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.

中文翻译：

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基于多任务主动感知方法和子结构跨域迁移学习的钢结构多螺栓松动监测

在使用寿命和外部环境的影响下，螺栓连接容易发生松动，从而可能导致结构危险。因此，对螺栓连接进行实时监控至关重要。基于主动传感方法，以往的研究人员主要关注量化单螺栓松动，很少关注多螺栓连接的定位和量化。本研究介绍了一种监测钢结构中多螺栓松动的创新方法。所提出的方法利用多任务主动传感，结合子结构跨域迁移学习技术。首先利用ABAQUS软件建立了门式刚架结构的有限元模型，并根据应力波传播特性确定了下部结构。其次，利用压电主动传感方法监测下部结构和门式框架结构中螺栓连接的位置和程度。下部结构的监测数据被标记为源域，而门架结构的数据被标记为目标域。为了有效解耦松动特征，本研究从应力波信号中提取多域能量和无阈值多元递归图。这些元素用于查明位置并评估松动程度。然后，对抗域适应网络监控多螺栓连接。最终，利用目标域的输入，优化后的模型能够逐步精确地检测多螺栓连接的位置和程度。实验结果表明，所提出的技术对于多螺栓松动监测具有很大的前景。