Integrated model of commercial complex design in modern urban planning using artificial intelligence based on the teachings of Iranian markets and industry 6
Subject Areas :Reza Torkaman 1 , Seyed Mehdi Abtahi 2 , Zahra Rezaei 3 , Ahmad Torkaman 4
1 - Department of Management, Na. C., Islamic Azad University, Najafabad, Islamic Azad University, Najafabad, Iran
2 - Department of Management, Marv. C., Islamic Azad University, Marvdasht, Iran
3 - Department of Computer Engineering, Marv. C., Islamic Azad University, Marvdasht, Iran
4 - Department of Architecture Engineering, Marv. C., Islamic Azad University, Marvdasht, Iran
Keywords: Commercial Complex Design, Artificial Intelligence, Iranian Architecture, Industry 6, Layout Optimization,
Abstract :
Commercial complexes, as key pillars of urban development, play a crucial role in fostering economic and social interactions. By creating spaces for commerce and engagement, they contribute to the formation of economic hubs in cities. With the advancement of modern technologies and the emergence of Industry 6, like artificial intelligence (AI), blockchain, and more, the need for transformation in the design and management of these complexes has become evident. Despite the impact of new technologies on increasing the efficiency and flexibility of commercial complexes, the integration of Iranian architectural principles with AI algorithms for improving the design and management has received little attention. This study aims to present a model for designing commercial complexes by utilizing AI and Iranian architectural principles to enhance efficiency, create rooted spaces, and meet future needs. The research falls under mixed-method and applied studies, employing artificial neural network modeling to optimize the arrangement of spatial units and layouts. Architectural data and constraints incorporate into the model, and through AI algorithms, an optimized model was developed. The results showed that the proposed model, with 92% accuracy, outperformed other algorithms. The combination of traditional architectural principles with AI has led to the creation of unique and flexible spaces that, in addition to aligning with contemporary needs and preserving cultural identity, enhance the sustainability of commercial complexes. Moreover, they can adapt to future technologies and Industry 6. The model can serve as a guideline for designers and commercial complex managers to improve efficiency and adaptability to urban needs.
Extended Abstract
Introduction
Despite advances in modern technologies, traditional architectural concepts, especially those inspired by Iranian Bazaars, still have significant value in organizing space and enhancing the user experience.
This study aims to integrate Industry 6.0 technologies with traditional Iranian architectural principles to create an optimized model for designing commercial complexes. The primary objective is to address challenges such as creating flexible, adaptive, and efficient spaces while preserving cultural heritage and meeting contemporary urban needs. Despite notable advancements in AI-driven design tools, a gap remains in utilizing culturally rich design principles alongside modern technology. This research seeks to bridge that gap by offering innovative solutions that enhance both functionality and cultural identity.
Methodology
To determine spatial solutions in commercial complexes, it is essential to identify spaces where commercial activities occur, including indoor spaces, covered areas, linear routes, and large open spaces. AI-driven platforms can define roles and incorporate data such as climate, urban location, complex size, development phases, and factors influencing profitability. These inputs guide AI algorithms to propose optimal spatial layouts and volumetric designs.
Let's teach the model our mental principles and patterns, the previous data is known
- Using synthetic data and presenting the data with specific rules
- Training the model with supervised learning based on manual labels
- Using rule-based methods (Rule-Based Systems): A set of rules is defined, each of which is suitable for each layout and set of features. For example, if the number of cells is more than 50 and the area of the fields is a certain value, a linear layout is selected, etc.
- Training the model with feedback (Reinforcement Learning) in complex qualities and changes, from a reinforcement model.
(Learning from hidden patterns and from existing data will determine the possibility of a suitable layout based on the input data of each complex, which is a kind of proposed research model).
This research focuses on designing and optimizing spatial layouts using AI, specifically Artificial Neural Networks (ANN). The study employs a mathematical modeling approach to predict optimal spatial arrangements. The dataset includes architectural elements and environmental data from 50 commercial complexes, later expanded to 1,000 entries through data augmentation techniques. Key elements such as corridors, shop configurations, and architectural features like four-way intersections and courtyards were extracted from traditional Iranian bazaars to inform the AI model.
The ANN model was structured with an input layer containing 12 neurons representing architectural elements (e.g., corridors, shops, four-way intersections). The hidden layers—comprising 8 and 6 neurons respectively—used ReLU activation functions to process spatial relationships. The output layer, with 8 neurons and a softmax activation function, predicted different spatial configurations, including linear, central courtyard, and cross-sectional layouts.
Results and Discussion
Key findings include: Optimized Spatial Utilization: AI-driven layout optimization maximized space efficiency. Traditional elements, such as courtyards, were reimagined as multi-functional hubs. The ANN model accurately predicted foot traffic patterns, reducing congestion and improving user flow.
Flexibility and Adaptability: Leveraging IoT sensors and blockchain enabled real-time spatial adjustments based on user preferences and occupancy data. This adaptability ensured optimal space utilization and enhanced user satisfaction.
Cultural Identity Preservation: The model successfully integrated traditional Iranian architectural elements, such as segmented spaces and intricate facades, into modern designs. This approach received positive feedback from stakeholders, emphasizing the potential for cultural preservation in contemporary commercial spaces.
Sustainability and resource efficiency: AI simulations reduce energy consumption and operating costs, aligning with sustainable design goals.
User Experience Enhancement: The AI model prioritized user-centric design, leveraging IoT data for personalized navigation, dynamic signage, and smart facilities. Flexible spaces for community events further enhanced user engagement and satisfaction.
Challenges and Considerations: The implementation of advanced technologies required significant initial investment and technical expertise. Balancing modern functionality with traditional aesthetics posed additional design challenges. Future research should focus on cost-effective solutions and advanced optimization techniques, such as hyperparameter tuning and deeper network architectures.
The ANN achieved a 92% accuracy rate in predicting optimal layouts, demonstrating its potential as a powerful tool for spatial optimization. Comparisons with other machine learning algorithms, such as support vector machines (SVM) and k-nearest neighbors (KNN), revealed superior performance, especially in handling complex layouts.
Conclusion
This study concludes that integrating AI with traditional Iranian architectural principles provides a promising strategy for designing and managing commercial complexes in the Industry 6.0 era. The hybrid model not only enhances spatial efficiency and adaptability but also preserves cultural identity. By leveraging AI-driven insights and incorporating elements from Iranian bazaars, the proposed model addresses the evolving needs of modern urban spaces.
The findings emphasize the importance of interdisciplinary collaboration, where technology and tradition intersect to create innovative solutions. As commercial complexes continue to play a critical role in urban development, adopting such models can drive economic growth, improve user experiences, and foster cultural preservation. Future research should explore refining the model, assessing its scalability, and applying it to various types of commercial spaces. Combining AI with architectural simulations can yield even better outcomes, ensuring resilient, efficient, and culturally meaningful designs that meet the demands of the 21st century.
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