# Failure Evaluation of Steel Plate Shear Walls in Multi-Storey Steel Buildings Under Seismic Excitation Using Convolutional Neural Networks

## TL;DR

This paper introduces a method using computer vision and neural networks to assess damage on steel shear walls in buildings during earthquakes.

## Contribution

A novel CNN-based methodology for automatic failure evaluation of steel shear walls under seismic conditions is proposed.

## Key findings

- CNNs accurately predict failure distribution on shear plate walls using building geometry and seismic intensity as inputs.
- The methodology was tested on random buildings with satisfactory accuracy.
- The approach can be integrated into structural digital twin systems for real-time evaluation.

## Abstract

Multi-storey steel buildings may be susceptible to structural damage under seismic loading. Shear plate walls are often integrated in the structural system of this type of buildings in order to restrict the lateral response. This article aims, therefore, to propose a methodology for the automatic evaluation of failure on the shear plate walls of multi-storey steel buildings using computer vision. Physics-based non-linear dynamic finite element models have been developed and solved for a range of geometries, shear plate wall thicknesses and seismic loading from past events. Images depicting failure on shear plate walls given as equivalent plastic strain contour plots are included in the output data of the parametric simulations. Then, Convolutional Neural Networks (CNNs) are introduced, predicting the failure distribution on shear plate walls. The input parameters are the geometric properties of the buildings and the seismic event intensity, and the output parameters is the equivalent plastic strain images. This scheme was tested on random buildings with satisfactory accuracy. The proposed methodology can be adopted and used within structural digital twin solutions.