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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]

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]

Satellites provide unique perspectives on aerosol global and regional spatial and temporal distributions, and offer compelling evidence that visibility and air quality are affected by particulate matter transported over long distances. The heights at which emissions are injected into the atmosphere are major factors governing downwind dispersal. In order to better understand the environmental factors determining injection heights of smoke plumes from wildfires, we have developed a prototype system for automatically searching through several years of MISR and MODIS data to locate fires and the associated smoke plumes and to retrieve injection heights and other relevant measurements from them. We are refining this system and assembling a statistical database, aimed at understanding how injection height relates to the fire severity and local weather conditions. In this paper we focus on our working proof-of-concept system that demonstrates how machine-learning and data mining methods aid in processing of massive volumes of satellite data. Automated algorithms for distinguishing smoke from clouds and other aerosols, identifying plumes, and extracting height data are described. Preliminary results are presented from application to MISR and MODIS data collected over North America during the summer of 2004. [Copyright &y& Elsevier]