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Volume 40, Issue 1 (2025)
GeoRes 2025, 40(1): 41-52 |
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Received: 2025/02/17 | Accepted: 2025/03/15 | Published: 2025/03/21
Received: 2025/02/17 | Accepted: 2025/03/15 | Published: 2025/03/21
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Manuchehri A, Zabihi H, Zarabadi Z. Role of Artificial Intelligence in Optimizing and Enhancing the Accuracy of Parametric Urban Planning and Design. GeoRes 2025; 40 (1) :41-52
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URL: http://georesearch.ir/article-1-1689-en.html
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1- Department of Urban Development, Science and Research Branch, Islamic Azad University, Tehran, Iran
Abstract (487 Views)
Aims: Urban design optimization techniques are constantly evolving and advancing to meet ever-changing needs. Since urban design is a multidimensional field due to complex human, social, economic, and environmental interactions, it requires innovative approaches. Accordingly, this study aims to present a model for creating flexible and responsive urban spaces that meet various needs by utilizing complex algorithms to provide this possibility.
Methodology: In this paper, using a descriptive-analytical research method, the main concepts of parametric urban design and artificial intelligence were first examined. Then, based on the gathered information, the study parameters were identified. Subsequently, a set of questions and items were developed to measure each of the parameters, which formed the structure for interviews with 16 subject matter experts. In the next step, the identified outputs were used as input data, and the results obtained in the context of structural interpretive modeling provided the foundation for formulating the final model.
Findings: In this project, machine learning algorithms were used to optimize the impact of various indicators on urban spaces. Based on data analysis and the prediction of human behaviors, social indicators had the most significant impact on environmental changes and information reception, followed by physical changes with a considerable gap.
Conclusion: The presence of policies and defined standards in the demographic-social aspect has the greatest impact on other components.
Methodology: In this paper, using a descriptive-analytical research method, the main concepts of parametric urban design and artificial intelligence were first examined. Then, based on the gathered information, the study parameters were identified. Subsequently, a set of questions and items were developed to measure each of the parameters, which formed the structure for interviews with 16 subject matter experts. In the next step, the identified outputs were used as input data, and the results obtained in the context of structural interpretive modeling provided the foundation for formulating the final model.
Findings: In this project, machine learning algorithms were used to optimize the impact of various indicators on urban spaces. Based on data analysis and the prediction of human behaviors, social indicators had the most significant impact on environmental changes and information reception, followed by physical changes with a considerable gap.
Conclusion: The presence of policies and defined standards in the demographic-social aspect has the greatest impact on other components.
Keywords:
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