Meta-learning for Structural Damage Localization and Quantification
Existing deep-learning-based methods for structural damage identification rely heavily on massive amounts of labeled data.Therefore,a meta-learning-based approach is proposed for structural damage localization and quantification. First,a structural damage localization and quantification model was established using an artificial neural network.This model was used to learn the nonlinear mapping relationship between structural modal data (frequency and mode shape) and substructure stiffness parameters.Second,a model-agnostic meta-learning strategy was used to train the damage localization and quantification model.The generalizability of the damage localization and quantification models can be improved by optimizing the initial weight parameters of the artificial neural network (ANN). The proposed method utilizes a model-agnostic meta-learning training strategy to acquire prior knowledge,thereby accelerating the learning process for new structural damage localization and quantification tasks with limited training data.The method was verified on a numerical three-span bridge and benchmark project of the Z24 bridge. The results demonstrate that the proposed approach provides efficient and accurate localization and quantification of potential structural damage using limited data. Compared with conventional ANN and transfer learning methods,the method exhibited faster convergence and higher identification accuracy.
bridge engineeringdamage localization and quantificationmeta-learningmodal datahealth monitoringartificial neural network
万方数据
维普
