Active learning guided automated cable force monitoring based on modified S-transform

Abstract

This study introduces an active learning-guided online cable force monitoring system based on a modified S-transform approach, addressing key challenges in real-time system identification: complex non-stationary excitations, computational efficiency, and robustness. The framework identifies cable tension during non-stationary wind loads, significantly improving accuracy and efficiency via an extended active learning Kriging method. It effectively detects potential outliers, identifies the cable's fundamental frequency using a data fusion technique, and calculates real-time cable force and tensile stress with empirical formulae. A comprehensive analysis, including numerical and sensitivity studies, shows an error rate of less than 4 % in all cases, proving the proposed framework's superior accuracy, efficiency, and robustness compared to traditional methods. Laboratory validations using cable test data and Jiu Zhou Bridge data demonstrate the system's stability, even under extreme conditions, such as during Super Typhoon Mangkhut, providing a reliable solution for real-world cable force online monitoring.