The scanning of bridge surface roughness by the test vehicle is a coupled and non-stationary problem since the bridge deflection caused by vehicles will inevitably enter into the vehicle response. To this end, a two-step procedure is proposed to retrieve the bridge surface profile from the noise-contaminated responses of a two-axle vehicle moving over bridges. Central to this is the elimination of the bridge deflection from the estimated unknown input to the test vehicle system. First, the extended Kalman filter with unknown inputs (EKF-UI) algorithm is extended to formulating the state-space equations for the moving vehicle over the bridge. Analytical recursive solutions are derived for the improved vehicle states and the unknown input vector consisting of the vehicle–bridge contact displacement and surface profile. Second, the correlation between the cumulated contact residuals and contact displacements for the two axles is approximately defined by using the vehicle’s parameters and location on the bridge. Then, the surface profile is retrieved from the unknown input by removing the roughness-free contact (bridge) displacement, calculated with no prior knowledge of bridge properties. The efficacy of the proposed procedure was validated by the finite element method and demonstrated in the parametric study for various properties of the system. It is confirmed that the retrieved bridge surface profile is in excellent agreement with the original (assumed). For practical use, the vehicle is suggested to run at a not-too-high speed or in a too noisy environment. The proposed technique is robust with regard to vehicle mass and bridge damping.

Dual-function amplifiers are proposed for the first time herein for enhancing the capability of a scanning test vehicle for bridges. To start, closed-form solutions are derived for the dynamic responses of the amplifier-vehicle-bridge system with a moving test vehicle. Then, the dynamic amplification factors of the amplifier and vehicle are presented for assessing the bridge/vehicle and vehicle/amplifier transmissibility. It was known that the spectrum of the vehicle may be hindered for extracting the bridge frequencies because of vehicle frequency and rough pavement. Two differentially tuned amplifiers are called on to tackle the problem: one (i.e., the vehicle damper) is to suppress the effect of vehicle’s frequency, acting like the tuned mass damper (TMD), and the other (i.e., the bridge amplifier) is to enlarge the amplitude of bridge frequency of concern. Based on the parametric study, it is concluded that (1) the bridge amplifier performs better than the vehicle one in extracting bridge frequencies by increasing their visibility in the spectrum; (2) the effect of vehicles frequency can be suppressed by tuning the vehicle damper such that it functions like a TMD of the vehicle; (3) by tuning the bridge amplifier to any of the first few bridge frequencies, the latter can be well detected even for rough pavement.