A dynamic harmonic regression approach for bridge structural health monitoring

Structural damage in a bridge is defined as a significant deviation in the structural response from its standard operating conditions, not explainable by variations in external environmental and operational effects. However, environmental effects such as temperature fluctuations can cause significant seasonal variations in the structural response of a bridge and can mask its changes due to structural damage. This poses a challenge for structural health monitoring of bridges where reliable diagnosis of damage or deterioration is often related to isolation of the responses. To address it, a statistical damage-detection methodology is introduced where strain data are modelled using a dynamic harmonic regression time-series model. Prediction intervals of multi-step ahead forecasts from the dynamic harmonic regression model are then used as statistical control limits within which the observed phenomenon should fall under standard operating conditions. This single recursive structural health monitoring framework for automatic fitting and multi-step ahead forecasting of 1-min interval time-series strain data includes recorded temperature values and diurnal trends as regressors in the model to account for environmental variations. The potential of this method as a robust automatic structural health monitoring strategy is demonstrated on strain data sampled at 1-min interval from a full-scale damaged pre-stressed concrete bridge - before, during and after repair. The proposed method can capture both sudden and daily changes in structural response due to temperature effects, and a rolling multi-step ahead interval forecast was able to identify damage on back-cast data transitioning from a healthy state to a damaged state.

Automated summary

A statistical damage-detection methodology is introduced to improve the accuracy of bridge structural damage monitoring by using a dynamic harmonic regression time-series model. Prediction intervals from this model are used as statistical control limits to judge the structural health status. The potential and effectiveness of this method have been demonstrated on strain data sampled at 1-minute intervals from a full-scale damaged pre-stressed concrete bridge.