Structural damage detection based on convolutional neural networks and population of bridges

The CNN-based detection methods have been widely used in the field of structural health monitoring (SHM), however, they can only be used for individual structures under certain conditions; for the structures in-service, damage detection will be affected by a variety of external factors (unknown/uncertain load and geometric dimension, etc.). Therefore, in order to improve the applicability of the CNNs, their compatibility and robustness need to be thoroughly investigated. In this paper, a large number of random models were produced to establish a population of bridge structures, the damage features of the population were extracted by the CNN; subsequently, the CNN was applied to damage detection of the new randomly-created models. The results show that the best detection results (99.4% accuracy) can be obtained by using the acceleration signals as the CNN input. This demonstrates that the proposed method will expand the detection ability of the CNN beyond an individual structure.

Automated summary

This paper investigates the compatibility and robustness of CNNs in the field of structural health monitoring (SHM). By using acceleration signals as input, a population of bridge structures was created and the damage features were extracted using CNN. The results show that the detection ability of CNN can be extended beyond individual structures.