An Automated Tool for Long-Term Structural Health Monitoring Based on Vibration Tests

Abstract

Structural Health Monitoring (SHM) programs play an essential role in the field of civil engineering, especially for assessing safety conditions involving large structures such as viaducts, bridges, tall buildings, towers, and old historical buildings. Typically, an SHM process needs to be based on a trustful strategy for detecting structural novelties or abnormal behaviors. Usually, such an approach is complemented with human inspection and structural instrumentation routines, where the latter requires proper hardware equipment and software tools. However, most strategies already published in this topic mainly focus on modal identification procedures and tracking their outputs i.e., structural modal parameters. Such approaches usually lead to high computational costs and can still be insensitive to minor changes in structural behavior, thus missing crucial damage scenarios in their initial manifestations. To circumvent these drawbacks, recent studies showed that the use of symbolic representations derived directly from raw time-domain data (e.g., acceleration measurements) obtained from vibration tests could provide more damage-sensitive responses with lower computational effort. The proposed methodology employs a clustering technique over such symbolic representations within a moving time-window framework and uses a single-valued index to indicate if a novelty (i.e., structural damage and/or interventions, such as maintenance/repair) is present in the acquired data. Two practical studies—a highway and a railway bridge located in France—show that the proposed tool provide an unsupervised and adaptive scheme for automated real-time SHM applications based on vibration tests.