Pseudo-damage simulation and CNN deep learning for damage identification of submerged structure-foundation systems

Abstract

Autonomous damage identification of submerged structure-foundation systems is challenging due to the difficulty of acquiring damage-induced system responses for training deep learning models. In this study, a novel approach integrating pseudo-damage simulation and convolutional neural network (CNN) deep learning is proposed for damage identification in the submerged structure-foundation system. Pseudo-damage simulation is a technique to generate equivalent damage conditions in inaccessible submerged sub-systems for training deep learning models. The following approaches are implemented to achieve the objective. Firstly, a scheme of pseudo-damage simulation for 1-D CNN deep learning is designed for the caisson-foundation system. Secondly, a vibration monitoring method using pseudo-wave-impulse excitations is designed for the caisson-foundation system. Thirdly, 1-D CNN models are trained for individual caisson units to predict the location and size of foundation damage by vibration signals out of a series of pseudo-damage cases. The 1-D CNN models demonstrate accurate performance in handling untrained scenarios. Experimental results validate the effectiveness of the proposed approach in achieving high accuracy for identification of the foundation damage.