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CONTENTS
Volume 41, Number 4, October 2025 (Special Issue)
 

Abstract


Key Words


Address
Ahmed Elshaer (Lakehead University)
Jin Wang (University of Western Ontario)

Abstract
Rapid urbanization and vertical growth have made wind forces a crucial design factor for tall, flexible buildings. However, current design codes often lack comprehensive guidelines for extreme wind events, particularly thunderstorms and downbursts, which exhibit complex wind profiles that differ significantly from typical Atmospheric Boundary Layer (ABL) conditions. This study addresses these gaps by developing an aerodynamic database with experimental data on building responses to both ABL and downburst loads, aiming to enhance structural resilience against extreme wind events. Wind tunnel tests were conducted on nine building models with varying geometries and aspect ratios at the Toronto Metropolitan University Wind Tunnel, under a range of wind directions and terrain configurations to capture diverse wind-loading scenarios. Beyond establishing a comprehensive aerodynamic database, this research places particular emphasis on evaluating the structural response of buildings subjected to both ABL and downburst wind loads. The analysis systematically examines wind-induced forces, moments, and dynamic responses especially under downburst conditions. By integrating experimental data with advanced analysis, the study provides a robust framework for understanding and predicting building performance under extreme wind scenarios. By providing a more accurate and efficient tool for wind design in mid- and high-rise structures, this research reduces the dependency on extensive wind tunnel testing. The aerodynamic database developed here offers valuable insights for innovative engineering applications, facilitating safer and more adaptable urban building designs capable of withstanding both ABL and thunderstorm-induced wind loads.

Key Words
aerodynamic database; atmospheric boundary layer; downbursts; structures; wind engineering; wind loads; wind tunnel

Address
Moustafa Aboutabikh:Civil Engineering Department, Toronto Metropolitan University (formerly Ryerson University), Toronto, Ontario, Canada M5B 2K3

Tarek Ghazal:Civil Engineering Department, Toronto Metropolitan University (formerly Ryerson University), Toronto, Ontario, Canada M5B 2K3

Mohamed Abdelwahab:Civil Engineering Department, Toronto Metropolitan University (formerly Ryerson University), Toronto, Ontario, Canada M5B 2K3

Haitham Aboshosha:Civil Engineering Department, Toronto Metropolitan University (formerly Ryerson University), Toronto, Ontario, Canada M5B 2K3

Abstract
In recent decades, the evolution of modern city characteristics has preferred the development of tall structures, and Canada is no exception. The increasing prevalence of tall buildings in modern urban environments, including across Canada, necessitates a reexamination of traditional structural design and analysis methodologies. Recent advancements in computational power and algorithmic development have created opportunities to integrate machine learning (ML) and surrogate modelling techniques into structural engineering workflows. Optimizing tall buildings often relies on the characterization of dynamic wind load, which is a time-consuming and computationally demanding endeavour. The following research assesses the capacity of an ML algorithm based on a Convolutional Neural Network (CNN), to predict the structural performance of tall building designs. After utilizing Bayesian hyperparameter optimization, the model's performance describes the significant ability of CNNs to replicate results under linear dynamic wind load analysis. Through direct use of structural layout images as model inputs, the proposed framework allows for the rapid and accurate prediction of tall building design drawings. This work narrates the potential of CNN-based surrogate models in the design of tall buildings, especially when proposed for structural and multidisciplinary optimization.

Key Words
CNN; deep learning; performance-based design; shear wall; surrogate model; tall buildings; wind engineering

Address
Stephen T. Vasilopoulos:Department of Civil Engineering, Lakehead University, 955 Oliver Rd, Thunder Bay, ON P7B 5E1, Canada

Magdy Alanani and Ahmed Elshaer:Department of Civil Engineering, Lakehead University, 955 Oliver Rd, Thunder Bay, ON P7B 5E1, Canada

Abstract
Wind effects on buildings with rectangular plans have been investigated widely by wind engineering researchers either in wind tunnels or through CFD simulations. These studies provided comprehensive overviews and detailed descriptions of wind pressures on rectangular buildings and created the basic source required to formulate the wind provisions in the national and international codes and standards. However, buildings with irregular (i.e., non-rectangular) plans have not received adequate attention from wind tunnel investigations. Therefore, wind loads on irregular buildings are described shortly and shyly, if at all, in the current building codes and standards. This paper describes the experimental investigations into the flat-roof pressures of buildings with four non-rectangular shapes —L, U, T, and X— in an atmospheric boundary layer wind tunnel. The results reveal that the distribution of wind loads on the outer roof corners and edges of buildings with non-rectangular plans resembles that experienced by a rectangular building. However, the wind loads on the inner perimeter area, particularly the inner edge of the U shaped building, were observed to be generally higher than those recorded on the edges of a typical rectangular roof. 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 irregular buildings. This involved the utilization of a Gradient Boosting Regressor (GBR) 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. Further, the study demonstrated that a structured split of the dataset reflects a more realistic assessment of the ML models.

Key Words
machine learning; nonrectangular buildings; pressure zonal system; random split; roof pressures; structured split; wind tunnel testing

Address
Murad Aldoum:Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada

Ted Stathopoulos:Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada

Abstract
The paper provides the trajectory of research and practical applications established in structural wind engineering during the last decades in Canada, for low- mid- rise buildings. Various perspectives are discussed such as code provisions, wind tunnel experiments, full-scale measurements, while emphasis is given to Computational Wind Engineering (CWE). Latest versions of the National Building Code of Canada (NBCC) are considered, and some important new additions are discussed. Wind tunnel experimental results are presented from various studies to indicate the level of agreement among various facilities. The complexity of the interaction between wind flow and structures in the atmospheric boundary layer is significant, considering the differences found among similar experimental campaigns. Full-scale measurements are also compared with wind tunnel results and code provisions. Thresholds of accuracy based on various building configurations for area averaged and local loads are drawn that are useful for validation of computational approaches. CWE has rapidly evolved during the last decades and some representative studies from the literature and their methodologies are discussed. National and international codes/standards committees have initiated efforts to establish guidelines for practical use of CWE for estimation of wind-induced loads, on their upcoming versions. The endeavors from Eurocode and the Architectural Institute of Japan are analyzed. A novel state-of-the-art application is presented that has recently been developed by the authors, with good prospects to combine accuracy and efficiency for CWE. Results are within the threshold of accuracy established by comparisons between various wind tunnels, full scale data and code provisions.

Key Words
ABL; CFD; dynamic terrain; NBCC; wind induced loads; wind tunnel

Address
Theodore Potsis:Building, Civil and Environmental Engineering, Centre of Zero Building Energy Studies, Concordia University,
Montreal, QC, H3J 2W1, Canada

Ted Stathopoulos:Building, Civil and Environmental Engineering, Centre of Zero Building Energy Studies, Concordia University,
Montreal, QC, H3J 2W1, Canada

Abstract
The traditional approach for predicting wind pressures on the components and cladding (C&C) of roofs in low-rise buildings relies on directly measured peak wind pressure coefficients obtained from wind tunnel testing, as outlined in ASCE 7 22 and NBCC 2020. In contrast, EN 1991-1-4 recommends a constant gust effect factor (structural factor CsCd) of 1.0 for C&C wind loads on roof elements, with mean external pressure coefficients defined based on the loaded area. However, the actual gust effect factors of C&C wind loads remain underexplored. This study evaluated the gust effect factors of wind loads on C&C of low-rise buildings' roofs by analyzing their statistical properties. Aerodynamic data from the NIST database, obtained from the Boundary-Layer Wind Tunnel II at the University of Western Ontario, is utilized for the analysis. This study examines a range of low-slope low-rise building configurations with different roof heights and plan dimensions. The analysis explores key statistical properties of wind pressure coefficients, including mean, standard deviation, skewness, kurtosis, and peak factor, considering varying tributary areas and roof zone locations. In addition, this study assesses the background response factor, which quantifies the relationship between wind load fluctuations and upstream turbulence, along with the gust effect factors. The results reveal that the mean and standard deviation of wind pressures on C&C follow similar trends with effective wind area as observed for the peak wind pressures in ASCE 7-22 and NBCC 2020. Non-Gaussian features of wind pressures, indicated by high skewness and kurtosis, lead to large peak factors. The gust effect factors for tributary areas of 9 ft2 at corner zone (Zone 3), and edge zone (Zone 2) of roofs are approximately 1.5 across various building configurations. However, in the internal zone (Zone 1'), the gust effect factors for effective wind area less than 100 ft2 exhibit significant variability ranging from 1.0 to 2.5. In addition, models for gust effect factors with effective wind area are proposed for different roof zones in this study.

Key Words
components and cladding; gust effect factor; low-rise buildings; quasi-steady theory

Address
Jigar N Mokani:Department of Civil and Environmental Engineering, University of Western Ontario, London, N6A 5B9, London, ON, Canada

Jin Wang:Department of Civil and Environmental Engineering, University of Western Ontario, London, N6A 5B9, London, ON, Canada

Abstract
The roof configuration of a building is crucial in determining the flow patterns around structures, thereby affecting the aerodynamic loads on these structures. This study investigates the effects of roof slope on wind pressure correlations over roof surfaces using boundary-layer wind tunnel data from Tokyo Polytechnic University (TPU), Japan. Thirty-two building models, with a plan aspect ratio of 2:3, varying heights, and roof slopes ranging from 4.8° to 45° under suburban terrain, are analyzed in this study. The distribution of mean wind pressure coefficients is examined to characterize flow patterns over roofs, and wind pressure correlations are evaluated with the underlying mechanisms discussed. The results demonstrate that buildings with roof slopes below 9o exhibit similar flow patterns in terms of mean wind pressure coefficient distribution. For roof slopes between 9° and 18°, wind flow re-separates at the ridge and may reattach on the leeward roof. For roof slopes exceeding 18°, no reattachment is observed on the leeward roof. At roof slopes greater than 25°, positive pressures are observed across the windward roof. Within the separation bubble, the mean reattachment length (Xr) is a key parameter for characterizing the distribution of correlation coefficients of wind pressures. In addition, for wind perpendicular to the ridge, high correlation coefficients on both windward and leeward roofs are observed for steep roofs. On the leeward side, correlations approach 0.7 for roof slopes exceeding 27°. Under cornering winds at 45°, correlations in the windward corner regions increase with roof slope, reaching values above 0.9 for a 45° roof.

Key Words
aerodynamic; flow pattern; low-rise building; roof slope; wind pressure correlation

Address
Jin Wang:Department of Civil & Environment Engineering, University of Western Ontario, N6A 5B9, London, ON, Canada

Dong Guo:Department of Civil & Environment Engineering, University of Western Ontario, N6A 5B9, London, ON, Canada

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