Most ancient timber structures in China only use manual inspection to identify and record cracks, and a small number of ancient timber structures have introduced nondestructive testing instruments such as ultrasonic, stress wave, and Provis. Therefore, how to use new theories and new methods to accurately detect and dynamically monitor cracks in timber structures of ancient architecture that is about to reach dangerous crack indicators is a new problem and challenge in the field of timber structure protection of ancient architecture.Įvaluation index of dangerous cracks. According to “Technical code for maintenance and strengthening of ancient timber architecture” and “Code for construction and acceptance of in traditional Chinese ancient architecture”, the criteria for identifying dangerous cracks are shown in Table 2. General cracks will not affect the safety of the structure, but under the effect of load and environment, after the general cracks further expand into dangerous cracks, they will cause local structural fractures, resulting in the continuous collapse of the overall structure. The strong orthotropic anisotropy and complex microstructure of timber lead to extremely complicated development laws of cracks. The timber structures of ancient Chinese architecture adopt mortise and tenon joints, and the cracks are characterized by large numbers, complicated causes, and great harm. Almost all structural components such as columns, beams, purlins, and fang will crack to a certain extent (see Figure 1 for details).Ĭracks in the timber structures of ancient architecture. Through our research and investigation, we have found that cracks are the most common form of damage due to the characteristics of biological materials in ancient timber architecture. During its long service cycle, various forms of damage have appeared due to the influence of the natural environment, climate temperature, and human factors. The timber structures of ancient Chinese architecture have been constructed for a long time and have extremely high historical and cultural value.
Taking Beijing as an example, there are more than 1,700 cultural relics at the national, municipal, and district levels, among which there is more than 1,200 ancient timber architecture, accounting for about 71%. At the same time, timber structures account for a large proportion of existing ancient architecture in our country. Type one means revolutionary sites and revolutionary memorial architecture, type two means important historical sites and representative architecture in neoteric and modern times, type three means cave temples, stone carvings, and others, type four means ancient architecture and historical monuments, type five means ancient sites, and type six means ancient burials.
There are 5,058 key national preservation units of cultural relics in China.2,162 of them are ancient architecture and historical memorial architecture, which account for 42.74% (see Table 1 for details). We published the first picture data set of cracks in timber structures of ancient architecture, and applied YOLO model in the intelligent identification field of cracks in timber structures of ancient architecture for the first time, which opened up a new idea for the intelligent operation and maintenance of the timber structures of ancient architecture. We have summarized and analyzed the advantages and disadvantages of the three models in cracks detection of the timber structures of ancient architecture, and concluded the comparing results of the three models in cracks detection based on experiments. In the comparing process, we mainly have discussed the index performance of the three models in terms of training time, loss function, recall rate, and mAP value. In order to find a better algorithm, this paper mainly adopts three models including YOLO v3, YOLO v4s-mish, and YOLO v5s to detect cracks in the timber structures of ancient architecture, and compares and analyzes the advantages and disadvantages of the three models. However, there are many different versions of YOLO series models. In previous studies, we have verified that YOLO v5s is effective in crack detection in timber structures of ancient architecture. The image data has a large proportion of pixels, which is obviously different from the background gray value, and there is timber grain noise, thus the existing methods cannot accurately extract the complex texture contour feature of cracks. However, the timber structures of ancient architecture have strong orthotropic anisotropy and complex microscopic structures, and the law of cracks development is extremely complex. Deep learning has achieved good results in the crack detection of roads and bridges.
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