Visual Discomfort in the Built Environment: Leveraging Generative AI and Computational Analysis to Evaluate Predicted Visual Stress in Architectural Façades
The built environment is increasingly recognized as a critical determinant of human health, profoundly influencing neurophysiological and psychological well-being. Previous studies show that specific visual patterns can elicit cortical hyperexcitation and visual discomfort, particularly in individua...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2075-5309/15/13/2208 |
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| author | Cleo Valentine Arnold J. Wilkins Heather Mitcheltree Olivier Penacchio Bruce Beckles Ian Hosking |
| author_facet | Cleo Valentine Arnold J. Wilkins Heather Mitcheltree Olivier Penacchio Bruce Beckles Ian Hosking |
| author_sort | Cleo Valentine |
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| description | The built environment is increasingly recognized as a critical determinant of human health, profoundly influencing neurophysiological and psychological well-being. Previous studies show that specific visual patterns can elicit cortical hyperexcitation and visual discomfort, particularly in individuals with a predisposition to cortical hyperexcitability. However, traditional approaches to examining visual stress have yet to capture the complexity of ways in which the built environment may contribute to visual discomfort. This study presents a novel, integrated analytical methodology that merges generative artificial intelligence (using Midjourney v6.1) with advanced Fourier-based computational analysis to quantify the impact of architectural façades on visual stress. By systematically varying contrast ratios, pattern periodicity, spatial frequency distribution, stylistic elements, and geometric curvature across nine façade designs, the research generated a diverse array of stimuli that were then analyzed using the Visual Stress Analysis Tool (ViStA). This tool employs Fourier spatial frequency decomposition to extract key metrics that are proxy indicators of potential cortical stress responses. The results revealed that façades with regularly spaced elements at approximately three cycles per degree exhibited the highest stress metrics, particularly when combined with high contrast ratios and consistent repetition. Vertical wooden slats and vertical metal screening elements produced the most pronounced indicators of visual stress, while more varied geometric compositions demonstrated substantially lower stress metrics. This methodology offers a scalable, reproducible approach for the evaluation of visual stress. The framework lays the groundwork for developing a more robust evidence base to support architectural design decision-making that proactively addresses the health impacts of the built environment. |
| format | Article |
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| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-3aad9777ea44444c978982ad785b4e0b2025-08-20T02:35:47ZengMDPI AGBuildings2075-53092025-06-011513220810.3390/buildings15132208Visual Discomfort in the Built Environment: Leveraging Generative AI and Computational Analysis to Evaluate Predicted Visual Stress in Architectural FaçadesCleo Valentine0Arnold J. Wilkins1Heather Mitcheltree2Olivier Penacchio3Bruce Beckles4Ian Hosking5Department of Architecture, University of Cambridge, Cambridge CB2 1PX, UKDepartment of Psychology, University of Essex, Colchester CO4 3SQ, UKDepartment of Architecture, University of Cambridge, Cambridge CB2 1PX, UKComputer Vision Center, Universitat Autònoma de Barcelona, 08193 Bellaterra, SpainUniversity Information Services, University of Cambridge, Cambridge CB2 1PX, UKDepartment of Engineering, University of Cambridge, Cambridge CB2 1PX, UKThe built environment is increasingly recognized as a critical determinant of human health, profoundly influencing neurophysiological and psychological well-being. Previous studies show that specific visual patterns can elicit cortical hyperexcitation and visual discomfort, particularly in individuals with a predisposition to cortical hyperexcitability. However, traditional approaches to examining visual stress have yet to capture the complexity of ways in which the built environment may contribute to visual discomfort. This study presents a novel, integrated analytical methodology that merges generative artificial intelligence (using Midjourney v6.1) with advanced Fourier-based computational analysis to quantify the impact of architectural façades on visual stress. By systematically varying contrast ratios, pattern periodicity, spatial frequency distribution, stylistic elements, and geometric curvature across nine façade designs, the research generated a diverse array of stimuli that were then analyzed using the Visual Stress Analysis Tool (ViStA). This tool employs Fourier spatial frequency decomposition to extract key metrics that are proxy indicators of potential cortical stress responses. The results revealed that façades with regularly spaced elements at approximately three cycles per degree exhibited the highest stress metrics, particularly when combined with high contrast ratios and consistent repetition. Vertical wooden slats and vertical metal screening elements produced the most pronounced indicators of visual stress, while more varied geometric compositions demonstrated substantially lower stress metrics. This methodology offers a scalable, reproducible approach for the evaluation of visual stress. The framework lays the groundwork for developing a more robust evidence base to support architectural design decision-making that proactively addresses the health impacts of the built environment.https://www.mdpi.com/2075-5309/15/13/2208visual stressallostatic loadgenerative AIarchitectural neurophysiologycortical hyperexcitabilityneural processing |
| spellingShingle | Cleo Valentine Arnold J. Wilkins Heather Mitcheltree Olivier Penacchio Bruce Beckles Ian Hosking Visual Discomfort in the Built Environment: Leveraging Generative AI and Computational Analysis to Evaluate Predicted Visual Stress in Architectural Façades Buildings visual stress allostatic load generative AI architectural neurophysiology cortical hyperexcitability neural processing |
| title | Visual Discomfort in the Built Environment: Leveraging Generative AI and Computational Analysis to Evaluate Predicted Visual Stress in Architectural Façades |
| title_full | Visual Discomfort in the Built Environment: Leveraging Generative AI and Computational Analysis to Evaluate Predicted Visual Stress in Architectural Façades |
| title_fullStr | Visual Discomfort in the Built Environment: Leveraging Generative AI and Computational Analysis to Evaluate Predicted Visual Stress in Architectural Façades |
| title_full_unstemmed | Visual Discomfort in the Built Environment: Leveraging Generative AI and Computational Analysis to Evaluate Predicted Visual Stress in Architectural Façades |
| title_short | Visual Discomfort in the Built Environment: Leveraging Generative AI and Computational Analysis to Evaluate Predicted Visual Stress in Architectural Façades |
| title_sort | visual discomfort in the built environment leveraging generative ai and computational analysis to evaluate predicted visual stress in architectural facades |
| topic | visual stress allostatic load generative AI architectural neurophysiology cortical hyperexcitability neural processing |
| url | https://www.mdpi.com/2075-5309/15/13/2208 |
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