Structural damage identification based on LSTM neural networks under ambient temperature variations
The change of ambient temperature will cause the change of modal parameters,and the degree of change will cover up or partially cover up the change caused by damage,resulting in the misjudgment of false positive or false negative issued by the structural health monitoring system.Therefore,eliminating the temperature effect is the key to improve the accuracy of damage identification.Based on LSTM neural network,this paper proposes a method to identify structural damage under the impact of ambient temperature.By making full use of the nonlinear mapping advantage of LSTM neural network,the correlation model of multivariate temperature and modal frequencies is established.On this basis,the data normalization method is used to eliminate the temperature effect,and the control chart is used to judge the abnormal change of modal frequencies to determine the damage condition.Finally,the proposed method is applied to a numerical model and an actual bridge.The results show that the method can effectively eliminate the temperature effect.Combined with the control chart method,it can identify the damage time and has a certain noise resistance.In the real bridge data analysis,it can still show good damage sensitivity.
LSTM neural networkstructural health monitoringtemperaturemodal frequencyvariational mode decomposition
万方数据
维普
