On the use of drive-by measurement for indirect bridge monitoring

Abstract

Indirect bridge monitoring methods, using the responses measured from vehicles passing over bridges, are under development for about a decade. A major advantage of these methods is that they use sensors mounted on the vehicle – no sensors or data acquisition system needs to be installed on the bridge. Most of the proposed methods are based on the identification of dynamic characteristics of the bridge from responses measured on the vehicle, such as natural frequency, mode shapes and damping. In addition, some of the methods seek to directly detect bridge damage based on the interaction between the vehicle and the bridge. A critical review of indirect methods for bridge monitoring is presented and discussion and recommendations on the challenges to be overcome for successful implementation in practice are provided.A novel Short Time Frequency Domain Decomposition (STFDD) method is proposed to estimate bridge mode shapes from the dynamic response of the vehicle. In Frequency Domain Decomposition (FDD), several segments are defined on the bridge and the measurement is performed using two instrumented axles. Here, the FDD method is employed in a multi-stage procedure applied to the bridge segments in sequence. A rescaling process is used to construct the global mode shape vector. Numerical case studies are investigated using Finite Element (FE) models of vehicle bridge interaction (VBI) to validate the effectiveness and performance of the proposed method. In other indirect bridge identification methods, the road profile may excite the vehicle, making it difficult to detect the bridge modes. This is addressed using two concepts: applying external excitation to the bridge and subtracting signals in the axles of successive trailers towed by the vehicle. The results obtained from the numerical investigation demonstrate that the proposed method can estimate the bridge mode shapes with acceptable accuracy. The sensitivity of the method to added white noise is also investigated.A novel algorithm for bridge damage detection based on the mode shapes estimated from a passing vehicle is also presented. The bridge response at the moving coordinate is measured from an instrumented vehicle with laser vibrometers and accelerometers. A modified version of the Short Time Frequency Domain Decomposition (STFDD) method is applied to the measured responses. The bridge mode shapes are estimated with high resolution as is appropriate for damage detection. A damage index based on mode shape squares (MOSS) is used to detect the presence and location of the damage. A numerical case study of a half-car model passing over a bridge is described which validates the performance of the proposed approach. Several damage scenarios are considered including different locations and severities. It is shown that the presence and location of the damage can be detected with acceptable accuracy when the vehicle is moving very slowly. In addition, the performance of the method using higher vehicle speeds is investigated and shows that the approach works well for speeds up to 8 m/s. The sensitivity of the algorithm to measurement noise is also studied by adding several levels of noise to the responses measured on the vehicle.It is shown theoretically that such a response includes three main components; vehicle frequency, bridge natural frequency and a vehicle speed pseudo-frequency component. The Empirical Mode Decomposition (EMD) method is used to decompose the signal into its main components. A damage detection method is proposed using the Intrinsic Mode Functions (IMFs) corresponding to the vehicle speed component of the response measured on a passing vehicle. Numerical case studies using Finite Element modelling of Vehicle Bridge Interaction are used to show the performance of the proposed method. It is demonstrated that it can successfully localise the damage location in the absence of road profile. A difference in the acceleration signals of healthy and corresponding damaged structures is used to identify the damage location in the presence of road profile.A truck-trailer system is assumed, equipped with an external excitation at a frequency close to one of the bridge natural frequencies. The excitation makes the bridge response dominant at its natural frequency. The acceleration responses are measured on two following axles of the vehicle. It is shown that the amplitude of the signal includes the bridge mode shape data. The energy of the responses measured on two following axles is obtained using the Hilbert Huang Transform. It is shown that the bridge mode shape can be constructed with high resolution using a rescaling process. The presence of road roughness introduces additional contributions to the response measured on the vehicle, in addition to the bridge response. The concept of subtraction of the responses measured from two identical axles is used to remove the effect of road roughness