Experimental Study of Online Structural Health Monitoring Using the Recursive Subspace Approach

Abstract

As a pivotal component in advancing the sustainable development goals (SDGs), structural health monitoring (SHM) has garnered increasing attention in the field of civil engineering. Considering the various approaches, model-based SHM is the most prevalent and remains highly effective due to its theoretical framework and nondestructive nature, creating a robust framework for effective SHM, enabling early detection of issues, and supporting informed maintenance strategies. Through decades, stochastic subspace identification (SSI) has been proven, and recursive SSI (RSSI) has been consequently applied for model-based SHM due to its ability to track modal parameters and generate accurate models. However, online validation through structural experiments has yet to be conducted with large-scale specimens. In this study, a shaking table experiment is conducted to validate the online implementation of RSSI for tracking time-varying modal parameters in real time. The full-scale specimen, experimental setup, and test framework are first described with great detail, and a numerical model is developed through a pretest using the shaking table system located in Taiwan. Subsequently, the simulation study provides numerous suggestions for experimental implementation. The experimental study then demonstrates that the proposed approach not only enables an online identification but also produces an accurate dynamic model. Besides, practical measures are recommended to fulfill online processing through the comprehensive simulation and experiential studies, especially those related to the user-defined parameters and ambient excitations. The results evidence that the SHM systems based on RSSI can effectively track the changes of dynamic characteristics under ambient excitations, ultimately facilitating the assessment and maintenance of structures.