Coupling model of fuzzy soft set and Bayesian method to forecast internal defects of ancient wooden structures based on nondestructive test

Abstract

In order to improve the detection precision of internal defect in the ancient wooden structures, defect simulation tests on pine and elm commonly used in ancient buildings were performed by using stress wave detection and drilling resistance detection. Based on detection data, three typical evaluation criteria, which are the information entropy, the correlation coefficient, and residual sum of squares, were selected as a priori information. Combining with the expert’s fuzzy evaluation value, Bayesian formula was used to modify the prior information to determine the weight coefficients of the two detection methods, and a combined prediction model was established. The results show that the combination of subjectivity and objectivity enables the revised weights to more reasonably and accurately reflect the relative importance of each detection method in prediction evaluation, which reduces the forecasting error. Specifically speaking, the mean error of the combined model was reduced by 49.8% and 59.8%, respectively, compared with stress wave detection and drilling resistance detection. Moreover, the five error indicators of this combined forecasting model are the smallest in all methods, indicating the proposed method has a better forecasting effect. It provides an effective application tool for the practice of forecasting the internal defects of wooden components in ancient buildings.