A Bayesian non-parametric clustering approach for semi-supervised Structural Health Monitoring

A key challenge in Structural Health Monitoring (SHM) is the lack of availability of datafrom a full range of changing operational and damage conditions, with which to train anidentification/classification algorithm. This paper presents a framework based onBayesian non-parametric clustering, in particular Dirichlet Process (DP) mixture models,for performing SHM tasks in a semi-supervised manner, including an online feature extrac-tion method. Previously, methods applied for SHM of structures in operation, such asbridges, have required at least a year’s worth of data before any inferences on performanceor structural condition can be made. The method introduced here avoids the need for train-ing data to be collected before inference can begin and increases in robustness as more dataare added online. The method is demonstrated on two datasets; one from a laboratory test,the other from a full scale test on civil infrastructure. Results show very good classificationaccuracy and the ability to incorporate information online (e.g. regarding environmentalchanges)

Grey-box models for wave loading prediction

The quantification of wave loading on offshore structures and components is a crucial element in the assessment of their useful remaining life. In many applications the well-known Morison's equation is employed to estimate the forcing from waves with assumed particle velocities and accelerations. This paper develops a grey-box modelling approach to improve the predictions of the force on structural members. A grey-box model intends to exploit the enhanced predictive capabilities of data-based modelling whilst retaining physical insight into the behaviour of the system; in the context of the work carried out here, this can be considered as physics-informed machine learning. There are a number of possible approaches to establish a grey-box model. This paper demonstrates two means of combining physics (white box) and data-based (black box) components; one where the model is a simple summation of the two components, the second where the white-box prediction is fed into the black box as an additional input. Here Morison's equation is used as the physics-based component in combination with a data-based Gaussian process NARX – a dynamic variant of the more well-known Gaussian process regression. Two key challenges with employing the GP-NARX formulation that are addressed here are the selection of appropriate lag terms and the proper treatment of uncertainty propagation within the dynamic GP. The best performing grey-box model, the residual modelling GP-NARX, was able to achieve a 29.13% and 5.48% relative reduction in NMSE over Morison's Equation and a black-box GP-NARX respectively, alongside significant benefits in extrapolative capabilities of the model, in circumstances of low dataset coverage