A combined finite element and deep learning network for structural dynamic response estimation on concrete gravity dam subjected to blast loads

Abstract

Social infrastructures such as dams are likely to be exposed to high risk of terrorist and military attacks, leading to increasing attentions on their vulnerability and catastrophic consequences under such events. This paper tries to develop advanced deep learning approaches for structural dynamic response prediction and dam health diagnosis. At first, the improved long short-term memory (LSTM) networks are proposed for data-driven structural dynamic response analysis with the data generated by a single degree of freedom (SDOF) and the finite numerical simulation, due to the unavailability of abundant practical structural response data of concrete gravity dam under blast events. Three kinds of LSTM-based models are discussed with the various cases of noise-contaminated signals, and the results prove that LSTM-based models have the potential for quick structural response estimation under blast loads. Furthermore, the damage indicators (i.e., peak vibration velocity and domain frequency) are extracted from the predicted velocity histories, and their relationship with the dam damage status from the numerical simulation is established. This study provides a deep-learning based structural health monitoring (SHM) framework for quick assessment of dam experienced underwater explosions through blast induced monitoring data.