Structural Stiffness Identification of Traditional Mortise-Tenon Joints Based on Statistical Process Control Chart

Abstract

This paper proposes a novel index, the extreme value of the largest principal component scores of the generalized likelihood ratio based on the statistical process control chart, to develop a structural stiffness identification method for assessing traditional Chinese mortise-tenon joints. The proposed method involves four stages. First, a generalized likelihood ratio test is conducted by transforming the collected acceleration signals into the generalized likelihood ratio matrix. Second, principal component analysis (PCA) is used to reduce data dimensionality and extract the extreme values of the first principle component scores as a novel control index. Subsequently, a statistical process control chart is drawn via the proposed control index. Finally, the ratio of the structural stiffness reduction can be evaluated by establishing the relationship between the stiffness and number of control indices outside the upper and lower control limits in the statistical process control chart. The proposed method is validated by vibration test data acquired from a traditional timber frame under reversed cyclic loads and vibration in a laboratory. The results show that (1) the proposed index performed in the statistical process control chart is able to monitor the novelty of the mortise-tenon timber joint; and (2) the proposed method can be used to assess the states of the timber structure and even predict further structural stiffness.