Machine Learning for Pedestrian-Level Wind Comfort Analysis

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dc.contributor.authorGür M.
dc.contributor.authorKaradag I.
dc.date.accessioned2024-07-22T08:01:11Z
dc.date.available2024-07-22T08:01:11Z
dc.date.issued2024
dc.description.abstract(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 of research on the comfort of pedestrians in terms of wind conditions with respect to building design, particularly in historic sites. (2) Objectives: This research aims to evaluate ML- and computational fluid dynamics (CFD)-based pedestrian wind comfort (PWC) analysis outputs using a novel method that relies on the sophisticated handling of image data. The goal is to propose a novel assessment method to enhance the efficiency of AI models over different urban scenarios. (3) Methodology: The stages include the analysis of climate data, CFD analysis with OpenFOAM, ML analysis using Autodesk Forma, and comparisons of the CFD and ML results using a novel image similarity assessment method based on the SSIM, MSE, and PSNR metrics. (4) Conclusions: This study effectively demonstrates the considerable potential of utilizing ML as a supplementary tool for evaluating PWC. It maintains a high degree of accuracy and precision, allowing for rapid and effective assessments. The methodology for precise comparison of two visual outputs in the absence of numerical data allows for more objective and pertinent comparisons, as it eliminates any potential distortions. (5) Recommendations: Additional research can explore the integration of ML models with climate data and different case studies, thus expanding the scope of wind comfort studies. © 2024 by the authors.
dc.identifier.DOI-ID10.3390/buildings14061845
dc.identifier.issn20755309
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11359
dc.language.isoEnglish
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rightsAll Open Access; Gold Open Access
dc.subjectArchitectural design
dc.subjectClimate models
dc.subjectComputational fluid dynamics
dc.subjectData handling
dc.subjectImage analysis
dc.subjectStructural design
dc.subjectArtificial intelligence learning
dc.subjectBuilding design
dc.subjectClimate data
dc.subjectComfort analysis
dc.subjectCultural heritages
dc.subjectMachine learning techniques
dc.subjectMachine-learning
dc.subjectPedestrian wind comfort
dc.subjectWind conditions
dc.subjectWind engineering
dc.subjectMachine learning
dc.titleMachine Learning for Pedestrian-Level Wind Comfort Analysis
dc.typeArticle

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