关键词: 压电阻抗法(EMI), 卷积神经网络(CNN), 建筑施工, 模板支撑体系, 健康监测, 压电陶瓷传感器(PZT)

Abstract:

In order to monitor the health state of construction formwork support systems and prevent the risk of safety accidents caused by formwork collapses, a new intelligent monitoring method combining EMI and CNN for joints of formwork support systems was proposed. Firstly, based on the electromechanical coupling and sensing-driving characteristics of PZT, PZT-joint coupling model was built based on the electromechanical impedance sensing mechanism. Secondly, the original conductivity of PZT patch, coupled with the monitored structure, was used as a monitoring signature for identifying joint looseness based on the EMI technique. Thirdly, EMI-CNN model was built with the 801 original conductance signals of PZT over the sensitive frequency range as the inputs, and the nine degrees of joint looseness as the outputs. In total, the dataset consisted of 189 samples, 162 for training and 27 for testing. At last, taking an actual formwork support system joint from building site as an example, EMI-CNN model was verified and compared with EMI-BP model by the experiment. The research results show that EMI-CNN model reached convergence after 85 iterations. The prediction accuracy of the EMI-CNN model reached 100%, which is 29.63% better than EMI-BP model. This proposed method is distinguished by its real-time, accurate and non-destructive monitoring capabilities, providing an effective solution for health monitoring of joints in construction formwork support systems.

Key words: electro-mechanical impedance (EMI), convolutional neural networks (CNN), building construction, formwork support system, health monitoring, piezoelectric ceramic transducer (PZT)