Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components

• URL: http://arxiv.org/abs/2301.02580v1
• Date: Mon, 19 Dec 2022 03:02:26 GMT
• Title: Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components
• Authors: Hamed Bolandi, Gautam Sreekumar, Xuyang Li, Nizar Lajnef, Vishnu Naresh Boddeti
• Abstract summary: It is crucial to predict dynamic stress distributions during highly disruptive events in real-time. Deep learning model, Neuro-DynaStress, is proposed to predict the entire sequence of stress distribution based on finite element simulations.
• Score: 10.588266927411434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 performance of the proposed framework is compared to finite element simulations using a PDE solver.

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