Diesel railcars are widely used in rail transport, particularly in rural areas, because of their ability to operate without overhead power lines. However, the exhaust gas emitted by diesel railcars can cause soot stains on the car body surface, which requires regular cleaning. In this study, computational fluid dynamics (CFD) simulations were conducted to investigate the effects of roof equipment and exhaust pipe configurations on the exhaust flow around a car body. Unsteady flow analysis was performed using delayed detached eddy simulation. The exhaust flow from the exhaust pipe was simulated using a non-isothermal flow based on the Boussinesq approximation. The velocity profiles obtained by CFD were validated against wind tunnel test results. The CFD results showed that the exhaust gas emitted into a cavity consisting of roof equipment caused soot staining on the car body surface. This study proposes an appropriate location for the exhaust outlet, in which the flow velocity normalised to the train speed was higher than 0.7 to reduce soot stains on the surface.
{"title":"CFD analysis of exhaust flow for reducing soot stains on railcar body surfaces","authors":"Natsuki Harada, Yuhei Noguchi, Yuto Araki, Tokuzo Miyachi","doi":"10.1016/j.jweia.2026.106359","DOIUrl":"10.1016/j.jweia.2026.106359","url":null,"abstract":"<div><div>Diesel railcars are widely used in rail transport, particularly in rural areas, because of their ability to operate without overhead power lines. However, the exhaust gas emitted by diesel railcars can cause soot stains on the car body surface, which requires regular cleaning. In this study, computational fluid dynamics (CFD) simulations were conducted to investigate the effects of roof equipment and exhaust pipe configurations on the exhaust flow around a car body. Unsteady flow analysis was performed using delayed detached eddy simulation. The exhaust flow from the exhaust pipe was simulated using a non-isothermal flow based on the Boussinesq approximation. The velocity profiles obtained by CFD were validated against wind tunnel test results. The CFD results showed that the exhaust gas emitted into a cavity consisting of roof equipment caused soot staining on the car body surface. This study proposes an appropriate location for the exhaust outlet, in which the flow velocity normalised to the train speed was higher than 0.7 to reduce soot stains on the surface.</div></div>","PeriodicalId":54752,"journal":{"name":"Journal of Wind Engineering and Industrial Aerodynamics","volume":"270 ","pages":"Article 106359"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-09DOI: 10.1016/j.jweia.2026.106335
Zhong-Xu Tan , Le-Dong Zhu , Xiu-Yu Chen
This study investigates the transient aerodynamic pressures on a 3:2 rectangular prism under accelerating flow at a critical angle of attack of 10° using numerical simulations. Variational Mode Decomposition (VMD) is employed to decompose the pressure signals into three principal components: a time-varying mean component, an attenuated fluctuating component, and a stable fluctuating component. Proper Orthogonal Decomposition (POD) is further applied to characterize the spatiotemporal features of each component, revealing that the first two POD modes capture over 92 % of the total energy, with spatially invariant covariance modes across different flow accelerations. The results indicate that flow acceleration induces significant unsteady effects, including amplified pressure fluctuations on leeward surfaces and adjacent corner regions, as well as a reduced Strouhal number during acceleration. Flow field analysis shows that acceleration alters vortex shedding patterns, enhances flow separation, and thereby amplifies transient pressure fluctuation. These findings demonstrate that conventional quasi-steady theory is inadequate for predicting wind loads under accelerating flows, and that the proposed VMD–POD analytical framework provides an effective component-based methodology for the wind-resistant design of structures exposed to non-synoptic wind events.
{"title":"Flow acceleration effects on aerodynamic pressures of a 3:2 rectangular prism at critical angle of attack: A VMD–POD-based analysis","authors":"Zhong-Xu Tan , Le-Dong Zhu , Xiu-Yu Chen","doi":"10.1016/j.jweia.2026.106335","DOIUrl":"10.1016/j.jweia.2026.106335","url":null,"abstract":"<div><div>This study investigates the transient aerodynamic pressures on a 3:2 rectangular prism under accelerating flow at a critical angle of attack of 10° using numerical simulations. Variational Mode Decomposition (VMD) is employed to decompose the pressure signals into three principal components: a time-varying mean component, an attenuated fluctuating component, and a stable fluctuating component. Proper Orthogonal Decomposition (POD) is further applied to characterize the spatiotemporal features of each component, revealing that the first two POD modes capture over 92 % of the total energy, with spatially invariant covariance modes across different flow accelerations. The results indicate that flow acceleration induces significant unsteady effects, including amplified pressure fluctuations on leeward surfaces and adjacent corner regions, as well as a reduced Strouhal number during acceleration. Flow field analysis shows that acceleration alters vortex shedding patterns, enhances flow separation, and thereby amplifies transient pressure fluctuation. These findings demonstrate that conventional quasi-steady theory is inadequate for predicting wind loads under accelerating flows, and that the proposed VMD–POD analytical framework provides an effective component-based methodology for the wind-resistant design of structures exposed to non-synoptic wind events.</div></div>","PeriodicalId":54752,"journal":{"name":"Journal of Wind Engineering and Industrial Aerodynamics","volume":"270 ","pages":"Article 106335"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-15DOI: 10.1016/j.jweia.2026.106355
Wei-Chao Yang , Lun Zhao , Yi-Kang Liu , Hong He , E Deng
When the high-speed train passes through the tunnel, its rapid movement causes severe air disturbance, leading to complex and intense train slipstream effects. Under these slipstream conditions, the Overhead Electrification Catenary Support Structure (OESS) inside the tunnel inevitably interacts with the transient airflow through fluid-structure interaction, consequently inducing complex vibrational responses. This study investigates the dynamic response characteristics of OESS in high-speed railway tunnels under train-induced slipstream effects using a three-dimensional fluid-structure interaction model. The results demonstrate that the longitudinal aerodynamic loads dominate the structural response, inducing significantly higher displacements and accelerations compared to the lateral and vertical directions. Notably, it is found that shorter train formations generate more critical aerodynamic excitation than longer formations, producing higher dynamic responses and load magnitudes. Quantitative analysis reveals distinct power-law relationships between train speed and OESS response parameters, while tunnel cross-sectional area shows linear correlations. Aerodynamic loads distribute non-uniformly across OESS components, with the Mast Pole experiencing the highest load intensity and the Steady Arm the lowest. Mechanistic insight from flow field analysis demonstrates that the enhanced responses under shorter formations originate from substantially increased local wind speeds (by over 10 %), elevated turbulence intensity, and more pronounced vortex structures. These findings provide critical insights for the aerodynamic safety design and fatigue assessment of OESS in high-speed railway tunnels.
{"title":"Dynamic characteristics of overhead electrification catenary support structure in high-speed railway tunnel under train slipstream: A FSI simulation study","authors":"Wei-Chao Yang , Lun Zhao , Yi-Kang Liu , Hong He , E Deng","doi":"10.1016/j.jweia.2026.106355","DOIUrl":"10.1016/j.jweia.2026.106355","url":null,"abstract":"<div><div>When the high-speed train passes through the tunnel, its rapid movement causes severe air disturbance, leading to complex and intense train slipstream effects. Under these slipstream conditions, the Overhead Electrification Catenary Support Structure (OESS) inside the tunnel inevitably interacts with the transient airflow through fluid-structure interaction, consequently inducing complex vibrational responses. This study investigates the dynamic response characteristics of OESS in high-speed railway tunnels under train-induced slipstream effects using a three-dimensional fluid-structure interaction model. The results demonstrate that the longitudinal aerodynamic loads dominate the structural response, inducing significantly higher displacements and accelerations compared to the lateral and vertical directions. Notably, it is found that shorter train formations generate more critical aerodynamic excitation than longer formations, producing higher dynamic responses and load magnitudes. Quantitative analysis reveals distinct power-law relationships between train speed and OESS response parameters, while tunnel cross-sectional area shows linear correlations. Aerodynamic loads distribute non-uniformly across OESS components, with the Mast Pole experiencing the highest load intensity and the Steady Arm the lowest. Mechanistic insight from flow field analysis demonstrates that the enhanced responses under shorter formations originate from substantially increased local wind speeds (by over 10 %), elevated turbulence intensity, and more pronounced vortex structures. These findings provide critical insights for the aerodynamic safety design and fatigue assessment of OESS in high-speed railway tunnels.</div></div>","PeriodicalId":54752,"journal":{"name":"Journal of Wind Engineering and Industrial Aerodynamics","volume":"270 ","pages":"Article 106355"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-26DOI: 10.1016/j.jweia.2026.106360
Jessica J. van den Heuvel , Gregory A. Kopp
Although irregular plan shapes are common in modern construction, wind load provisions for low-rise buildings continue to treat simple rectangular buildings as the default case and offer little explicit guidance for irregular geometries. ASCE 7–22 suggests that standard rectangular-based wind load provisions conservatively envelope most irregular structures, though this assumption remains largely unvalidated for low-rise structures. This study presents wind tunnel results for a range of irregular building models tested under multiple wind directions, terrains, and geometries. Each configuration is compared directly to its equivalent rectangular footprint. Contrary to the prevailing assumptions, the results show that irregular shapes with reentrant corners can produce higher base shear and uplift loads than their rectangular counterparts. The increase in base shear is primarily driven by how reentrant corners shorten the effective distance between windward and leeward faces, leading to higher suction pressures on the leeward wall. Increased uplift is attributed to three main aerodynamic mechanisms: area effects, where similar pressures act over relatively larger portions of the roof on irregular shapes; windward wall effects, where recessed roof edges experience suction more akin to fully exposed windward edges; and corner effects, where the geometry produces multiple zones of high suction without increasing the peak value but resulting in greater overall uplift. These findings highlight the need for improved guidance on wind loading for irregular low-rise buildings with reentrant corners.
{"title":"Effects of reentrant corners on wind loads for non-rectangular-plan buildings","authors":"Jessica J. van den Heuvel , Gregory A. Kopp","doi":"10.1016/j.jweia.2026.106360","DOIUrl":"10.1016/j.jweia.2026.106360","url":null,"abstract":"<div><div>Although irregular plan shapes are common in modern construction, wind load provisions for low-rise buildings continue to treat simple rectangular buildings as the default case and offer little explicit guidance for irregular geometries. ASCE 7–22 suggests that standard rectangular-based wind load provisions conservatively envelope most irregular structures, though this assumption remains largely unvalidated for low-rise structures. This study presents wind tunnel results for a range of irregular building models tested under multiple wind directions, terrains, and geometries. Each configuration is compared directly to its equivalent rectangular footprint. Contrary to the prevailing assumptions, the results show that irregular shapes with reentrant corners can produce higher base shear and uplift loads than their rectangular counterparts. The increase in base shear is primarily driven by how reentrant corners shorten the effective distance between windward and leeward faces, leading to higher suction pressures on the leeward wall. Increased uplift is attributed to three main aerodynamic mechanisms: area effects, where similar pressures act over relatively larger portions of the roof on irregular shapes; windward wall effects, where recessed roof edges experience suction more akin to fully exposed windward edges; and corner effects, where the geometry produces multiple zones of high suction without increasing the peak value but resulting in greater overall uplift. These findings highlight the need for improved guidance on wind loading for irregular low-rise buildings with reentrant corners.</div></div>","PeriodicalId":54752,"journal":{"name":"Journal of Wind Engineering and Industrial Aerodynamics","volume":"270 ","pages":"Article 106360"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-17DOI: 10.1016/j.jweia.2025.106327
Yan-Jiao Guo , Xiang-Wei Min , Wen-Li Chen
{"title":"Corrigendum to “Flow-induced vibration and force characteristics of a downstream cylinder with two degrees of freedom influenced by upstream cylinder wake” [J. Wind Eng. Ind. Aerod. 265 (2025) 106163]","authors":"Yan-Jiao Guo , Xiang-Wei Min , Wen-Li Chen","doi":"10.1016/j.jweia.2025.106327","DOIUrl":"10.1016/j.jweia.2025.106327","url":null,"abstract":"","PeriodicalId":54752,"journal":{"name":"Journal of Wind Engineering and Industrial Aerodynamics","volume":"270 ","pages":"Article 106327"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-29DOI: 10.1016/j.jweia.2026.106362
Jack K. Wong, Oya Mercan, Paul J. Kushner
When applying large eddy simulation (LES) for wind-load assessment, simulating inflow turbulence characteristics in the atmospheric boundary layer (ABL) is crucial for achieving accurate results. Advances in divergence-free synthetic turbulence generators for ABL conditions have made LES more computationally affordable. However, empty-domain tests reveal systematic deviations between the synthetic turbulence and the prescribed profiles that can impact downstream loads. This study introduces a gradient-based iterative calibration workflow that simultaneously adjusts the mean velocity, turbulence intensities and integral length scales to reduce such discrepancies. Unlike approaches that calibrate individual components, the proposed method accounts for the interactions of turbulence quantities and corrects discrepancies caused by divergence-free and mass-flux corrections and turbulence dissipation, leading to more control over the incident flow. The method is applied to a tall-building case from the Tokyo Polytechnic University aerodynamic database for wind angles 0° and 45°. By calibrating the inflow at different locations, the effects of correctors and convection are quantified. For both wind angles, the effect of calibration is most pronounced on the windward pressure and drag coefficients. It substantially reduces the coefficient of variation of root-mean-square error (CVRMSE) of the standard deviation (STD) of windward pressure coefficients (e.g. 8 % to 1 % at 0°and 17 % to 1 % at 45°) and improves drag moment predictions. At 0°, the percentage error in the STD of drag moment coefficient changes from −26 % to +4 % and to −6 % for the respective calibrations. At 45°, the change is from −26 % to +19 % and −3 %, respectively.
{"title":"Calibration of synthetic inflow turbulence and its effects on the wind loads on a tall building","authors":"Jack K. Wong, Oya Mercan, Paul J. Kushner","doi":"10.1016/j.jweia.2026.106362","DOIUrl":"10.1016/j.jweia.2026.106362","url":null,"abstract":"<div><div>When applying large eddy simulation (LES) for wind-load assessment, simulating inflow turbulence characteristics in the atmospheric boundary layer (ABL) is crucial for achieving accurate results. Advances in divergence-free synthetic turbulence generators for ABL conditions have made LES more computationally affordable. However, empty-domain tests reveal systematic deviations between the synthetic turbulence and the prescribed profiles that can impact downstream loads. This study introduces a gradient-based iterative calibration workflow that simultaneously adjusts the mean velocity, turbulence intensities and integral length scales to reduce such discrepancies. Unlike approaches that calibrate individual components, the proposed method accounts for the interactions of turbulence quantities and corrects discrepancies caused by divergence-free and mass-flux corrections and turbulence dissipation, leading to more control over the incident flow. The method is applied to a tall-building case from the Tokyo Polytechnic University aerodynamic database for wind angles 0° and 45°. By calibrating the inflow at different locations, the effects of correctors and convection are quantified. For both wind angles, the effect of calibration is most pronounced on the windward pressure and drag coefficients. It substantially reduces the coefficient of variation of root-mean-square error (CVRMSE) of the standard deviation (STD) of windward pressure coefficients (e.g. 8 % to 1 % at 0°and 17 % to 1 % at 45°) and improves drag moment predictions. At 0°, the percentage error in the STD of drag moment coefficient changes from −26 % to +4 % and to −6 % for the respective calibrations. At 45°, the change is from −26 % to +19 % and −3 %, respectively.</div></div>","PeriodicalId":54752,"journal":{"name":"Journal of Wind Engineering and Industrial Aerodynamics","volume":"270 ","pages":"Article 106362"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-27DOI: 10.1016/j.jweia.2026.106363
Ruiqing Han , Teng Wu
The long-span bridge decks are susceptible to wind-induced vibrations due to their high flexibility and low damping. Considering the potential material savings by allowing the nonlinearity in structural elements under strong winds, the study is motivated by the recent performance-based wind design methodology to evaluate and understand the inelastic behaviors of long-span bridge decks at multiple buffeting performance levels. While the nonlinear time history analysis can offer very detailed wind structural response information, the required volume of computations is significant due to the long duration of windstorms. Hence, the static nonlinear analyses at multi-level wind hazards (i.e., wind buffeting pushover analysis) are explored in this study to efficiently provide adequate information on wind demands of the bridge deck and its components. To this end, the conventional equivalent static wind loads (ESWLs) for the linear elastic buffeting analysis is extended into the nonlinear inelastic regime, with the consideration of higher structural modes, inelastic behaviors, and multi-location responses. Inspired by the modal pushover analysis procedure for seismic demand evaluation and load-response-correlation method for wind load distribution estimation, the peak displacements at multiple bridge deck locations considering contributions from multiple modes and their coupling effects are first obtained using the pseudo-excitation method, and then the ESWLs are acquired using the displacement influence line. Furthermore, the structural characteristics (e.g., modal properties and displacement influence lines) are updated at each step of the pushover analysis to consider the effects of bridge deck inelastic behaviors on the ESWLs. A long-span truss bridge deck is employed as the case study to demonstrate the high accuracy and efficiency of the developed adaptive modal pushover analysis (AMPA) procedure for buffeting performance evaluation. Based on the inelastic behavior evolution of bridge deck elements with the increase of wind intensity, four buffeting performance levels are identified on the capacity curve. Finally, the sensitivity analysis is conducted to examine the contributions of multiple-mode and inelastic considerations to the wind demands estimated with AMPA.
{"title":"Adaptive modal pushover analysis for efficient buffeting performance evaluation of long-span bridge decks","authors":"Ruiqing Han , Teng Wu","doi":"10.1016/j.jweia.2026.106363","DOIUrl":"10.1016/j.jweia.2026.106363","url":null,"abstract":"<div><div>The long-span bridge decks are susceptible to wind-induced vibrations due to their high flexibility and low damping. Considering the potential material savings by allowing the nonlinearity in structural elements under strong winds, the study is motivated by the recent performance-based wind design methodology to evaluate and understand the inelastic behaviors of long-span bridge decks at multiple buffeting performance levels. While the nonlinear time history analysis can offer very detailed wind structural response information, the required volume of computations is significant due to the long duration of windstorms. Hence, the static nonlinear analyses at multi-level wind hazards (i.e., wind buffeting pushover analysis) are explored in this study to efficiently provide adequate information on wind demands of the bridge deck and its components. To this end, the conventional equivalent static wind loads (ESWLs) for the linear elastic buffeting analysis is extended into the nonlinear inelastic regime, with the consideration of higher structural modes, inelastic behaviors, and multi-location responses. Inspired by the modal pushover analysis procedure for seismic demand evaluation and load-response-correlation method for wind load distribution estimation, the peak displacements at multiple bridge deck locations considering contributions from multiple modes and their coupling effects are first obtained using the pseudo-excitation method, and then the ESWLs are acquired using the displacement influence line. Furthermore, the structural characteristics (e.g., modal properties and displacement influence lines) are updated at each step of the pushover analysis to consider the effects of bridge deck inelastic behaviors on the ESWLs. A long-span truss bridge deck is employed as the case study to demonstrate the high accuracy and efficiency of the developed adaptive modal pushover analysis (AMPA) procedure for buffeting performance evaluation. Based on the inelastic behavior evolution of bridge deck elements with the increase of wind intensity, four buffeting performance levels are identified on the capacity curve. Finally, the sensitivity analysis is conducted to examine the contributions of multiple-mode and inelastic considerations to the wind demands estimated with AMPA.</div></div>","PeriodicalId":54752,"journal":{"name":"Journal of Wind Engineering and Industrial Aerodynamics","volume":"270 ","pages":"Article 106363"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-21DOI: 10.1016/j.jweia.2026.106361
Hui Yu, Huibo Zhang
Accurate quantification of the spatiotemporal distribution of wind-driven rain (WDR) on building facades is critical yet restricted by the high computational cost of numerical simulations and the limited precision of semi-empirical methods. To address this, this study aims to develop a rapid and accurate machine learning framework for predicting facade-level WDR spatiotemporal distribution. A quantitative approach was employed where a comprehensive dataset, covering diverse meteorological conditions and building configurations, was generated through numerical simulations to train an artificial neural network (ANN) model. Additionally, a surface roughness correction model was derived using symbolic regression. Results indicate that inlet wind speed and vertical position strongly influence WDR intensity. The ANN model achieved excellent accuracy (R2 = 0.9998) and generalization (R2 ≥ 0.996), with a computational speedup of over 4700 times compared to CFD simulations. The correction model effectively captured roughness effects (R2 = 0.96). The framework's robustness and scalability were validated through a case study of a heritage building, demonstrating its utility in providing reliable boundary conditions for hygrothermal and durability analyses to support resilient building design.
{"title":"Rapid prediction of the spatiotemporal distribution of facade wind-driven rain using ANN and symbolic regression","authors":"Hui Yu, Huibo Zhang","doi":"10.1016/j.jweia.2026.106361","DOIUrl":"10.1016/j.jweia.2026.106361","url":null,"abstract":"<div><div>Accurate quantification of the spatiotemporal distribution of wind-driven rain (WDR) on building facades is critical yet restricted by the high computational cost of numerical simulations and the limited precision of semi-empirical methods. To address this, this study aims to develop a rapid and accurate machine learning framework for predicting facade-level WDR spatiotemporal distribution. A quantitative approach was employed where a comprehensive dataset, covering diverse meteorological conditions and building configurations, was generated through numerical simulations to train an artificial neural network (ANN) model. Additionally, a surface roughness correction model was derived using symbolic regression. Results indicate that inlet wind speed and vertical position strongly influence WDR intensity. The ANN model achieved excellent accuracy (R<sup>2</sup> = 0.9998) and generalization (R<sup>2</sup> ≥ 0.996), with a computational speedup of over 4700 times compared to CFD simulations. The correction model effectively captured roughness effects (R<sup>2</sup> = 0.96). The framework's robustness and scalability were validated through a case study of a heritage building, demonstrating its utility in providing reliable boundary conditions for hygrothermal and durability analyses to support resilient building design.</div></div>","PeriodicalId":54752,"journal":{"name":"Journal of Wind Engineering and Industrial Aerodynamics","volume":"270 ","pages":"Article 106361"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-07DOI: 10.1016/j.jweia.2025.106330
Haoqing Li , Yiming Zhang , Hao Wang , Yichao Xu , Dan Li
Long-span bridges usually suffer severe vibrations under extreme wind events (such as typhoons), potentially leading to engineering failures and traffic accidents. Data-driven approaches facilitate the mitigation of risks through timely and accurate prediction of typhoon effects. Deep learning (DL) algorithms, including the convolutional neural network (CNN), long short-term memory (LSTM), and their combined models, have been extensively applied in various fields. Despite the superior predictive performance of CNN-LSTM in time series, it fails to provide probabilistic estimates to quantify uncertainty and lacks adequate interpretability. In this work, a CNN-bidirectional LSTM-based explainable deep ensemble (CNN-BiLSTM-EDE) model is proposed for the probabilistic prediction of typhoon effects on long-span bridges. Specifically, CNN and BiLSTM are integrated to enhance the capability of processing spatiotemporal typhoon characteristics. A deep ensemble scheme is then adopted to modify the CNN-BiLSTM architecture, enabling dynamic response estimation within a probabilistic framework. The final prediction is obtained by averaging the results through ensemble learning. Shapley additive explanation (SHAP) is introduced to reveal the marginal contributions and substantive impacts of feature variables on the model predictions. Decade-long typhoon datasets of a kilometer-scale long-span bridge are utilized to validate the proposed model. The results indicate that CNN-BiLSTM-EDE provides reliable response predictions while quantifying uncertainty by offering corresponding conditional distribution. According to the SHAP visualization results, mean wind speed and wind direction angle are identified as the most influential factors in predicting typhoon effects. Compared with four probabilistic benchmark models, CNN-BiLSTM-EDE demonstrates superior prediction accuracy and uncertainty quantification performance.
{"title":"An explainable deep ensemble model for probabilistic prediction of typhoon effects on a long-span bridge","authors":"Haoqing Li , Yiming Zhang , Hao Wang , Yichao Xu , Dan Li","doi":"10.1016/j.jweia.2025.106330","DOIUrl":"10.1016/j.jweia.2025.106330","url":null,"abstract":"<div><div>Long-span bridges usually suffer severe vibrations under extreme wind events (such as typhoons), potentially leading to engineering failures and traffic accidents. Data-driven approaches facilitate the mitigation of risks through timely and accurate prediction of typhoon effects. Deep learning (DL) algorithms, including the convolutional neural network (CNN), long short-term memory (LSTM), and their combined models, have been extensively applied in various fields. Despite the superior predictive performance of CNN-LSTM in time series, it fails to provide probabilistic estimates to quantify uncertainty and lacks adequate interpretability. In this work, a CNN-bidirectional LSTM-based explainable deep ensemble (CNN-BiLSTM-EDE) model is proposed for the probabilistic prediction of typhoon effects on long-span bridges. Specifically, CNN and BiLSTM are integrated to enhance the capability of processing spatiotemporal typhoon characteristics. A deep ensemble scheme is then adopted to modify the CNN-BiLSTM architecture, enabling dynamic response estimation within a probabilistic framework. The final prediction is obtained by averaging the results through ensemble learning. Shapley additive explanation (SHAP) is introduced to reveal the marginal contributions and substantive impacts of feature variables on the model predictions. Decade-long typhoon datasets of a kilometer-scale long-span bridge are utilized to validate the proposed model. The results indicate that CNN-BiLSTM-EDE provides reliable response predictions while quantifying uncertainty by offering corresponding conditional distribution. According to the SHAP visualization results, mean wind speed and wind direction angle are identified as the most influential factors in predicting typhoon effects. Compared with four probabilistic benchmark models, CNN-BiLSTM-EDE demonstrates superior prediction accuracy and uncertainty quantification performance.</div></div>","PeriodicalId":54752,"journal":{"name":"Journal of Wind Engineering and Industrial Aerodynamics","volume":"269 ","pages":"Article 106330"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-07DOI: 10.1016/j.jweia.2026.106332
Ahmed M. Maky , Djordje Romanic , Matiyas A. Bezabeh
Most studies on structural response under downburst wind loads rely on a single downburst model or limited wind measurements. Uncertainties in key modeling parameters further complicate accurate response predictions. Furthermore, there are no standard formulations for power spectral density (PSD) or coherence functions specific to downburst turbulence characteristics. As a first step toward developing a probabilistic uncertainty quantification framework, this study examines the impact of various modeling assumptions and parameter uncertainties on structural response, utilizing the CAARC building as a testbed. A hazard analysis was conducted to predict the design-level downburst wind speed based on NOAA database records over 45 years. A global sensitivity analysis was utilized to rank the influence of uncertain modeling parameters. The results indicate that the structural response is mainly sensitive to maximum wind speed in the vertical profile. Other moderately influential parameters include the stagnation region radius, the building's position relative to the downburst center, and the mean turbulence intensity. While different vertical wind profile models affect the mean responses, they have minimal impact on the response probability distribution. In contrast, the coherence function significantly affects the probability distribution of maximum building drift, whereas variations in PSD functions have negligible effects.
{"title":"Sensitivity analysis of structural response to thunderstorm downburst models","authors":"Ahmed M. Maky , Djordje Romanic , Matiyas A. Bezabeh","doi":"10.1016/j.jweia.2026.106332","DOIUrl":"10.1016/j.jweia.2026.106332","url":null,"abstract":"<div><div>Most studies on structural response under downburst wind loads rely on a single downburst model or limited wind measurements. Uncertainties in key modeling parameters further complicate accurate response predictions. Furthermore, there are no standard formulations for power spectral density (PSD) or coherence functions specific to downburst turbulence characteristics. As a first step toward developing a probabilistic uncertainty quantification framework, this study examines the impact of various modeling assumptions and parameter uncertainties on structural response, utilizing the CAARC building as a testbed. A hazard analysis was conducted to predict the design-level downburst wind speed based on NOAA database records over 45 years. A global sensitivity analysis was utilized to rank the influence of uncertain modeling parameters. The results indicate that the structural response is mainly sensitive to maximum wind speed in the vertical profile. Other moderately influential parameters include the stagnation region radius, the building's position relative to the downburst center, and the mean turbulence intensity. While different vertical wind profile models affect the mean responses, they have minimal impact on the response probability distribution. In contrast, the coherence function significantly affects the probability distribution of maximum building drift, whereas variations in PSD functions have negligible effects.</div></div>","PeriodicalId":54752,"journal":{"name":"Journal of Wind Engineering and Industrial Aerodynamics","volume":"269 ","pages":"Article 106332"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号