Application of Machine Learning Methods for Structural Health Monitoring

Abstract

This article explores the application of machine learning methods for diagnosing the condition of building structures. The research focuses on developing an effective methodology based on the analysis of the natural frequencies of structures using the finite element method to calculate dynamic characteristics. The machine learning algorithm selected is the random forest method, which provides high accuracy in the classification of defects, such as corrosion, cracks, and fatigue damage.
As a result of the experiments, a system was developed that can automatically identify the location of defects based on changes in the natural frequencies of the structure. The achieved performance metrics–accuracy, precision, recall, and F1-score–were all 1.00, confirming the effectiveness of the proposed methodology. This article highlights the potential application of the developed system for automated real-time monitoring of structural health, enabling timely detection of damage and extending the service life of structures. The work opens new perspectives for future research in structural health monitoring and diagnostics using modern machine learning methods.