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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]

The preparation of detailed models of information and process flow by 14 member companies of the North American Precast Concrete Software Consortium has provided a unique window into the current management, engineering design, and production operations in this industry. The modeling was performed using the authors’ Georgia Tech Process for Product Modeling tool, within the framework of the consortium’s effort to develop a precast concrete product model and to specify new integrated three dimensional modeling software. The paper opens with a comparative economic review of precast construction internationally and in North America, which reveals that the market share of precast construction in North American is relatively low. The models are analyzed and aspects of the underlying management procedures that they reveal are discussed, such as types of contracting arrangements, cost estimating, design outsourcing, engineering design communication, mold design, product diversity, and quality control. The results highlight aspects of precast management processes that may be re-engineered through appropriate application of information technology. [ABSTRACT FROM AUTHOR]