Ensemble learning-based structural health monitoring of a bridge using an interferometric radar system

Abstract

AbstractDue to the increase in population, urbanisation, transportation development, and the aging of existing bridges, there is a growing need for new and rapid structural health monitoring (SHM) of bridges. To address this challenge, a method that stands out is the use of an interferometric radar system-based device, specifically Image by Interferometric Survey-Frequency for structures (IBIS-FS). Known for its portability and non-intrusive operation, IBIS-FS does not require direct contact with the bridge. This study utilised IBIS-FS to capture a pedestrian bridge’s natural frequencies and mode shapes. The data obtained were found to be consistent with results from finite element models, demonstrating the reliability of IBIS-FS in capturing modal parameters. Building upon this foundation, the study then explores the application of advanced ensemble-based machine-learning techniques. By leveraging the data acquired from IBIS-FS, algorithms such as Random Forest, Gradient-boosted Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost) are used for bridge damage detection. These machine-learning (ML) techniques are suited to analyse the incomplete modal parameters of bridges, as captured by IBIS-FS. The study focuses on using these algorithms to interpret the changes in modal parameters, specifically identifying damage as a reduction in the stiffness of elements. This approach allows for a comprehensive analysis, where the modal parameters, including mode shapes and natural frequencies altered by varying noise levels, are fed as input to the models. It was observed that all three ML methods, with Random Forest in particular, can effectively identify the location and severity of damage, demonstrating an efficient training process. The robustness of GBDT and XGBoost in handling complex data sets also shows great promise for their application in bridge damage detection. Collectively, these results underscore the potential of combining advanced ML techniques like Random Forest, GBDT, and XGBoost with the data acquired from IBIS-FS.