To accurately identify the damage degree of bridge structure,the key bridge component of prestressed reinforced concrete beams was fabricated,and the three-point bending loading experiment was carried out.The acoustic emission(AE)signals were collected during the whole damage failure process,and the damage and failure process of the beam was divided into four typical stages through AE parameter analysis.The long short-time memory(LSTM)neural network was constructed.To solve the problem of falling into local optimum by setting hyper parameters of LSTM model according to the experience,the improved whale optimization algorithm(WO A)based on sine chaotic map and adaptive weight was proposed to optimize hyper parameters of LSTM.The IWOA-LSTM algorithm model was designed to train and identify AE signals characteristic parameter data of experimental beam at various damage stages.The network structure was finalized,and the AE signals of other beam under the same working conditions were identified.The results show that the recognition accuracies all exceed or approach 92％.Compared with the common LSTM model,the recognition accuracy of IWOA-LSTM model is improved by about 7％.
维普
万方数据