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Bridge stay cables are susceptible to fatigue damage caused by cyclic traffic loads. However, the effects of road roughness on fatigue assessment are often ignored, even though it is an essential factor that amplifies the response in vehicle–bridge interaction (VBI) systems. To address this gap, this study proposes a framework for cable fatigue assessment considering road roughness. The framework includes the VBI analysis method accounting for road roughness, the generation of random traffic flow, and the evaluation of cable fatigue. A case study based on a typical cable-stayed bridge is conducted. The results show that the proposed framework can effectively and feasibly evaluate cable fatigue. It is found that a poor road surface can reduce the fatigue life of cables, causing up to 4.47 times more damage compared to smooth road conditions. The decline of cable fatigue life caused by road roughness is primarily concentrated on the cables around the main towers, while the other cables are less affected by road roughness. Based on the measured traffic volume under poor road surface conditions, the estimated minimum fatigue life of the cable is 64 years. Moreover, the study indicates that cable fatigue damage rises with increasing traffic volume. These findings highlight the importance of considering road roughness and maintaining bridge decks to prevent the reduction of cable fatigue life. • It presents a framework for evaluating the fatigue life of bridge stay cables considering road roughness. • The poor road surface will reduce the fatigue life of bridge stay cables. • The fatigue life of cables near the main tower is more susceptible to road roughness. [ABSTRACT FROM AUTHOR]

[Display omitted] The dynamics of vehicle-bridge interaction (VBI) involve the adoption of two-dimensional (2D) and three-dimensional (3D) vehicle models in simulations. It is worth noting that the 2D vehicle model lacks several crucial degrees of freedom and a complete road roughness compared to the 3D vehicle model. Despite the common usage of the 2D vehicle model in VBI simulations, the difference between the two models has not received sufficient attention in previous studies. Therefore, this study aims to quantitatively assess the differences in bridge response between the 2D and 3D vehicle models. To achieve this goal, the study employs a VBI modeling method based entirely on general finite element software first. Then, a series of comparisons between the 2D and 3D vehicle models are implemented through a steel composite girder bridge and a cable-stayed bridge. The results indicate that road roughness significantly contributes to the observed differences. Specifically, the 2D vehicle model generates larger bridge responses than the 3D vehicle model, primarily due to the absence of rolling and vibration cancellation effects. Furthermore, the differences between the two models increase as the road surface roughness worsens, with a maximum difference of 25.07 % in the cable-stayed bridge's mid-span acceleration. Notably, the discrepancies in bridge acceleration are more significant than those in displacement or cable stress. Adopting the 2D vehicle model in VBI simulations may lead to more conservative engineering recommendations, resulting in higher economic costs and greater consumption of building materials. Thus, this study emphasizes the importance of considering the 3D vehicle model in VBI simulations, particularly for bridges with poor road conditions. [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]