ISSN: 2222-6990
Open access
Cave dwelling architecture is a unique style of architecture, and it is also the embodiment of Chinese traditional culture in China. It is widely used in the life of working people in ancient China. According to studies, a significant number of people still live in above-ground underground houses and cave dwellings in Australia, Spain, China, Turkey, and Tunisia, and over 40 million people do so in China. However, with the continuous development of society, cave dwellings are gradually unable to meet the needs of more and more people for comparison and work, and tend to be weak. Shanxi cave dwellings have been greatly challenged. Against this background, the aim of this research is to renew and develop cave dwellings better. The research methodology is quantitative method and exploratory research approach. Based on the premise of deep learning to identify and classify the architectural features of Shanxi cave dwellings. In this study, 200 questionnaires were used to measure the residents' requirements for cave dwellings. Through detailed investigation and research on specific examples in Shanxi, the results are as follows: (1) We identify and classify the architectural features of Shanxi cave dwellings based on deep learning algorithm so that we can better understand and explore Shanxi cave dwellings. (2) By analyzing the present situation of examples, it is concluded that most villagers affirm the living comfort and regional adaptability of cave buildings, and the identification and classification of cave building features in Shanxi based on deep learning can provide effective reference, in order to better complete the renovation and development of cave buildings in Shanxi.
Bedard, A., Westerling-Bui, T., & Zuraw, A. (2021). Proof of Concept for a Deep Learning Algorithm for Identification and Quantification of Key Microscopic Features in the Murine Model of DSS-Induced Colitis. Toxicol Pathol, 49(4), 897-904.
https://doi.org/10.1177/0192623320987804
Deltshev, C. (2011). The faunistic diversity of cave-dwelling spiders (Arachnida, Araneae) of Greece. Arachnologische Mitteilungen, 40(40), 23-32.
https://doi.org/10.5431/aramit4004
Guo, P., & Tong, L. (2011). Research about influence of loess cave dwelling's leg width on safety factor by Strength Reduction Method. Applied Mechanics and Materials,
Huang, Z., & Ning, X. (2016). Environmental reconstruction design research of loess cave dwelling in Loess Plateau. Journal of Shanxi Agricultural University(Social Science Edition), 15(06). https://doi.org/10.13842/j.cnki.issn1671-816x.2016.06.012
Ke, Y., & Sun, S. (2015). Study on cave-dwelling forms and reinforcement measures. Shanxi Architecture, 41(35), 3-4. https://doi.org/10.13719/j.cnki.cn14-1279/tu.2015.35.002
Liu, M., Fu, B., Xie, S., He, H., & Fan, D. (2021). Comparison of multi-source satellite images for classifying marsh vegetation using DeepLabV3 Plus deep learning algorithm. Ecological Indicators, 125(11), 107562.
Lu, X., & Kong, F. (2003). A Resarch on the Preservation and the Possibility as Tourist Objects of the Underground Cave Dwelling Village Scenery in Shanxi,China-A Case Study of Huaixia,Pinglu. Rikkyo University Bulletin of Studies in Tourism, 5, 133-137.
Mian, T. S. (2021). Automation of Bug-Report Allocation to Developer using a Deep Learning Algorithm. 2021 International Congress of Advanced Technology and Engineering, ICOTEN 2021,
Piorkowski, D., Blamires, S. J., Doran, N. E., Liao, C. P., Wu, C. L., & Tso, I. M. (2018). Ontogenetic shift toward stronger, tougher silk of a web-building, cave-dwelling spider. Journal of Zoology, 304. https://doi.org/10.1111/jzo.12507
Porporato, N., Tun, T. A., Baskaran, M., Wong, D. W. K., Husain, R., Fu, H., Sultana, R., Perera, S., Schmetterer, L., & Aung, T. (2022). Towards 'automated gonioscopy': A deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography [Article]. British Journal of Ophthalmology, 106(10), 1387-1392. https://doi.org/10.1136/bjophthalmol-2020-318275
Ramseyer, V. (2014). Cave Dwelling. John Wiley & Sons, Ltd.
Sharmila, J., & Subramani, A. (2014). A method for extracting information from the web using deep learning algorithm. Journal of Theoretical Applied Information Technology, 68(2), 474-484.
Wang, F., & Liu, Y. (2002). Thermal environment of the courtyard style cave dwelling in winter [Article]. Energy and Buildings, 34(10), 985-1001. https://doi.org/10.1016/S0378-7788(01)00145-1
Zhao, M. Y., Bin-Wang, H. E., & Zhang, Z. (2014). Experimental research on application of loess reinforcement of consolidation of cave dwelling. Shanxi Architecture, 40(20), 110-111. https://doi.org/10.13719/j.cnki.cn14-1279/tu.2014.20.059
(Zong et al., 2023)
Zong, J., Mohamed, W. S. W., Jaafar, M. F. Z., & Ujang, N. (2023). Feature Recognition and Classification of Shanxi Cave Dwellings Based on Deep Learning. International Journal of Academic Research in Business and Social Sciences, 13(12), 1574–1594.
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