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The traditional approach to formulating building codes often is slow and 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 paper proposes a machine learning (ML) approach to append a variety of codal provisions, including those of empirical, statistical, and theoretical natures. 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 use in lieu of the codal expression. This approach was examined successfully for seven structural engineering phenomena contained within various building codes, including those in North America and Australia. The findings suggest that the proposed approach could lay the groundwork for implementing ML in the development of future building codes. [ABSTRACT FROM AUTHOR]

North America is facing an aging watermain infrastructure crisis. Pipes are surpassing their expected service life, and surveys continue to suggest that the rate of pipe breaks is increasing dramatically. This paper investigates two utilities in Ontario, Canada, that have managed to halt, and even decrease, break rates substantially over the last two decades. Specifically, the impact of adopting early rehabilitation for both cities, including hot-spot cathodic protection, retrofit cathodic protection, lining, early adoption of PVC, and proactive pipe replacement are examined. A random survival forest model is developed for major pipe cohorts for both cities, and using this machine-learning survival analysis model, the numbers of breaks avoided due to pipe rehabilitation are examined. The results indicate pipe rehabilitation techniques avoided 14,572 breaks between 1986 and 2015 for City A and 764 breaks between 1990 and 2020 for City B, and, in the absence of these undertakings, incurred break rates would have been more than 50% greater for both cities. Therefore, pipe rehabilitation is a major factor that has resulted in the declining break rates identified in both cities, suggesting that other cities struggling with increasing break rates should consider similar rehabilitation strategies. [ABSTRACT FROM AUTHOR]