Monitoring mechanical behaviors of CLT connections under reciprocating loading based on PZT-enabled active sensing and machine learning algorithms

Monitoring the mechanical behaviors of cross-laminated timber (CLT) connections is of great importance to the condition assessment of timber structures. To date, numerous research works have demonstrated that Lead Zirconate Titanate (PZT)-enabled active sensing approaches can achieve structural healthy state monitoring under monotonic loads, whereas their effectiveness for reciprocating loads still needs to be further studied. Moreover, traditional PZT-enabled active sensing approaches depend on prior knowledge and human judgment, restricting their field applications. Based on the above background, this research proposes an innovative method to monitor the mechanical behaviors of CLT connections under reciprocating loading by integrating PZT-enabled active sensing and eight machine learning (ML) approaches. Meanwhile, a new damage index based on wavelet packet decomposition and multiple signal path fusion is designed to improve the performance of ML methods. Finally, cyclic loading tests on CLT connections are conducted to demonstrate the outstanding capabilities of the proposed method than conventional PZT-enabled active sensing approaches.