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A Novel Optimization Approach for Revolutionizing Architectural Design in Chinese Cultural Heritage

Xueyong Li, Xiaoqing Yang

Abstract


The preservation of China's cultural heritage architecture, which combines contemporary and ancient building techniques, is difficult because of the aesthetic and structural degradation that has overtaken it. This architecture is a testament to the country's technical, artistic, and cultural achievements. A smokescreen with a resolution of 5192 × 4153 pixels was used to acquire surface photographs and ground shots of the Dazu Rock Carvings, Nanchan Temple, and Foguang Temple using the Microtrans Maryland 4-1000 program. The research aims to improve fault analysis in images of Chinese cultural heritage structures using an Ensemble Ant Colony Fused Convolutional Capsule Neural Network (EAC-CCNN). Then, using a combination of Augmented Reality (AR) and Building Information Modeling (BIM), the designing model for safety management and decision-making will be enhanced. Steps include collecting and annotating data, developing a hybrid EAC-CCNN model to probe the issue with the architectural building, training the model, connecting it with BIM, inspecting the site, and then analyzing the defects using augmented reality (AR) enhanced BIM models. The results show that this integrated approach works to increase the accuracy of defect identification, promote cooperation, and help maintain and preserve cultural heritage assets. The machine learning model's ability to detect and classify defects in buildings that are considered part of China's cultural heritage is evaluated using metrics such as accuracy and F1 score. “With an F1 Score of 95.47% and an accuracy of 93.29%, the architectural design fault identification and safety management model produces respectable results. Phases of training, validation, and testing measure performance in relation to project objectives. Using this approach, machine learning models may be taught to see patterns, fix errors, and make wise predictions under different conditions.

 

Doi: 10.28991/HIJ-2025-06-01-011

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Keywords


Architectural Design; Cultural Heritage; Building Information Modeling (BIM); Machine Learning; Ensemble Ant Colony Fused Convolutional Capsule Neural Network; Augmented Reality (AR).

References


Luo, H., & Chiou, B. S. (2021). Framing the hierarchy of cultural tourism attractiveness of Chinese historic districts under the premise of landscape conservation. Land, 10(2), 1–21. doi:10.3390/land10020216.

Gunawan, E., Kusbiantoro, K., & Kustedja, S. (2022). Transformation of a Chinese Cultural Heritage House in Bandung: Towards Sustainability. Proceedings of the 1st International Conference on Emerging Issues in Humanity Studies and Social Sciences - ICE-HUMS, 285–293. doi:10.5220/0010751000003112.

Petti, L., Trillo, C., & Makore, B. N. Cultural heritage and sustainable development targets: a possible harmonization. Insights from the European Perspective. Sustainability, 12(3), 926. doi:10.3390/su12030926.

Liu, Y., Wang, Y., Dupre, K., & McIlwaine, C. (2022). The impacts of world cultural heritage site designation and heritage tourism on community livelihoods: A Chinese case study. Tourism Management Perspectives, 43, 100994. doi:10.1016/j.tmp.2022.100994.

Gao, J., Zhang, C., Zhou, X., & Cao, R. (2021). Chinese tourists’ perceptions and consumption of cultural heritage: a generational perspective. Asia Pacific Journal of Tourism Research, 26(7), 719–731. doi:10.1080/10941665.2021.1908382.

Skublewska-Paszkowska, M., Milosz, M., Powroznik, P., & Lukasik, E. (2022). 3D technologies for intangible cultural heritage preservation—literature review for selected databases. Heritage Science, 10(1), 3. doi:10.1186/s40494-021-00633-x.

Ferdani, D., Fanini, B., Piccioli, M. C., Carboni, F., & Vigliarolo, P. (2020). 3D reconstruction and validation of historical background for immersive VR applications and games: The case study of the Forum of Augustus in Rome. Journal of Cultural Heritage, 43, 129–143. doi:10.1016/j.culher.2019.12.004.

Deng, X., Kim, I. T., & Shen, C. (2021). Research on convolutional neural network-based virtual reality platform framework for the intangible cultural heritage conservation of china Hainan li nationality: Boat-shaped house as an example. Mathematical Problems in Engineering, 5538434. doi:10.1155/2021/5538434.

Bui, D. K., Nguyen, T. N., Ghazlan, A., Ngo, N. T., & Ngo, T. D. (2020). Enhancing building energy efficiency by adaptive façade: A computational optimization approach. Applied Energy, 265, 114797. doi:10.1016/j.apenergy.2020.114797.

Harifi, S., Mohammadzadeh, J., Khalilian, M., & Ebrahimnejad, S. (2021). Giza Pyramids Construction: an ancient-inspired metaheuristic algorithm for optimization. Evolutionary Intelligence, 14(4), 1743–1761. doi:10.1007/s12065-020-00451-3.

Wang, X., Wu, C., & Bai, C. (2022). Generating the Regular Axis from Irregular Column Grids through Genetic Algorithm. Applied Sciences (Switzerland), 12(4), 2109. doi:10.3390/app12042109.

Wei, Y. (2024). Research on the Integration of Chinese Traditional Architectural Elements in Modern Architectural Design under Digital Technology Support. Applied Mathematics and Nonlinear Sciences, 9(1). doi:10.2478/amns-2024-1780.

Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, 141, 104440. doi:10.1016/j.autcon.2022.104440.

Liu, Y., Chen, H., Zhang, L., Wu, X., & Wang, X. jia. (2020). Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: A case study in China. Journal of Cleaner Production, 272, 122542. doi:10.1016/j.jclepro.2020.122542.

Chen, C., & Arus, B. B. M. (2024). Chinese Cultural and Opera Stage Architectural Design for Urbanization Henan, China. South Asian Journal of Social Sciences and Humanities, 5(3), 114–134. doi:10.48165/sajssh.2024.5307.

Roman, N. D., Bre, F., Fachinotti, V. D., & Lamberts, R. (2020). Application and characterization of metamodels based on artificial neural networks for building performance simulation: A systematic review. Energy and Buildings, 217. doi:10.1016/j.enbuild.2020.109972.

Yu, T., & Zhu, H. (2020). Hyper-parameter optimization: A review of algorithms and applications. arXiv preprint, arXiv:2003.05689.

Liu, Z., Zhu, D., Raju, L., & Cai, W. (2021). Tackling Photonic Inverse Design with Machine Learning. Advanced Science, 8(5). doi:10.1002/advs.202002923.

Jiang, S., Wang, M., & Ma, L. (2023). Gaps and requirements for applying automatic architectural design to building renovation. Automation in Construction, 147, 104742. doi:10.1016/j.autcon.2023.104742.

Rodrigues, F., Cotella, V., Rodrigues, H., Rocha, E., Freitas, F., & Matos, R. (2022). Application of Deep Learning Approach for the Classification of Buildings’ Degradation State in a BIM Methodology. Applied Sciences (Switzerland), 12(15), 7403. doi:10.3390/app12157403.

Zhang, X., Wang, Y., Zhang, N., Xu, D., & Chen, B. (2019). Research on scene classification method of high-resolution remote sensing images based on RFPNet. Applied Sciences (Switzerland), 9(10), 2028. doi:10.3390/app9102028.

Ye, A., Zhou, X., & Miao, F. (2022). Innovative Hyperspectral Image Classification Approach Using Optimized CNN and ELM. Electronics (Switzerland), 11(5), 775. doi:10.3390/electronics11050775.

Gour, M., Jain, S., & Sunil Kumar, T. (2020). Residual learning based CNN for breast cancer histopathological image classification. International Journal of Imaging Systems and Technology, 30(3), 621–635. doi:10.1002/ima.22403.


Full Text: PDF

DOI: 10.28991/HIJ-2025-06-01-011

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