Multibolt looseness monitoring of steel structure based on multitask active sensing method and substructure cross-domain transfer learning

Abstract

Under the influence of service life and the external environment, bolted connections are prone to loosening, which may lead to structural hazards. Thus, it is crucial to carry out real-time monitoring of bolted connections. Based on the active sensing method, previous researchers mainly focused on quantifying the single-bolt looseness, with little focus on locating and quantifying the multibolted connection. This study introduces an innovative approach to monitor multibolt looseness in steel structures. The proposed method utilizes multitask active sensing, incorporating a substructure cross-domain transfer learning technique. A finite-element model of a portal frame structure was first established by ABAQUS software, and the substructure was determined based on the stress wave propagation characteristics. Secondly, the location and degree of bolted connection in the substructure and portal frame structure were monitored using the piezoelectric active sensing method. Monitoring data of the substructure are tagged as the source domain, while data of the portal frame structure are tagged as the target domain. To efficiently decouple the characteristics of loosening, this study extracted multidomain energy and unthresholded multivariate recurrence plots from stress wave signals. These elements were employed to pinpoint the location and assess the degree of looseness. The multibolted connection was then monitored by the adversarial domain adaptation networks. Ultimately, using the target domain’s input, the optimized model was able to precisely detect the location and degree of the multibolted connection step by step. The experimental results demonstrated that the suggested technique has a lot of promise for multibolt looseness monitoring.