Search

Summary: It would be beneficial to bridge design and maintenance if bridge damages and vehicle parameters could be identified. A simultaneous identification method for identifying bridge damage and vehicle parameters is proposed, considering more than one vehicle based on the output bridge responses. Bridge damage is localized in advance to decrease the bridge damage parameters. System parameters are identified by minimizing the error between the measured and identified responses. The proposed method is then verified with a numerical simulation, and the effects of environmental noise, damage degree, road surface roughness, and multiple damages on the identification accuracy are investigated, demonstrating the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The emerging noncontact vision-based displacement sensor system offers a promising alternative to the conventional sensors for quantitative structural integrity assessment. Significant advantages of the noncontact vision-based sensor include its low cost, ease of operation, and flexibility to extract structural displacement responses at multiple points. This study aims to link the measured displacement data to the quantification of the structural health condition, by validating the feasibility of simultaneous identification of structural stiffness and unknown excitation forces in time domain using output-only vision-based displacement measurement. Numerical analysis are first carried out to investigate the accuracy, convergence and robustness of identified results to different noise levels, sensor numbers, and initial estimates of structural parameters. Then, experiment on a laboratory scaled beam structure is conducted. Results show that the global stiffness of the beam specimen as well as external hammer excitation forces can be successfully and accurately identified from displacement measurement at two points using one camera. The proposed output-only time-domain identification procedure utilizing vision-based displacement measurement represents a low-cost method for either periodic or long-term bridge performance assessment. [ABSTRACT FROM AUTHOR]

Most of the existing bridge superstructure damage detection methods are based on acceleration or strain response due to ambient excitation. This paper proposes a bridge damage detection procedure that utilizes vehicle-induced displacement response without requiring prior knowledge about the traffic excitation and road surface roughness. This study is partially motivated by the recent advances in convenient measurement of structural displacements enabled by video-based sensors. Vehicle-bridge interaction analysis shows that when subjected to moving vehicles, the bridge displacement response is dominated by the first-order mode component. This justifies the proposed damage detection method that only requires the first-order mode shape curvature. While conventionally the mode shapes are extracted based on operational modal analysis, the first-order mode shape is extracted by directly analyzing the power spectral density functions of measured bridge displacement responses under vehicle excitations. Numerical simulations are carried out to investigate the feasibility and performance of the proposed damage detection method using three damage scenarios including damage at single, double, and multiple locations, each involving several extents of damage defined by the reduction in element stiffness. The results reveal that all the damage cases can be successfully identified. Furthermore, the damage detection performance is evaluated for cases involving different classes of road surface roughness and less measurement points. The study demonstrates the potential of the proposed model-free and time-efficient method for damage detection of bridge structures. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Most of the existing methods for identification of vehicle axle loads are based on a model with known system parameters. In this study, a new method is proposed to simultaneously identify bridge structural parameters and vehicle dynamic axle loads of a vehicle–bridge interaction system from a limited number of response measurements. As an inverse output-only identification problem, the estimation of unknown axle loads is incorporated in the framework of an iterative parametric optimization process, wherein the objective is to minimize the error between the measured and predicted system responses. A Bayesian inference regularization is presented to solve the ill-posed least squares problem for input axle loads. Numerical analyses of a simply-supported single-span bridge and a three-span continuous bridge are conducted to investigate the accuracy and efficiency of the proposed method. Effects of the vehicle speed, the number of sensors, the measurement noise, and initial estimates of structural parameters on the accuracy of the identification results are investigated, demonstrating the robustness and efficiency of the proposed algorithm. Finally, it is shown that the bridge dynamic response can be accurately predicted using the identified axle load histories and structural parameters. [ABSTRACT FROM AUTHOR]