Machine learning prediction of maximum displacement of RC beams subjected to impact load

Abstract

In this study, a method using a surrogate model based on machine learning was considered to propose a fast and accurate estimation method for the maximum displacement of RC beams subjected to impact action. Six representative estimation models were used: generalized linear model (GLM), deep learning (DL), decision tree (DT), random forest (RF), gradient boosting decision tree (GBDT), and support vector machine (SVM). As a result, the applicability of machine learning to the estimation of the maximum displacement of RC beams subjected to impact load was confirmed. However, more detailed consideration is required by identifying the range of explanatory variables in the training data and outliers in the experimental data. Within the scope of this study, it was revealed that when using RF in the case that selected three explanatory variables are used, it is possible to estimate the plasticity ratio up to about 12 with a certain degree of accuracy.