The Non-Destructive Testing of Architectural Heritage Surfaces via Machine Learning: A Case Study of Flat Tiles in the Jiangnan Region
Source abstract
Abstract
This study focuses on the ancient buildings in Cicheng Old Town, a typical architectural heritage area in the Jiangnan region of China. These buildings are famous for their well-preserved Tang Dynasty urban layout and Ming and Qing Dynasty roof tiles. However, the natural aging, weathering, and biological erosion of the roof tiles seriously threaten the integrity of heritage protection. Given that current detection methods mostly depend on manual checks, which are slow and cover only a small area, this study suggests using deep learning technology for heritage protection and creating a smart model to identify damage in flat tiles using the YOLOv8 architecture. During this research, the team used drone aerial photography to collect images of typical building roofs in Cicheng Old Town. Through preprocessing, unified annotation, and system training, a damage dataset containing 351 high-quality images was established, covering five types of damage: breakage, cracks, the accumulation of fallen leaves, lichen growth, and vegetation growth. The results show that (1) the model has an overall mAP of 73.44%, an F1 value of 0.75 in the vegetation growth category, and a recall rate of 0.70, showing stable and balanced detection performance for various damage types; (2) the model performs well in comparisons using confusion matrices and multidimensional indicators (including precision, recall, and log-average miss rate) and can effectively reduce the false detection and missed detection rates; and (3) the research team applied the model to drone images of the roof of Fengyue Painted Terrace Gate in Cicheng Old Town, Jiangbei District, Ningbo City, Zhejiang Province, and automatically detected and located multiple tile damage areas. The prediction results are highly consistent with field observations, verifying the feasibility and application potential of the model in actual heritage protection scenarios.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: 3D Surveying and Cultural Heritage · Conservation Techniques and Studies · Building materials and conservation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Humanities and Arts, Macau University of Science and Technology, Avenida Wai Long, Tapai, Macau 999078, China
Faculty of Design and Architecture, Zhejiang Wanli University, No. 8 Qianhu South Road, Yinzhou District, Ningbo 315100, China
Zhuhai MUST Science and Technology Research Institute, Macau University of Science and Technology, Zhuhai 519099, China
Funding
Faculty Research Grants funded by Macau University of Science and Technology
References (43)
Show source-provided references
1. (2025, May 10). Winners of 2009 UNESCO Asia-Pacific Awards for Cultural Heritage Conservation. Available online: https://www.unesco.org/en/articles/winners-heritageawards.
2. Knapp, R.G. (2012). Chinese Houses: The Architectural Heritage of a Nation, Tuttle Publishing.
3. Study on History of Little Black Tile in China
4. The use of unmanned aerial vehicles for sloped roof inspections–considerations and constraints
5. Endless Sights of Tiled Buildings: A Discussion of the Association Between the Developments of the Tiled Roofs and the Wooden Structures System
6. Influence of “Qin Brick and Han Tile” in History
7. Characteristic classification using physicochemical analysis data of ancient roof tiles10.1007/s10967-023-09184-w ↗
8. Two traditions: A comparison of roof tile manufacture and usage in Angkor and China10.1353/asi.2020.0039 ↗
9. Cuilin, L. (2016). Construction and Craftsmanship of Tile Roofing in Jiang-Zhe Area. [Master’s Thesis, Southeast University].
10. Chen, Y. (2024). An ‘Anthropo-Architectural Approach’ to Integrated Conservation: A Case Study of Cultural Heritage Associated with the Traditional Chinese Roof Tile Industry, in School of Architecture, Design and Planning. [Ph.D. Thesis, The University of Queensland]. Available online: https://espace.library.uq.edu.au/view/UQ:435e694.
11. Yong, L. (2023). Research on the Brick and Tile Craftsmanship of Huizhou Traditional Residence in the Ming and Qing Dynasties, in School of Architeture and Urban Planning. [Master’s Thesis, Anhui Jianzhu University].
12. Tile and Brick Making in China: A Study of the “Yingzao Fashi”
13. Antique blue brick and fired common brick on the quality of the research
14. Conservation of disappearing traditional manufacturing process for Chinese grey brick: Field survey and laboratory study10.1016/j.conbuildmat.2019.03.317 ↗
15. Peng, Z. (2015). Deterioration Mechanism of Grey Brick and Masonry Structures Under the Action of Load and Environment in Materials Science and Engineering School. [Ph.D. Thesis, Southeast University].
16. Dai, S., Huang, J., Shen, Z., Zhou, Y., and Ju, F. (2014, January 18). Characteristics of Historic Roof tiles and Proposed Technical Standard for Restoration of traditional architecture in the Southern Shanxi Province, China. Proceedings of the 2014 Conference on Architectural Historian Studies of China, Fuzhou, China.
17. Investigating the spatial distribution mechanisms of traditional villages from the human geography region: A case study of Jiangnan, China10.1016/j.ecoinf.2024.102649 ↗
18. Automatic detection and recognition method of Chinese clay tiles based on YOLOv4: A case study in Macau10.1080/15583058.2023.2246029 ↗
19. Yang, X., Zheng, L., Chen, Y., Feng, J., and Zheng, J. (2023). Recognition of Damage Types of Chinese Gray-Brick Ancient Buildings Based on Machine Learning—Taking the Macau World Heritage Buffer Zone as an Example. Atmosphere, 14.10.3390/atmos14020346 ↗
20. Damage and restoration technology of historic buildings of brick and wood structures: A review10.1186/s40494-024-01422-y ↗
21. Llamas, J., Lerones, M.P., Medina, R., Zalama, E., and Gómez-García-Bermejo, J. (2017). Classification of Architectural Heritage Images Using Deep Learning Techniques. Appl. Sci., 7.10.3390/app7100992 ↗
22. Analysis of structural integrity through the combination of non-destructive testing techniques in heritage inspections: The study case of San Segundo’s hermitage (Ávila, Spain)10.1016/j.jobe.2024.109295 ↗
23. Deep learning in the construction industry: A review of present status and future innovations10.1016/j.jobe.2020.101827 ↗
24. Application of computer vision technology in surface damage detection and analysis of shedthin tiles in China: A case study of the classical gardens of Suzhou10.1186/s40494-024-01185-6 ↗
25. Automatic damage detection of historic masonry buildings based on mobile deep learning10.1016/j.autcon.2019.03.003 ↗
26. Ravichand, M., Kumar, R., Hazela, B., and Suthar, T. (2022, January 1–3). Crack on brick wall detection by computer vision using machine learning. Proceedings of the 2022 6th International Conference on Electronics, Communication and Aerospace Technology, Coimbatore, India.10.1109/ICECA55336.2022.10009343 ↗
27. Autonomous damage segmentation and measurement of glazed tiles in historic buildings via deep learning10.1111/mice.12488 ↗
28. Xu, J., Zeng, F., Liu, W., and Takahashi, T. (2022). Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning. Appl. Sci., 12.10.20944/preprints202204.0240.v1 ↗
29. Object detection using YOLO: Challenges, architectural successors, datasets and applications10.1007/s11042-022-13644-y ↗
30. A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS10.3390/make5040083 ↗
31. Jacob, I.J., Piramuthu, S., and Falkowski-Gilski, P. (2023). A review on yolov8 and its advancements. Data Intelligence and Cognitive Informatics, Springer. ICDICI 2023; Algorithms for Intelligent Systems.10.1007/978-981-99-7962-2 ↗
32. Research on intelligent monitoring technology for roof damage of traditional Chinese residential buildings based on improved YOLOv8: Taking ancient villages in southern Fujian as an example10.1186/s40494-024-01345-8 ↗
33. Applying optimized YOLOv8 for heritage conservation: Enhanced object detection in Jiangnan traditional private gardens10.1186/s40494-024-01144-1 ↗
34. The development of a 2D/3D BIM-GIS web platform for planned maintenance of built and cultural heritage: The MAIN10ANCE project10.5194/isprs-archives-XLVIII-M-2-2023-433-2023 ↗
35. GIS-HBIM integration for the management of historical buildings10.5194/isprs-archives-XLII-2-1129-2018 ↗
36. Ávila, F., Blanca-Hoyos, Á., Puertas, E., and Gallego, R. (2024). HBIM: Background, Current Trends, and Future Prospects. Appl. Sci., 14.10.3390/app142311191 ↗
37. Analysis of the Current Status of Sensors and HBIM Integration: A Review Based on Bibliometric Analysis10.3390/heritage7040098 ↗
38. Historic building information modelling (HBIM)10.1108/02630800910985108 ↗
39. The Application of Historic Building Information Modelling (HBIM) to Cultural Heritage: A Review10.3390/heritage6100350 ↗
40. HBIM for storing life-cycle data regarding decay and damage in existing timber structures10.1016/j.autcon.2020.103262 ↗
41. Dionizio, R.F., and Dezen-Kempter, E. (2024). From Data and Metadata to HBIM-GIS Integration. Int. J. Archit. Herit., 1–14.10.1080/15583058.2024.2439916 ↗
42. Li, Q., Zhang, T., Fang, Y., and Lin, F. (2024). A Pathological Diagnostic Method for Traditional Brick-Masonry Dwellings: A Case Study in Guangfu Ancient City. Buildings, 14.10.3390/buildings14113563 ↗
43. Effectiveness of the anti-erosion of an MICP coating on the surfaces of ancient clay roof tiles10.1016/j.conbuildmat.2020.118202 ↗