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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 analysis of international and collaborative publication trends in prominent behavior‐analytic journals has been a topic of interest for behavioral researchers. This paper focuses on publication trends from 1997 through 2020 in three prominent journals: Journal of the Experimental Analysis of Behavior (JEAB), Journal of Applied Behavior Analysis (JABA), and Perspectives on Behavior Science (PBS). The variable of interest was the percentage of articles published per geographical category—Australasia/East Asia, Europe, Latin America, Middle East, North America, and Africa. The results showed that 79, 96, and 87% of the published articles in JEAB, JABA, and PBS, respectively, were conducted by researchers with a North American affiliation. Furthermore, 12, 4, and 4% of the articles in JEAB, JABA, and PBS, respectively, were coauthored by at least two researchers from different geographical categories. [ABSTRACT FROM AUTHOR]