基于EMI-CNN的建筑施工模板支撑体系节点健康监测

Health monitoring of joints in construction formwork support systems based on EMI-CNN

为预防模板坍塌引发建筑施工安全事故风险,提出一种基于压电阻抗法(EMI)和卷积神经网络(CNN)的模板支撑体系节点智能监测方法。首先,利用压电陶瓷传感器(PZT)的机电耦合特性及其集驱动-传感于一体的特点,建立PZT-节点耦合系统的机电阻抗传感机制模型;其次,基于EMI法,以与待测结构耦合的PZT片电导信号为监测指标,确定模板支撑体系节点松动的发生;然后,以敏感频段内PZT片的801个原始电导信号为模型输入,9个节点松动程度为模型输出,构建162组学习样本和27组测试样本,建立EMI-CNN模型,确定节点松动程度;最后,以一个实际工程中的建筑施工模板体系节点为例,验证EMI-CNN模型的有效性,并对比分析EMI-BP模型。研究结果表明:EMI-CNN模型经过85次迭代达到收敛,预测准确率达到100%,相较于EMI-BP模型提高29.63%。该监测方法可实现对建筑施工模板支撑体系节点健康状态实时、准确、无损监测。

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.