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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 increase of road freight around the world, alongside limited funding on bridge infrastructure, demands objective and accurate safety assessments for greater loads to achieve better utilization of existing aging bridges. Probability-based bridge assessment (PBBA) is already recommended around the world as a higher-tier assessment beyond current deterministic methods. However, its uptake in international practice lags behind that merited. The promotion of PBBA in practice hinges on four aspects: (i) justification, through proof of concept; (ii) accessibility, through integration into current practice; (iii) effectiveness, through utilizing state-of-the-art tools, and; (iv) familiarity, through clear communication and education. This paper describes a recent Austroads research project that has developed solutions to these aspects to promote the adoption of PBBA in Australia. The experience gained in developing the freely available guideline is described in this paper to support the promotion of PBBA adoption in other countries. [ABSTRACT FROM AUTHOR]

Offshore oil and gas platforms, pipelines and other ancillary offshore infrastructure are aging in Australia and current regulatory frameworks favour complete removal at the end of life. However, evidence indicates that artificial reefs have formed around some of these structures and their removal could cause more harm than good. Furthermore, other perceived social, environmental and economic benefits of a total removal policy may not be warranted. The Australian regulator (NOPSEMA) is currently exploring the possibility of supporting an in situ decommissioning policy, in which alternatives to full removal such as leaving in situ, partial removal or nearby relocation may be adopted if demonstrated to be the preferable approach. This will necessarily involve changes to law and policy but such amendments must be evidence-based. The evidence needed will largely involve the disciplines of engineering and natural sciences, but also fields such as environmental management, economics, social sciences and law. If Australia were to progress an in situ decommissioning policy shift, research will be needed across all of these areas in the specific national context. This paper commences by outlining emergent engineering knowledge, showing the general conservatism of current methodologies available to assess the integrity of decommissioned offshore facilities. Thereafter, the particular legal environment in Australia is explored. This article contributes to the growing body of literature on in situ decommissioning but in setting a multi-disciplinary research agenda takes a more holistic approach. [ABSTRACT FROM AUTHOR]