Failure mode prediction of reinforced concrete columns using machine learning methods

In this article, new efficient methods are presented to classify failure modes in reinforced concrete columns. For this purpose, machine learning techniques were utilized with consideration of laboratory datasets collected from the literature. Two different approaches, including decision tree and artificial neural network, have been studied to determine the failure mode of the columns. The variables used to estimate the failure mode were compressive strength of the concrete, span-to-depth ratio, axial load ratio, longitudinal reinforcement ratio, volumetric transverse reinforcement ratio, yield stress of longitudinal reinforcement, and yield stress of transverse rein-forcement. A comparison study between the two introduced models indicated that the proposed decision tree provides a desirable accuracy and could specify the failure mode, with no need to a complex calculation. The proposed model has many applications in structural engineering such as seismic evaluation, retrofitting, and rehabilitation as a suitable tool for estimating the failure modes in reinforced concrete columns.

Automated summary

This article introduces a new efficient method for classifying failure modes in reinforced concrete columns using machine learning techniques. Through a comparison study, the decision tree model is found to provide desirable accuracy and computational simplicity in determining the failure mode.