SHM of bridges: characterising thermal response and detecting anomaly events using a temperature-based measurement interpretation approach

Abstract

Abstract:A major bottleneck preventing widespread use of Structural Health Monitoring (SHM) systems for bridges is the difficulty in making sense of the collected data. Characterising environmental effects in measured bridge behaviour, and in particular the influence of temperature variations, remains a significant challenge. This paper proposes a novel data-driven approach referred to as Temperature-Based Measurement Interpretation (TB-MI) approach to solve this challenge. The approach is composed of two key steps -(i) characterisation of thermal effects in bridges using a methodology referred to as Regression-Based Thermal Response Prediction (RBTRP) methodology, and (ii) detection of anomaly events by analysing differences between measured and predicted structural behaviour. Measurements from a laboratory truss structure that is setup to simulate a range of structural scenarios are employed to evaluate the performance of the TB-MI approach. The study examines how the predictive capability of the RBTRP methodology is influenced by dimensionality reduction and measurement down-sampling, which are common pre-processing techniques used to deal with high spatial and temporal density in measurements. It also proposes a novel anomaly detection technique referred to as signal subtraction method that detects anomaly events from timeseries of prediction errors, which are computed as the difference between in-situ measurements and predictions obtained using the RBTRP methodology. Results demonstrate that the TB-MI approach has potential for integration within data interpretation frameworks of SHM systems of full-scale bridges. Keywords: structural health monitoring; thermal effects; data-driven methods; anomaly detection; measurement interpretation. Rolands AcknowledgmentsThe authors would like to express their gratitude to Bill Harvey Associates and Pembrokshire County Council for providing access to the measurements of the Cleddau Bridge, and to Elena Barton (National Physical Laboratory) for providing the data from the National Physical Laboratory Footbridge project.

Data-driven approaches for measurement interpretation: analysing integrated thermal and vehicular response in bridge structural health monitoring

“…PCA finds a set of principal component vectors defining an orthogonal transformation from the original set of variables which are linearly-correlated to a new set of variables which are uncorrelated. According to [28], the first one-third of the principal components covers 99.99% of the variability in temperatures. Hence these principal components alone are sufficient as input to the regression models.…”

“…A sketch of the truss depicting its principal dimensions and the location of sensors is shown in Figure 5. Further details on the truss are available in authors' previous work [28]. Temperature variations are simulated with three infrared heating lamps ( Figure 5).…”

“…Damage to a bridge component will modify prior correlations since bridges are typically well-connected structural systems such that damage affects load paths within the structure. In previous studies, signal subtraction method (SSM) [28] and cointegration [15] have been shown to detect anomaly events better than other signal processing techniques such as moving principal component analysis and moving fast Fourier transform [34]. Therefore, in this study, SSM and cointegration are employed to analyse PE signals for anomalies.…”

“…SSM is a novel technique proposed in [28]. In SSM two PE signals are linearly combined to generate a subtracted signal, which is then analysed for anomalies.…”

Long-term strain measurements of traffic and temperature effects on an RC bridge deck slab strengthened with an R-UHPFRC layer

“…Obviously, the bigger the temperature variation, the bigger are the induced strains. This dependency can be used to verify the reliability of the sensors [21]. The longitudinal gauge of Group 1 followed the thermal amplitude only until spring 2018 when this gauge no longer functioned properly, probably because of humidity due to improper sealing.…”