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This study proposes a novel moving force neural identifier (MFNI), a deep neural network designed for accurately estimating dynamic vehicular axle loads without knowing the vehicle speed and axle configurations (i.e., axle number and spacing). The MFNI architecture comprises a multi-time resolution encoder/decoder (MRED) and a dynamic moving force correction block (DMF-corr block). Firstly, the MRED with hierarchical resolution is developed to adapt to the complex dynamic inverse problem-solving. It can capture the multi-time resolution features of bridge responses and dynamic moving forces, and perform stably under various lengths of identification sampling windows. Then, the DMF-corr block limits the range of identified values by assuming that the vehicular excitation on the bridge is unidirectional, which enables the MFNI model to better determine the axle number, vehicle speed, as well as whether the axle is acting on the bridge. Numerical studies are implemented to confirm the accuracy and efficiency of the proposed MFNI approach, and a parametric sensitivity analysis is conducted to evaluate the robustness of the approach. Finally, a laboratory experiment confirms the practicability of the MFNI in detecting vehicular moving forces with a limited number of sensors and training samples. Both numerical and experimental results demonstrate the potential of the proposed method to serve as a nothing-on-road bridge weigh-in-motion system in practical engineering. • A deep-learning method for moving force identification is proposed, without the prior information on the axle configurations. • The proposed framework can adaptively determine the speed, axle count, and axle base during the moving force identification. • The feasibility of using simulated data for model training and subsequently predicting experimental loads is demonstrated. [ABSTRACT FROM AUTHOR]

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]

To address the limitations of current sensor systems for field applications, the research community has been actively exploring new technologies that can advance the state-of-the-practice in structural health monitoring (SHM). Thanks to the rapid advances in computer vision, the camera-based noncontact vision sensor has emerged as a promising alternative to conventional contact sensors for structural dynamic response measurement and health monitoring. Significant advantages of the vision sensor include its low cost, ease of setup and operation, and flexibility to extract displacements of any points on the structure from a single video measurement. This review paper is intended to summarize the collective experience that the research community has gained from the recent development and validation of the vision-based sensors for structural dynamic response measurement and SHM. General principles of the vision sensor systems are firstly presented by reviewing different template matching techniques for tracking targets, coordinate conversion methods for determining calibration factors to convert image pixel displacements to physical displacements, measurements by tracking artificial targets vs. natural targets, measurements in real time vs. by post-processing, etc. Then the paper reviews laboratory and filed experimentations carried out to evaluate the performance of the vision sensors, followed by a discussion on measurement error sources and mitigation methods. Finally, applications of the measured displacement data for SHM are reviewed, including examples of structural modal property identification, structural model updating, damage detection, and cable force estimation. [ABSTRACT FROM AUTHOR]

Cable is the most important component in cable-supported bridges and roof structures. Existing vibration methods for cable tension estimates are mainly based on measured acceleration responses. Such practice is relatively expensive and time-consuming due to the required installation of contact-type sensors and data acquisition systems. In this study, the novel noncontact vision-based sensor is proposed as an accurate and cost-effective alternative for the determination of cable tension forces. The noncontact measurement capacity of the vision sensor eliminates the need to access the cable to install conventional sensors, which may be highly difficult and risky. The proposed method is applied to measure cable forces for the cable-supported roof structure of the Hard Rock Stadium in Florida. To ensure that cable forces reach their design values, a series of field tests have been carried out during different roof erection stages. Satisfactory agreements are observed between measured cable forces by the vision-based sensor and the reference readings from load cells. The proposed method represents a convenient and low-cost method for either periodic or long-term monitoring of cable-supported structures. [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]

An extradosed bridge is a hybrid structure that combines the main elements of the classic cable-stayed bridge and continuous girder bridge. Almost all extradosed bridges have the main girder transversely restrained at the pier locations, which may cause extremely large transverse shear force and bending moment on bridge substructures during strong earthquakes. This study thoroughly investigates the efficacy of using the E-shaped steel damping bearing (ESSDB) for lateral seismic control of a prototype extradosed railway bridge. Firstly, high-fidelity finite element models are developed for nonlinear time history analyses for both the ESSDB isolated bridge and two conventional non-isolated configurations (i.e., rigid frame system and continuous system). Parametric sensitivity study is then carried out, which indicates a tendency of decreasing transverse deck displacements and increasing pier base transverse forces with the increase of the ESSDB yield force. The optimal ESSDB yield force is thus selected mainly based on a tradeoff between maximizing the structural force reduction and limiting the deformation demand of the ESSDB. Finally, through detailed comparisons of bridge seismic responses with the two non-isolated cases, it shows that the ESSDB can significantly reduce the transverse seismic force demands in bridge substructures, at a cost of a moderate increase in superstructure displacements and formation of permanent inelastic deformation in the ESSDB. It is concluded that the ESSDB can provide a simple and efficient solution to the transverse seismic control of the extradosed railway bridge. • This study investigated the seismic control of an extradosed railway bridge using E-shaped steel damping bearing (ESSDB). • High-fidelity finite element models were developed for nonlinear time history analyses of isolated and non-isolated bridges. • Parametric sensitivity analysis was conducted to determine the optimal ESSDB parameter. • ESSDB provided an efficient solution to seismic control, at a cost of a moderate increase in superstructure displacement. [ABSTRACT FROM AUTHOR]

Numerical simulation of turbulent wind field is essential for the time-domain buffeting analysis of long-span bridges. However, recent field measurements captured strong non-stationary features during extreme wind events, which may pose questions on the current bridge aerodynamic analysis based on the stationary assumption. Therefore, it is both necessary and realistic to perform non-stationary simulation of turbulent wind fields of long-span bridges. In this study, a spectral-representation-based non-stationary process simulation algorithm, which takes advantage of the FFT technique, is proposed using non-negative matrix factorization. In addition, an appropriate scheme is recommended to search the initial matrices for the factorization. After factorizing the decomposed spectrum, the FFT technique can be utilized to enhance the non-stationary simulation efficiency. Numerical examples demonstrate the feasibility and effectiveness of the factorization of the decomposed spectrum. Then, the non-stationary turbulent wind field on the long-span bridge deck is simulated. The computational efficiency, accuracy and applicability of the proposed method are compared with alternative approaches. Results show that the factorization and simulation agree well with the target ones. Therefore, the proposed approach can be used to simulate the non-stationary turbulent wind field in practice. • A NMF-based SRM is proposed to simulate the non-stationary turbulent wind field of long-span bridge deck. • A Wavelet-based method is developed to find the initial matrices properly for the NMF of the decomposed EPSD. • The proposed NMF-assisted method is conducive to the non-stationary turbulent wind field simulation in practice. • Field measured EPSD is selected to simulate the non-stationary turbulent wind field of long-span bridge deck. [ABSTRACT FROM AUTHOR]

• Propose a laser-and-camera (LAC) displacement measurement system that is portable, simple to assemble, and capable of measuring multipoint displacements in the field at a low manual and economic cost. • Provide an efficient video processing flow with an improved binarization strategy for pinpointing the laser spot's center to extract the displacement from the video collected by LAC devices. • Implement the laser-assisted vision-based measurement technique for structural modal identification for the first time. Vision-based techniques for modal analysis of full-scale civil infrastructure have been constrained by the challenge of collecting measurements of all points on a large structure using limited-resolution video devices. This paper proposes a portable, simple-to-assemble laser-and-camera (LAC) system capable of measuring multipoint displacements at a low cost. An efficient video processing flow with an improved binarization strategy for pinpointing the laser spot's center is provided to extract the displacement from the video collected by LAC devices. In addition, the laser-assisted vision-based measurement technique is implemented for structural modal identification for the first time. To evaluate the performance of LAC devices in displacement measurement and structural modal identification, an error analysis test, a measurement accuracy test, and a scaled bridge model experiment are conducted in succession. The results with different types of laser receiving panels and under variable light environments demonstrate the feasibility and application potential of the proposed LAC system and approach for structural health monitoring. [ABSTRACT FROM AUTHOR]

• Comparison of three most used mathematical modelling methods (MMMs) was analyzed. • The sampling design for high-dimensional variables was well implemented by LHS. • Accuracies of the three MMMs were acceptable verified by the bridge model updating. • Kriging and neural network have better accuracy than quadratic polynomial model. • Kriging is more time-consuming, while neural network is better in application. Mathematical modelling method (MMM) is widely applied in engineering issues. But the performance of MMMs is rarely compared, especially with high dimensional variables. This research focused on the comparison among three mostly used MMMs, i.e., quadratic polynomial (QPMM), kriging (KMM) and neural network (NNMM), based on model updating of a bridge with 13 variables and in-situ data. Firstly, 200 indetermined samples by Latin Hypercube sampling were generated and the relevant responses were computed by finite element model (FEM). Secondly, explicit expressions of responses by MMMs were established. Finally, with the optimization program and explicit expressions, updated variable groups were optimized. From the process and results of FEM updating, it shows that all the MMMs lead to an acceptable result as the discrepancies were reduced sharply. In terms of accuracy, KMM and NNMM are better than QPMM, but in terms of efficiency, KMM is time-consuming. [ABSTRACT FROM AUTHOR]

Wind and seismic resistance design are two critical elements in structural design of long-span cable stayed bridges. In order to investigate the influences of the originally equipped seismic viscous dampers on the wind-resistant performance of bridges, full bridge aeroelastic wind tunnel tests have been carried out on the overall scaled model of Hangzhou Bay Cable Stayed Bridge. It is found that the bridge exhibits good wind-resistant performance, while seismic viscous dampers have very minor effects on the vertical buffeting displacement of the mid-span girder. The study also shows that optimizing the damper parameters could only slightly improve the ability of the dampers to reduce wind vibrations. Therefore, when designing long span cable stayed bridges, the bridge seismic design and wind-resistant design can be carried out separately, without considering the wind-resistant effects of the dampers which aim at dissipating earthquake energy. [ABSTRACT FROM AUTHOR]