Wavelet Based Damage Detection Approach for Bridge Structures Utilising Vehicle Vibration

This paper investigates a wavelet-based damage detection approach for bridge structures. By analysing the continuous wavelet transform of the vehicle response, the approach aims to identify changes in the bridge response which may indicate the existence of damage. A numerical vehicle-bridge interaction model is used in simulations as part of a sensitivity study. Furthermore, a laboratory experiment is carried out to investigate the effects of varying vehicle configuration, speed and bridge damping on the ability of the vehicle to detect changes in the bridge response. The accelerations of the vehicle and bridge are processed using a continuous wavelet transform, allowing time-frequency analysis to be carried out on the responses of the laboratory vehicle-bridge interaction system. Results indicate the most favourable conditions for successful implementation of the approach.

Experimental validation of a drive-by stiffness identification method for bridge monitoring

An experimental investigation is carried out to verify the feasibility of using an instrumented vehicle to detect and monitor bridge dynamic parameters. The low-cost method consists of the use of a moving vehicle fitted with accelerometers on its axles. In the laboratory experiment, the vehicle–bridge interaction model consists of a scaled two-axle vehicle model crossing a simply supported steel beam. The bridge model also includes a scaled road surface profile. The effects of varying the vehicle model configuration and speed are investigated. A finite element beam model is calibrated using the experimental results, and a novel algorithm for the identification of global bridge stiffness is validated. Using measured vehicle accelerations as input to the algorithm, the beam stiffness is identified with a reasonable degree of accuracy.

Experimental Investigation of the Detection of Bridge Dynamic Parameters using a Moving Vehicle

This paper investigates the feasibility of using an instrumented vehicle to detect bridge dynamic parameters, such as natural frequency and structural damping, in a scaled laboratory experiment. In the experiment, a scaled vehicle model crosses a steel girder which has been adopted as the bridge model. The bridge model also includes a scaled road surface profile. The effects of varying vehicle model mass and speed are investigated. The damping of the girder is also varied. The bridge frequency and changes in damping are detected in the vehicle acceleration response in the presence of a rough road surface profile.

Dynamic axle force and road profile identification using a moving vehicle

The axle forces applied by a vehicle through its wheels are a critical part of the interaction between vehicles, pavements and bridges. Therefore, the minimisation of these forces is important in order to promote long pavement life spans and ensure that bridge loads are small. Moreover, as the road surface roughness affects the vehicle dynamic forces, the monitoring of pavements for highways and bridges is an important task. This paper presents a novel algorithm to identify these dynamic interaction forces which involves direct instrumentation of a vehicle with accelerometers. The ability of this approach to predict the pavement roughness is also presented. Moving force identification theory is applied to a vehicle model in theoretical simulations in order to obtain the interaction forces and pavement roughness from the measured accelerations. The method is tested for a range of bridge spans in simulations and the influence of road roughness level on the accuracy of the results is investigated. Finally, the challenge for the real-world problem is addressed in a laboratory experiment.

Parametric study to extract bridge frequencies from the dynamic response of passing vehicles

A conventional way to identify bridge frequencies is utilizing vibration data measured directly from the bridge. A drawback with this approach is that the deployment and maintenance of the vibration sensors are generally costly and time-consuming. One way to cope with the drawback is an indirect approach utilizing vehicle vibrations while the vehicle passes over the bridge. In the indirect approach, however, the vehicle vibration includes the effect of road surface roughness, which makes it difficult to extract the bridge modal properties. One solution may be subtracting signals of two trailers towed by a vehicle to reduce the effect of road surface roughness. A simplified vehicle-bridge interaction model is used in the numerical simulation; the vehicle - trailer and bridge system are modeled as a coupled model. In addition, a laboratory experiment is carried out to verify results of the simulation and examine feasibility of the damage detection by the indirect method.

A field experiment on a steel Gerber-truss bridge for damage detection utilizing vehicle-induced vibrations

A field experiment was conducted on a real continuous steel Gerber-truss bridge with artificial damage applied. This article summarizes the results of the experiment for bridge damage detection utilizing traffic-induced vibrations. It investigates the sensitivities of a number of quantities to bridge damage including the identified modal parameters and their statistical patterns, Nair’s damage indicator and its statistical pattern and different sets of measurement points. The modal parameters are identified by autoregressive time-series models. The decision on bridge health condition is made and the sensitivity of variables is evaluated with the aid of the Mahalanobis–Taguchi system, a multivariate pattern recognition tool. Several observations are made as follows. For the modal parameters, although bridge damage detection can be achieved by performing Mahalanobis–Taguchi system on certain modal parameters of certain sets of measurement points, difficulties were faced in subjective selection of meaningful bridge modes and low sensitivity of the statistical pattern of the modal parameters to damage. For Nair’s damage indicator, bridge damage detection could be achieved by performing Mahalanobis–Taguchi system on Nair’s damage indicators of most sets of measurement points. As a damage indicator, Nair’s damage indicator was superior to the modal parameters. Three main advantages were observed: it does not require any subjective decision in calculating Nair’s damage indicator, thus potential human errors can be prevented and an automatic detection task can be achieved; its statistical pattern has high sensitivity to damage and, finally, it is flexible regarding the choice of sets of measurement points.

Monitoring and model updating of an FRP pedestrian truss bridge

This paper presents the results of a real bridge field experiment, carried out on a fiber reinforced polymer (FRP) pedestrian truss bridge of which nodes are reinforced with stainless steel plates. The aim of this paper is to identify the dynamic parameters of this bridge by using both conventional techniques and a model updating algorithm. In the field experiment, the bridge was instrumented with accelerometers at a number of locations on the bridge deck, recording both vertical and transverse vibrations. It was excited via jump tests at particular locations along its span and the resulting acceleration signals are used to identify dynamic parameters, such as the bridge mode shape, natural frequency and damping constant. Pedestrianinduced vibrations are also measured and utilized to identify dynamic parameters of the bridge. For a complete analysis of the bridge, a numerical model of the FRP bridge is created whose properties are calibrated utilizing a model updating algorithm. Comparable frequencies and mode shapes to those from the experiment were obtained by the FE models considering the reinforcement by increasing elastic modulus at every node of the bridge by stainless steel plate. Moreover, considering boundary conditions at both ends as fixed in the model resulted in modal properties comparable/similar to those from the experiment. This study also demonstrated that the effect of reinforcement and boundary conditions must be properly considered in an FE model to analyze real behavior of the FRP bridge.