Persian
Language
Geographical Research is Published in Persian Fulltext.
Article Type:
Original Research
Subject:
History
Received: 2025/02/17
Accepted: 2025/03/17
Accepted: 2025/03/17
Rights and permissions
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
1- Department of Urban Development, Science and Research Branch, Islamic Azad University, Tehran, Iran
2- Department of Urban Development, Science and Research Branch, Islamic Azad University, Tehran, Iran ,h.zabihi@srbiau.ac.ir
3- Department of Urban Development, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2- Department of Urban Development, Science and Research Branch, Islamic Azad University, Tehran, Iran ,
3- Department of Urban Development, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Abstract (107 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. Artificial Intelligence, particularly machine learning and evolutionary algorithms, is becoming an essential tool in parametric urban design. 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 are first examined. Then, based on the gathered information, the variables under study are identified. Subsequently, a set of questions and items were developed to measure each of the variables, 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. For example, 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. Additionally, the results of this research show that the use of artificial intelligence in parametric urban design can assist designers in creating smarter, more adaptable designs that align with citizens' needs while minimizing environmental negative impacts.
Methodology: In this paper, using a descriptive-analytical research method, the main concepts of parametric urban design and artificial intelligence are first examined. Then, based on the gathered information, the variables under study are identified. Subsequently, a set of questions and items were developed to measure each of the variables, 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. For example, 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. Additionally, the results of this research show that the use of artificial intelligence in parametric urban design can assist designers in creating smarter, more adaptable designs that align with citizens' needs while minimizing environmental negative impacts.
Keywords: