Predicting crack through a well generalized and optimal tree-based regressor

Abstract

Purpose To avoid the structural defect, early crack detection is oneof the important aspects in the recent area of research. The purpose of this paper is to detect the crack before its failure by means of its position and severity. Design/methodology/approach This paper uses two trees based regressors, namely, decision tree (DT) regressor and random forest (RF) regressor for their capabilities to adopt different types of parameter and generate simple rules by which the method can predict the crack parameters with better accuracy, making it possible to effectively predict the crack parameters such as its location and depth before failure of the beam. Findings The predicted parameters can be achieved, if the relationship between vibration and crack parameters can be attained. The relationship yields the results of beam natural frequencies, which is used as the input value for the regression techniques. It is observed that the RF regressor predicts the parameters with better accuracy as compared to DT regressor. Originality/value The idea is used the developed regression techniques to identify the crack parameters which are more effective as compared to other developed methods because the alternate name of prediction is called regression. The authors have used DT regressor and RF regressor to achieve the target. In this paper care has been given to the generalization of the model, so that the adaptability of the model can be ensured. The robustness of proposed methods has been verified in support of numerical and experimental analysis.