:   Woodhead Publishing
Weight:   1.000kg
ISBN:  

9780128240731

ISBN 10:   0128240733
Pages:   298
Publication Date:   23 October 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

1. Integrated schematic design method for shear wall structures: A practical application of generative adversarial networks 2. Leveraging machine learning techniques to support a holistic performance-based seismic design of civil structures 3. Deep learning-based damage inspection for concrete structures 4. Explainable computational intelligence method to evaluate the damage on concrete surfaces compared to traditional visual inspection techniques 5. Smart building fire safety design driven by artificial intelligence 6. The potential of deep learning in dynamic maintenance scheduling for thermal energy storage chiller plants 7. The use of IDA on GPR data to monitor road transport infrastructures 8. Ai for large-scale evacuation modelling: promises and challenges 9. On the application of machine learning classifiers in evaluating liquefaction potential of civil infrastructure 10. Explainable machine learning model for prediction of axial capacity of strengthened CFST columns 11. Harnessing data from benchmark testing for the development of spalling detection techniques using deep learning

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Author Information

M. Z. Naser is a tenure-track Assistant Professor at the Department of Civil and Environmental Engineering and Earth Sciences and a member of the Artificial Intelligence Research Institute for Science and Engineering (AIRISE) at Clemson University. At the moment, his research group is creating causal & eXplainable machine learning methodologies to discover new knowledge hidden within systems belonging to the domains of structural engineering and materials science to help realize functional, sustainable, and resilient infrastructure. He is currently serving as the chair of the ASCE Advances in Information Technology committee and on a number of international editorial boards, as well as codal building committees (in ASCE, ACI, PCI, and FiB). He is a registered professional engineer in the states of Michigan and South Carolina.

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