Integrating Machine Learning into Building Codes: Establishing Equivalence through Causality and Intuition

Abstract

The traditional approach to formulating building codes is often slow, labor-intensive, and may struggle to keep pace with the rapid evolution of technology and domain findings. Overcoming such challenges necessitates a methodology that streamlines the modernization of codal provisions. This seminar proposes a machine learning (ML) approach to append a variety of codal provisions, including those of empirical, statistical, and theoretical nature. In this approach, a codal provision (i.e., equation) is analyzed to trace its properties (e.g., engineering intuition and causal logic). Then a ML model is tailored to preserve the same properties and satisfy a collection of similarity and performance measures until declared equivalent to the provision at hand. The resulting ML model harnesses the predictive capabilities of ML while arriving at predictions similar to the codal provision used to train the ML model, and hence, it becomes possible to adopt in line with the codal expression. This approach has been successfully examined on seven structural engineering phenomena contained within various building codes, including those in North America and Australia. Our findings suggest that the proposed approach could lay the groundwork for implementing ML in the development of future building codes.

Bio

M.Z. Naser, professional engineer and an assistant professor at the School of Civil and Environmental Engineering and Earth Sciences at Clemson University and a faculty member of the AI Research Institute for Science and Engineering (AIRISE), serves as the current chair of the American Society of Civil Engineers (ASCE) Advances in Information Technology (AIT) committee and a voting member of various national and international engineering institutions. Naser’s research creates causal and explainable machine learning methodologies to help us realize functional, sustainable, and resilient infrastructure. He has co-authored over 140 peer-reviewed publications, including a new textbook on machine learning and civil engineering, titled “Machine Learning for Civil and Environmental Engineers: A Practical Approach to Data-Driven Analysis, Explainability, and Causality” by Wiley. He is listed in the company with the world’s most impactful researchers by Elsevier and Stanford University, ranking among the world’s top 2% of scientists for two constitutive years (2022–2023).