Physics Informed Recurrent Neural Networks for Seismic Response Evaluation of Nonlinear Systems

TL;DR

This paper introduces a physics-informed recurrent neural network approach to efficiently evaluate the seismic response of nonlinear structures, offering a promising alternative to traditional finite element analysis for real-time structural assessment.

Contribution

The paper presents a novel physics-informed RNN model specifically designed for seismic response evaluation of nonlinear MDOF systems, improving computational efficiency over conventional methods.

Findings

The physics-informed RNN accurately predicts seismic responses.

The model reduces computational time compared to FEA.

It demonstrates potential for real-time structural health monitoring.

Abstract

Dynamic response evaluation in structural engineering is the process of determining the response of a structure, such as member forces, node displacements, etc when subjected to dynamic loads such as earthquakes, wind, or impact. This is an important aspect of structural analysis, as it enables engineers to assess structural performance under extreme loading conditions and make informed decisions about the design and safety of the structure. Conventional methods for dynamic response evaluation involve numerical simulations using finite element analysis (FEA), where the structure is modeled using finite elements, and the equations of motion are solved numerically. Although effective, this approach can be computationally intensive and may not be suitable for real-time applications. To address these limitations, recent advancements in machine learning, specifically artificial neural networks, have been applied to dynamic response evaluation in structural engineering. These techniques leverage large data sets and sophisticated algorithms to learn the complex relationship between inputs and outputs, making them ideal for such problems. In this paper, a novel approach is proposed for evaluating the dynamic response of multi-degree-of-freedom (MDOF) systems using physics-informed recurrent neural networks. The focus of this paper is to evaluate the seismic (earthquake) response of nonlinear structures. The predicted response will be compared to state-of-the-art methods such as FEA to assess the efficacy of the physics-informed RNN model.