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ABSTRACT <br><br>Wind Loads On Non-Rectangular Flat-Roof Buildings: <br><br>Design Provisions And Application Of Machine Learning <br><br>Murad Aldoum <br><br>Concordia University, 2025 <br><br>Buildings with rectangular plans were, in the past 50 years, the main focus in the wind engineering field. Consequently, the wind design provisions of rectangular buildings are well established in the wind codes and standards. On the other hand, wind design provisions for non-rectangular buildings are generally not available in wind codes and standards. This study investigates wind pressures on roofs and walls of non-curved and non-rectangular buildings with four shapes L, U, T, and X with different plan dimensions and heights. This study aims to provide design guidelines for cladding and components of the envelope of non-rectangular low-rise buildings. <br><br>The experimental results of roofs and walls were analyzed and compared with the design provisions and guidelines of &#8203;NBCC 2020&#8203; and &#8203;ASCE/SEI 7-22, 2022&#8203; for rectangular buildings. The comparison with &#8203;NBCC 2020&#8203; indicated that roof design provisions are comparable to the experimental results in the corner zone and lower than the experimental peaks in the edge and interior zones. The comparison with &#8203;ASCE/SEI 7-22, 2022&#8203; shows that the standard roof design provisions of rectangular buildings are conservative and applicable for the design of non-rectangular buildings. The experimental results also indicate that the size of the roof pressure zone is mainly dependent on the roof height. <br><br>The wall pressures were also compared to the wall design peaks of the North American codes and standards. The experimental results indicate that relatively high suctions occur on the wall areas at the corners and the reentrant corners (wall edges) and lower suctions on the wall middle areas. The rectangular building provisions of ASCE 7-22 were satisfactory and can be used for the design of walls of non-rectangular buildings, while the rectangular building provisions of NBCC 2020 require further modifications to become applicable to non-rectangular buildings. <br><br>Furthermore, the wind tunnel measurements not only provided valuable data but also served as a dataset when applying Machine Learning (ML) as a tool to predict wind loads on non-rectangular buildings. This involved the utilization of ensemble ML and Artificial Neural Networks (ANN), using two data split approaches: random and structured splits. The ML models exhibit significant predictive accuracy, achieving minimal Mean Squared Error (MSE) and coefficients of determination (R-squared) of about 0.97 for wind pressure coefficients. In addition, the study demonstrated that a structured split of the dataset reflects a more realistic assessment of the ML models. <br><br>Keywords: non-rectangular buildings; wind tunnel testing; roof pressures; wall pressures; pressure zonal system; NBCC 2020; ASCE 7-22; ensemble Machine Learning; Artificial Neural Networks; wind-induced loads. <br><br> <br><br> <br><br>

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