Modelling and forecasting of SHM strain measurement for a large-scale suspension bridge during typhoon events using variational heteroscedastic Gaussian process

Abstract

The modelling and forecasting (M&F) of strain measurement (as a kind of local structural responses) during typhoon events provides valuable insight into the structural condition assessment of large suspension bridges. However, the presence of time-dependent noise in reality can pose difficulties for forecasting the field data obtained by structural health monitoring (SHM) systems. Gaussian process regression (GPR), as a nonparametric model, can obtain probabilistic estimation outputs, but its constant noise assumption hampers the reliability of the forecasting model. In this study, Variational Heteroscedastic Gaussian Process (VHGP), a combination of variational approximation and heteroscedastic Gaussian process (HGP), is applied to perform modelling and forecasting for SHM strain field data during typhoon events because of its heteroskedasticity characteristics, higher forecasting accuracy and strong ability to quantify uncertainty. The proposed M&F method is exemplified by using SHM monitoring strain data acquired from the instrumented Tsing Ma Suspension Bridge during typhoon events. The results reveal that VHGP has a better regression accuracy and can obtain varying confidence intervals which reflect noise variations. Meanwhile, VHGP yields more robust forecasting results. The uncertainty analysis shows that VHGP is competent to evaluate the noise level change of strain responses brought by typhoons, providing a basis for conducting structural health condition assessment for large-scale bridges.