ABSTRACT
For civil engineering infrastructure, the objectives of monitoring are often to quantify current performance behavior and seek to detect changes in behavior or status that could be attributed to component damage or degradation. For structures, responses such as displacements and stress changes are often strongly influenced by the effects of wind loading, traffic and temperature, the latter presenting via correlated daily and seasonal variations between structural components, often of differing materials. Displacements due to temperature change can be significant, and loading imparted by traffic is often only indirectly measured. In contrast, comparatively small deformations of the structure can be expected due to component degradation or localized failure. Without a principled approach to data analysis there is a risk that such subtle changes will be go unnoticed due to the complex daily, seasonal, and random volatility of the variable actions.
Through monitoring two long span bridges in Normandy, France, COWI have developed analysis methods that use measured data from several different sensor types to deliver insights with regards to the defined monitoring objectives. Bayesian multivariate linear regression has been found to be a versatile and scalable model class for predicting structural responses, owing to its compatibility with the linear system theory by which structures are typically analyzed. In this class of model, action-related measurement channels (e.g., temperatures, wind speeds) are typically used as regressors, with response measurements being the dependent variables. Response predictions obtained from deployed models serve to contextualize new measured data and identify anomalous responses. Sophisticated model variants, in which some of the parameters are time-varying, can serve to expose any long-term trends or changes that may be evident, yet otherwise masked, within the measured data. This approach constitutes an efficient automated methodology for processing large data quantities that scales well. Inferred model parameters deliver information that is relevant for digital twinning and asset management purposes. The practical application of this methodology is illustrated via a few case studies analyzing real data.
