Causal Machine Learning in Civil and Environmental Engineering. Case Studies and Datasets. Woodhead Publishing Series in Civil and Structural Engineering

Machine learning (ML) is in constant transformation and various engineering disciplines are now heavily investing in it too. Currently, the majority of civil- and environmental-based works on ML are utilizing pure data-driven (i.e., black box) models built on correlations and associations. These models, however, do not truly identify the cause-effect relationship needed to answer questions such as: what caused a given structure to fail? Why does a particular construction material behave the way it does under specific conditions?

Causal Machine Learning in Civil and Environmental Engineering: Case Studies and Datasets aims to introduce causal ML approaches to civil and environmental engineering, covering theories, applications, as well as providing datasets, code, and examples of solutions to key problems in the sector. Students, academics, and engineering professionals both in the private and public sectors will find this book to be an invaluable reference source.

1. Civil and Environmental Engineering: Past, Present, and Future
2. Machine Learning: The Pursuit of Data-driven Analysis
3. Why Do We Need Causality? Overcoming the Limitations of Data-driven Analysis
4. Introduction to Causal Machine Learning: Theory and Algorithms
5. Application of Causal Machine Learning to Discover Knowledge in Civil and Environmental Engineering Problems
6. Application of Causal Inference to Discover Knowledge in Civil and Environmental Engineering Problems
7. Best Practices for Adopting Causal Machine Learning and Future Research Directions
8. A Look into the Future of Civil and Environmental Engineering from the Lens of Causal Machine Learning

Authors

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. 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.

Table of Contents

Authors

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