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

Fang, Xin; Li, Heng; Zhang, Sherong; Wang, Xiaohua; Wang, Chao; Luo, Xiaochun

doi:10.1016/j.dt.2022.04.012

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…Long short-term memory (LSTM) [30] was used by Zhang et al [31] to predict nonlinear structural response under earthquake loading. Fang et al [32] proposed a deep-learning-based structural health monitoring (SHM) framework capable of predicting a dam's structural dynamic responses once explosions are experienced using LSTM. Kohar et al [33] used 3D-CNN-autoencoder and LSTM to predict the force-displacement response and deformation of the mesh in vehicle crash-worthiness.…”

Section: Related Workmentioning

confidence: 99%

Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components

Bolandi¹,

Sreekumar²,

Li³

et al. 2023

Preprint

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Structural components are typically exposed to dynamic loading, such as earthquakes, wind, and explosions. Structural engineers should be able to conduct real-time analysis in the aftermath or during extreme disaster events requiring immediate corrections to avoid fatal failures. As a result, it is crucial to predict dynamic stress distributions during highly disruptive events in real time. Currently available high-fidelity methods, such as Finite Element Models (FEMs), suffer from their inherent high complexity and are computationally prohibitive. Therefore, to reduce computational cost while preserving accuracy, a deep learning model, Neuro-DynaStress, is proposed to predict the entire sequence of stress distribution based on finite element simulations using a partial differential equation (PDE) solver. The model was designed and trained to use the geometry, boundary conditions and sequence of loads as input and predict the sequences of high-resolution stress contours. The proposed framework's performance is compared to finite element simulations using a PDE solver.

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