Assessment of the Exterior Quality of Traditional Residences: A Genetic Algorithm–Backpropagation Approach

• Published in: Buildings (Basel) Vol. 12; no. 5; p. 559
• Main Authors: Xu, Lu, Liu, Ke, Sang, Kun, Lin, Guiye, Luo, Qingliu, Huang, Caizhu, Giordano, Andrea
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2022
• Subjects: Algorithms; Architecture; Artificial intelligence; Back propagation networks; Dwellings; Evaluation; exterior quality assessment; GA–BP neural network model; Genetic algorithms; Local culture; Machine learning; Neural networks; Quality assessment; residential conservation; traditional village; Villages; China; Yunnan China; Myanmar (Burma)
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings12050559

The visual aesthetics of villages are remarkably affected by the exterior quality of traditional residences, influencing the impression and assessment of local culture. A proper scientific assessment of exterior quality can protect traditional cultures and improve the development of villages. This research was conducted in a village consisting of 115 residences (Mengjinglai village, which is on the border between China and Myanmar). The backpropagation (BP) neural network model with genetic algorithm (GA) was applied to evaluate the quality of the dwellings. All the evaluation values of the dwellings were defined by scores. Meanwhile, the score of each residence was affected by three main factors: architectural spatial elements, architectural construction elements, and historical and cultural elements. The results show that the village’s dwellings are well preserved and clearly express the traditional Dai style. Moreover, the GA–BP approach is more suitable than the traditional BP method for the assessment of the exterior quality. The quantitative machine learning model would be useful for other aspects of the assessment of similar villages in the future.