Damage detection in power transmission towers using machine learning algorithms

The purpose of this study is to utilize machine learning techniques to detect any damages that occurred in power transmission towers. In the first step, various machine learning algorithms were employed for a structural model with a small number of members so that the most efficient algorithm was selected based on the accuracy cri-terion. Then, the selected method was evaluated using acceleration responses obtained from larger power transmission towers in various states. It is worth mentioning that the parameters of the learning algorithms have been optimized by the Bayesian Optimization (BO) algorithm. Responses of three case studies, including 25-, 36-, and 160-member structures, under environmental vibrations, were polluted to 10% noise to simulate the field condition. Studies conducted on selecting the best classifier in detecting damages in these structures indicate that the Support Vector Machine (SVM) algorithm, with an approximate average of 96%, has the highest accuracy among different utilized algorithms. Moreover, the Linear Discriminant Analysis (LDA) algorithm, with an approximate average of 94%, is the second most accurate algorithm, and its computational cost is lower than the SVM algorithm. The results stemming from investigations of case studies show that the chosen features and method seem appropriate for identifying power transmission towers' damages. Finally, a study was conducted to investigate the impact of the number of input records on accuracy.