This study investigates the application of novel computational techniques for structural performance monitoring of bridges that enable quantification of temperature-induced response during the measurement interpretation process. The goal is to support evaluation of bridge response to diurnal and seasonal changes in environmental conditions, which have widely been cited to produce significantly large deformations that exceed even the effects of live loads and damage. This paper proposes a regression-based methodology to generate numerical models, which capture the relationships between temperature distributions and structural response, from distributed measurements collected during a reference period. It compares the performance of various regression algorithms such as multiple linear regression (MLR), robust regression (RR) and support vector regression (SVR) for application within the proposed methodology. The methodology is successfully validated on measurements collected from two structures -a laboratory truss and a concrete footbridge. Results show that the methodology is capable of accurately predicting thermal response and can therefore help with interpreting measurements from continuous bridge monitoring.
This paper illustrates how long-term measurements can be analysed to understand bridge behaviour under changing environmental conditions and how the developed understanding can help explain the performance of its critical components. Measurements from the Cleddau Bridge, a structure that has been continuously monitored for more than two years, are used to investigate thermal effects in steel box-girder bridges and, in particular, their bearings. Observed temperature distributions are very different to the recommended distributions in design codes (BS EN 1991-5:2003. These temperature distributions create plan bending of the box girder, which in turn impose forces at the bearings that have contributed to its wear. This paper investigates bearing movements of the bridge using numerical models, and estimates the resulting forces at the supports. A physics-based model of the bridge is created to which temperature distributions inferred from in-situ measurements are supplied as input. Model predictions are validated against measured deformations at the bearings. Subsequently the model is used to predict forces at the bearings due to plan bending and bearing locking.Results quantify the impact that thermal effects have on the performance of the bearings. They also highlight the significance of considering a range of temperature distribution scenarios that go beyond those given in the design codes in order to reliably evaluate thermal effects at the design stage.
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.
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