A Combined Machine Learning and Model Updating Method for Autonomous Monitoring of Bolted Connections in Steel Frame Structures Using Vibration Data

Abstract

This research paper presents a novel structural health monitoring strategy based on a hybrid machine learning and finite element model updating method for the health monitoring of bolted connections in steel planer frame structures using vibration data. Towards this, a support vector machine model is trained with the discriminative features obtained from time history data, and those features are used to distinguish between damaged and undamaged joints. An FE model of the planer frame is considered where the fixity factor (FF) of a joint is modeled with rational springs and the FF of the spring is assumed as the severity level of loosening bolts. The Cat Swarm Optimization technique is further applied to update the FE model to calculate the fixity factors of damaged joints. Initially, the method is applied to a laboratory-based experimental model of a single-story planer frame structure and later extended to a pseudo-numerical four-story planer frame structure. The results show that the method successfully localizes the damaged joints and estimates their fixity factors.