Fitting Cyclic Experimental Load-Deformation Data to The Pivot Hysteresis Model Using Genetic Algorithm

TL;DR

This paper presents a method to model cyclic load-deformation hysteresis data using a pivot hysteresis model optimized with a genetic algorithm, enabling accurate simulation and prediction of material and structural behavior under cyclic loading.

Contribution

It introduces a novel approach combining data resampling and genetic algorithms to accurately fit hysteresis models to experimental data, improving simulation fidelity.

Findings

Effective data resampling reduces measurement data size.

Genetic Algorithm accurately fits model parameters to experimental data.

Method enables prediction of material and structural performance under cyclic loads.

Abstract

Understanding the linear or nonlinear relationship between load and deformation in structural materials or structural frames is a key to a proper and a well-represented simulation. This research is dedicated to model a cyclic load-deformation hysteresis relationship, captured from experimental results, and utilize it to represent the cyclic hysteresis data. The Genetic Algorithm is used to find the best parameters to introduce the model and to minimize the deviation between the simulation and the experimental results. In other words, the parameters associated with the loading response of any displacement pattern, are found, while minimizing the deviation between simulation loading response and the loading data carried out from the experiment. First, to reduce the data size recorded with measuring devices or Linear Variable Differential Transformers (LVDTs in short), a data resampling technic is proposed. Second, the resampled data is used in Genetic Algorithm to seek for the best parameters to describe the model. This method could be used to train models to predict the capacity and performance of a materials and frames when they are exposed to any deformation patterns. Numerous examples are shown at the end of the paper.