The effect of ribs on wind load characteristics of cylindrical structures in streamwise sinusoidal flow: A numerical investigation

Physics of Fluids, Год журнала: 2025, Номер 37(2)

Опубликована: Фев. 1, 2025

This study examines the aerodynamic effects of streamwise sinusoidal flow on circular and ribbed cylinders using large-eddy simulation. Six cases with varying oscillation frequencies are analyzed to assess their impact forces wake dynamics. The results reveal that increasing frequency leads a rise in both drag lift coefficients at low frequencies, followed by sharp decline higher frequencies. Notably, (RC) exhibit mean lower root square fluctuations compared (CC) high Strouhal number for RC is also narrower, indicating less efficient characteristics under same conditions. Streamwise significantly alters structure, particularly fu/fst exceeding 1, peak wind pressure occurring = 2. shows complex fluctuations, especially windward side, though trend mirrors CC. For CC RC, vortex shedding suppressed complete cessation observed 2, corresponding coefficients. Dynamic mode decomposition analysis highlights low-frequency more coherent shedding, whereas cause street become organized. weaker pulsations, contributing its reduced greater stability. Overall, demonstrates configurations influence load behavior. offers superior stability high-turbulence flows, suggesting potential optimizing wind-resistant structural designs.

Язык: Английский

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An investigation using resampling techniques and explainable machine learning to minimize fire losses in residential buildings

Journal of Building Engineering, Год журнала: 2024, Номер 95, С. 110080 - 110080

Опубликована: Июль 4, 2024

Язык: Английский

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Additive-feature-attribution methods: A review on explainable artificial intelligence for fluid dynamics and heat transfer

International Journal of Heat and Fluid Flow, Год журнала: 2024, Номер 112, С. 109662 - 109662

Опубликована: Дек. 9, 2024

Язык: Английский

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Machine Learning for Pedestrian-Level Wind Comfort Analysis

Buildings, Год журнала: 2024, Номер 14(6), С. 1845 - 1845

Опубликована: Июнь 18, 2024

(1) Background: Artificial intelligence (AI) and machine learning (ML) techniques are being more widely employed in the field of wind engineering. Nevertheless, there is a scarcity research on comfort pedestrians terms conditions with respect to building design, particularly historic sites. (2) Objectives: This aims evaluate ML- computational fluid dynamics (CFD)-based pedestrian (PWC) analysis outputs using novel method that relies sophisticated handling image data. The goal propose assessment enhance efficiency AI models over different urban scenarios. (3) Methodology: stages include climate data, CFD OpenFOAM, ML Autodesk Forma, comparisons results similarity based SSIM, MSE, PSNR metrics. (4) Conclusions: study effectively demonstrates considerable potential utilizing as supplementary tool for evaluating PWC. It maintains high degree accuracy precision, allowing rapid effective assessments. methodology precise comparison two visual absence numerical data allows objective pertinent comparisons, it eliminates any distortions. (5) Recommendations: Additional can explore integration case studies, thus expanding scope studies.

Язык: Английский

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Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings

Alexandria Engineering Journal, Год журнала: 2024, Номер 111, С. 610 - 627

Опубликована: Ноя. 11, 2024

Язык: Английский

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Identification of the formation temperature field by Explainable Artificial Intelligence: A case study of Songyuan City, China

Energy, Год журнала: 2025, Номер unknown, С. 135172 - 135172

Опубликована: Фев. 1, 2025

Язык: Английский

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Machine Learning for Wind Speed Estimation

Buildings, Год журнала: 2025, Номер 15(9), С. 1541 - 1541

Опубликована: Май 2, 2025

For more than two decades, computational analysis has been pivotal in expanding architectural capabilities, enabling sustainable design through detailed environmental analysis. Central to creating environments is the profound understanding of wind dynamics, which significantly influence comfort levels around buildings. Traditionally, tunnel experiments, situ measurements, and fluid dynamics (CFD) simulations have employed assess speeds urban settings. However, advent machine learning (ML) introduced innovative methodologies that extend beyond these conventional approaches, offering new insights applications design. This study focuses on evaluating pedestrian-level using ML techniques, with a comparative against traditional measurements CFD simulations. Our findings reveal can predict sufficient accuracy for preliminary phases. One primary challenges addressed integration visual outputs from models quantitative data, necessary step enhance model reliability applicability. By developing novel techniques this integration, our research marks significant contribution field, benchmarking effectiveness established methods. The results validate model’s capability accurately estimate speeds, thereby supporting comfortable environments.

Язык: Английский

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Intelligent evaluation of interference effects between tall buildings based on wind tunnel experiments and explainable machine learning

Journal of Building Engineering, Год журнала: 2024, Номер 96, С. 110449 - 110449

Опубликована: Авг. 13, 2024

Язык: Английский

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Screening for Anomalous Safety Condition Among Existing Buildings Using Explainable Machine Learning

Structural Control and Health Monitoring, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

To ensure a safe environment for occupants, evaluating the physical status and service performance of existing buildings is essential. However, large‐scale building condition assessment usually relies on expertise judgment inspectors, which can be costly laborious due to unclear priorities, ambiguous procedures, ineffective operations. address these challenges, this study proposes an explainable machine learning‐based screening model anomalous safety among buildings, narrowing down scope requiring further detailed inspection monitoring. Initially, imbalanced dataset 18,090 survey reports unsafe labels collected. Then, synthetic minority oversampling technique (SMOTE) conducted balance dataset. Subsequently, seven learning models are trained utilizing 10‐fold cross‐validation with grid search. Findings reveal that, based balanced dataset, ensemble significantly better than that individual models. Specifically, XGBoost achieves highest performance, macro‐F1 98.49%, G‐mean value accuracy 98.49%. The final predictive (the SMOTE‐based model) explained using SHapley Additive exPlanations (SHAP). Service year, structure, location three most important features influencing structural safety. This represents promising approach automated optimizing resource allocation, enhancing effectiveness in decision‐making construction maintenance.

Язык: Английский

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A Systematic Review on Utilizing Artificial Intelligence in Lateral Resisting Systems of Buildings

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Апрель 27, 2025

Язык: Английский

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