Assistant Professor at Clemson University and AI Research Institute for Science & Engineering (AIRISE)
This #paper presents a pathway to tackle the complex process of modernizing building codes and standards, which often struggle to keep up with technological and domain knowledge advancements. This research introduces the concept of "Equivalent Machine Learning Models." In essence, we engineer ML models to comprehend the foundational principles behind codal provisions, effectively learning the rationale and methodology that guided their initial formulation. We cannot rely on the traditional approach to building ML models to realize equivalent models. Thus, the paper also proposes a new methodology in which equivalent models are trained on #data and to analyze the underlying properties of codal provisions in terms of physics principles, engineering intuition, and causal logic. This ensures the equivalent models not only accurately capture the DNA of the provisions but also produce reliable predictions that are inherently #understandable, mirroring the human logic embedded within the original codal provisions. We tested this methodology across seven structural engineering problems documented in several building codes (including the American Society of Civil Engineers, American Concrete Institute, Australian Building Codes Board, and the American Wood Council). These case studies cover #empirical, #statistical, and #theoretical codal provisions. Our findings indicate: 1. Equivalent Machine Learning Models have the potential to be easily integrated into future building codes, offering a faster, more efficient path to adoption. 2. Despite achieving high predictive accuracy, the “traditional” approach to building ML models is likely to suffer in capturing the properties of codal provisions. 3. This is one tiny step, and more work is evidently needed. Your thoughts and feedback could further pave the way for innovative solutions in our field. As always, I am grateful for the forward-thinking perspective and insights provided by the reviewers and editors of the ASCE’s Journal of Structural Engineering & the SEI - Structural Engineering Institute Paper: https://lnkd.in/e5EfqhKt Preprint on ResearchGate: https://lnkd.in/eJ4Xzruz #Construction #Civilengineering #buildingcodes #machinelearning