Damage detection of bridges subjected to moving load based on domain-adversarial neural network considering measurement and model error

Abstract

Bridge damage detection methods based on moving load induced response and deep learning are popular, and they are effective when the training data generated by a finite element model (FEM) and the testing data measured from the actual bridge have the same distribution. However, their distributions are normally different due to the measurement and model error. This paper proposes a Domain-adversarial Neural Network (DANN) based damage detection method for bridge structures. The moving load induced displacement responses of the FEM and the actual bridge are taken as network inputs, and the feature space with the minimum differentiation between the edge distributions of the FEM (source domain) and the actual bridge (target domain) is extracted by adversarial training between the domain discriminator and the feature extractor. This feature space is a domain invariant feature of the source domain and the target domain and contains damage information only. Thus the damage label information is transferred from the source domain to the target domain for damage detection. Numerical and experimental examples manifest that the proposed method can reduce the distribution gap between the source domain and the target domain, and achieve higher damage detection accuracy than that of the traditional deep learning method by using a small number of sensors without a large amount of damage label data.