Presenting new approaches based on Zhao–Atlas–Marks distribution and machine learning for detecting structural damage in steel beams

Purpose In this study, a new method for identifying damage in steel beams is presented. Ease of use, high accuracy, calculation volume reduction, output-only and reduction of health monitoring costs have been the main criteria in presenting the new methodology. Design/methodology/approach A new methodology for identifying damages in steel beams is presented based on the use of a Cone-Shape kernel distribution and machine learning. This research helps to improve the accuracy and ability to detect damage in steel beams. Findings To evaluate and ensure the performance of the proposed method, the results of k-fold as well as the results of the percentages of damage obtained from the test scenario were used. The results showed that all introduced machine learning algorithms have significant accuracy in identifying damage. By comparing the results obtained from all machine algorithms, it was found that the MLP Neural Network algorithm has a higher detection accuracy than other algorithms in identifying the intensity and location of damages. The very high capability of the presented methodology in damage detection is such that it detects the presence of several damages at the same time with different severities with very high accuracy. Originality/value The use of bilinear time–frequency distribution combined with machine learning algorithms is a completely new methodology for damage detection in steel beams. The capability of the presented methodology in damage detection is very remarkable. In such a way that the methodology can detect the presence of several damages at the same time with different severities with very high accuracy.