Implementation of a bio-inspired two-mode structural health monitoring system

Abstract

A bio-inspired two-mode structural health monitoring (SHM) system based on the Naïve Bayes (NB) classification method is discussed in this paper. For the micro-vibration mode, a two-tier auto-regression with exogenous (AR-ARX) process is used to extract the expression array from the recorded structural time history while an ARX process is applied for the analysis of the earthquake mode. The health condition of the structure is then determined using the NB classification method. To verify the performance and reliability of the SHM algorithm, a downscaled eight-storey steel building was used as the benchmark structure. The structural response from different damage levels and locations was collected and incorporated in the database to aid the structural health monitoring process. Preliminary verification has demonstrated that the structure health condition can be precisely detected by the proposed algorithm. To implement the developed SHM system in a practical application, a SHM prototype was developed, which consists of three separate modules namely, the input sensing module, the transmission module, and the SHM platform. The vibration data is first measured by the deployed sensor, and subsequently the SHM mode corresponding to the desired excitation is chosen automatically to quickly evaluate the health condition of the structure. Test results from the ambient vibration and shaking table test showed that the condition and location of the benchmark structure damage can be successfully detected by the proposed SHM prototype system, and the information is instantaneously transmitted to a remote server to facilitate real-time monitoring. Implementing the bio-inspired two-mode SHM practically has been successfully demonstrated.

Data fusion approaches for structural health monitoring and system identification: Past, present, and future

“…measurements P Damage with small magnitudes may not be detected. Lin et al 40 Acc. from ambient vibration and strong motion P Less accurate for cases in strong motions.…”

“…It is demonstrated through a laboratory test that the proposed approach depends on the model classes, damage magnitudes, and locations, as well as the configuration of sensor instrumentation. In Lin et al, 40 a Naı¨ve Bayes-based approach is proposed to monitor the health condition of building structures under various ground excitations. Acceleration measurements collected from the strong ground motion and the velocity response recorded from the microvibrations are used to detect damage in structures under the earthquake mode and the micro-vibration mode, respectively.…”