Abstract Summary
A growing number of urban structures, such as quay walls and bridges, are reaching the end of their operational life, while the cost of fully replacing these structures is often prohibitive. Consequently, asset owners face the challenge of having to perform manual assessment based on engineering models, structural codes and expert judgment, for a large number of structures. The necessary expertise and large cost involved makes this type of assessment infeasible for large numbers of structures. In recent years, the wider adoption of structural health monitoring methods combined with advancements in sensor technology have enabled the collection of large amounts of data, with high spatial and temporal resolution. Additionally, multiple heterogeneous sources of data may be available. Utilizing spatially and temporarily correlated measurement data from heterogeneous sources to perform inference, model calibration and prediction for structures presents several challenges. In this work, an evolving, probabilistic, physics informed machine learning model based on Bayesian statistical model updating is proposed. We devise an approach for defining prior distributions on uncertain parameters that leverages the framework of imprecise probabilities to describe information obtained from highly varied sources, including prior knowledge and data, and expert opinion and physical constraints in terms of bounds on statistical expectations. This enhanced prior description is combined with measurement data and a physics-based model to yield the posterior distribution of uncertain, unobservable parameters. The feasibility of the proposed approach is demonstrated by investigating a synthetic problem of a quay wall.
