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This paper introduces a new bridge damage indicator, the moving reference influence function (MRIF), to detect bridge bearing damage using deflections inferred from vehicle accelerations. Recently, vehicle acceleration has been used to find the apparent profile (AP) of a bridge when a vehicle passes. This AP consists of bridge profile elevations and bridge deflection components. To describe the relationship between these deflection components and load, a MRIF is proposed for the first time in this paper. An error minimization process is used to find the MRIF and the road surface profile on the bridge. The vehicle acceleration signals used in the paper are assumed to be collected from a partially instrumented vehicle fleet. In the fleet, only the first axle acceleration is collected from each vehicle. To simplify the minimization process, both the MRIF and the bridge profile are represented by kernel density functions. The results show that the bridge profile can be accurately obtained and that bridge bearing damage can be identified from the MRIF. Both area and skewness of the MRIF are damage sensitive and can be used together to find the location and severity of bridge bearing damage. [ABSTRACT FROM AUTHOR]

Mode shapes are sensitive to the structural condition of bridges but a reasonable estimate of such changes require several accelerometers, which can be resource intensive. This paper obviates this problem through a novel structure health monitoring (SHM) approach for estimating modal parameters of bridges, including damage-induced changes of boundary conditions by using progressively re-deploying sensors along a monitored bridge. This concept of re-deployable sensors and subsequent use of a series of measurements allow extracting data from different bridge segments and also to get an indication of the condition of the bridge through frequency domain decomposition. Data from different segments are combined to estimate the global mode shape of the bridge and its gradient is observed to be indicative of support stiffness change. The concept is successfully tested through a full-scale field trial on a railway bridge in the Republic of Ireland, before and after the rehabilitation of its supports. The results are expected to guide future on-site measurement of damages due to flooding, scour, and other natural hazards, along with the effectiveness of intervention actions like repair and rehabilitation, providing a clear evidence base for practical value of SHM. [ABSTRACT FROM AUTHOR]

Featured Application: Acceleration based moving force identification is applied for weigh in motion and bridge damage detection purposes. This paper presents a new moving force identification (MFI) algorithm that uses measured accelerations to infer applied vehicle forces on bridges. Previous MFI algorithms use strain or deflection measurements. Statistics of the inferred forces are used in turn as indicators of global bridge damage. The new acceleration-based MFI algorithm (A-MFI) is validated through numerical simulations with a coupled vehicle-bridge dynamic interaction model programmed in MATLAB. A focussed sensitivity study suggests that results are sensitive to the accuracy of the vehicle velocity data. The inferred Gross Vehicle Weight (GVW), calculated by A-MFI, is proposed as the bridge damage indicator. A real weigh-in-motion database is used with a simulation of vehicle/bridge interaction, to validate the concept. Results show that the standard deviation of inferred GVWs has a good correlation with the global bridge damage level. [ABSTRACT FROM AUTHOR]

This paper describes a novel method for detecting bridge damage that uses a partially instrumented vehicle fleet, each vehicle being instrumented with just one accelerometer. Importantly, no prior knowledge of the bridge or individual vehicle properties is required. Firstly, a model simplification concept is proposed to calculate the displacement under the vehicle wheel from single acceleration measurements on a half-car model. Then, optimisation is used to find individual vehicle properties using simulated noisy measurements from a fleet of vehicles. Finally, accelerations from the fleet are used to find the 'apparent profile difference', which contains bridge deflection data. The difference is found to be independent of surface profile but dependent on vehicle axle weight differences and a moving reference influence function (MRIF). When the axle spacings and the relative axle weights are reasonably consistent in a subset of the fleet, the MRIF can be simplified into a compound MRIF. Even when the MRIF and individual axle weights are not available on-site, the shape of the compound MRIF can be determined through an iterative process and used to monitor bridge health condition. Bearing damage is represented in this paper as an increase of rotational resistance of the bearings. The numerical results show that the damage severity and location can be identified. • A new drive-by fleet monitoring concept is introduced using a partially instrumented vehicle fleet without knowing individual vehicle properties. • A new concept, vehicle model simplification, is introduced to enable monitoring bridges using partially instrumented vehicles. • Vehicle wheel displacement is successfully obtained from vehicle accelerations and used in structural health monitoring. • The relation between bridge deflection under the wheel and vehicle load is modelled as a moving reference influence function. This influence function is simplified to monitor bridges. [ABSTRACT FROM AUTHOR]