Extraction of contact-point response in indirect bridge health monitoring using an input estimation approach

Abstract

Identification of bridge dynamic properties from moving vehicle responses presents several practical benefits. However, a problem that arises when working with vehicle responses for indirect bridge health monitoring is that the bridge dynamics may get low-pass filtered by the vehicle suspension dynamics, rendering the identification of higher bridge modes difficult. Instead, the contact-point (CP) response—response at the contact point of the vehicle with the bridge surface—is a superior alternative to the vehicle response for identifying the bridge modal features. In the $$\text {CP}$$ response, the vehicle dynamics is suppressed and the higher bridge modes are significantly enhanced, thus making it better suited for modal identification. Extracting the $$\text {CP}$$ response from vehicle response is, however, not straightforward for a multiple degrees of freedom (MDoF) vehicle model. In this study, a novel methodology is proposed to extract $$\text {CP}$$ acceleration from the measured vehicle acceleration using the knowledge of the $$\text {MDoF}$$ vehicle dynamics. The $$\text {CP}$$ acceleration is shown to act as a base-excited input to the test vehicle and is extracted via a joint input-state estimation procedure employing a Gaussian process latent force model (GPLFM). Numerical case studies are considered to assess the quality of the $$\text {CP}$$ acceleration estimated with the proposed approach. It is found that the proposed method performs well and the extracted $$\text {CP}$$ acceleration response is able to reduce the effect of vehicle dynamics and improve the prominence of higher bridge modes.