Vui lòng dùng định danh này để trích dẫn hoặc liên kết đến tài liệu này: http://thuvienso.vanlanguni.edu.vn/handle/Vanlang_TV/16202
Nhan đề: Detection and Prediction of Internal Damage in the Ancient Timber Structure Based on Optimal Combined Model
Tác giả: Wang, Ziyi
Ma, Donghui
Qian, Wei
Wang, Wei, ... [et al.]
Từ khoá: Fuzzy sets
Defects
Timber
Nondestructive testing
Damage detection
Poplar
Drilling
Damage assessment
Empirical analysis
Stress waves
Năm xuất bản: 2019
Nhà xuất bản: Hindawi Publishing Corporation
Tóm tắt: It is currently known that using stress wave and drilling resistance to detect the internal damage in the ancient timber structure is not a highly precise process. To improve the detection precision of this process, a simulation test was used to detect the internal damage of poplar and elm in ancient buildings. In this empirical study, we compared the detection precision of these two detection methods. Based on the idea of variable weight, we introduced three combined forecasting models based on the IOWA operator, IOWGA operator, and IOWHA operator to predict the internal damage in the ancient timber structure. The results show that the combined forecasting model based on the IOWA operator is more effective in predicting compared to a single detection method and other combined forecasting models. To be more specific, the results show that the detection precision of the combined model is increased by 25.8% and 4.7%, respectively, compared to the precision of the stress wave and drilling resistance tests. The error indicators of the combined forecasting model based on the IOWA operator are better than those of the other combined forecasting models. In addition, the analysis results based upon cross-validation theory show the combined forecasting model based on the IOWA operator has the best applicability, which provides a new practical method for evaluating internal damage of timber components in ancient buildings.
Mô tả: 18 tr.
Định danh: http://thuvienso.vanlanguni.edu.vn/handle/Vanlang_TV/16202
ISSN: 1687-8086
1687-8094 (e)
Bộ sưu tập: Bài báo_lưu trữ

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