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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]

The Π -shaped open section girder is a very common section, which has been widely used in the construction of modern bridges. However, compared with a streamlined box girder, it is easy to cause vortex-induced vibration (VIV) in bridges due to its bluff section shape and poor aerodynamic stability. In this paper, a typical open section with a height–width ratio of 10:1 is taken as the research object. Through wind tunnel tests and numerical simulation technology, the time-history data of VIV of the Π -shaped section are obtained. By selecting three different neural network models to establish the relationship between external parameters (wind attack angle, wind speed, turbulence) and structural response parameters (the onset wind speed of VIV, the length of the lock-in interval). After comparing different machine learning methods, the most suitable machine learning model is selected to predict the VIV response characteristics of the main girder. The research results show that the typical open section has a serious VIV phenomenon. The BP neural network model has the best performance in predicting the VIV performance of the open section in terms of calculation speed, prediction accuracy and solving nonlinear adaptability. At the same time, it is found that different characteristic parameters of the incoming wind field have different influences on the prediction results of the VIV response. Among them, the wind attack angle is the key parameter that affects the onset wind speed of VIV, but the turbulence intensity has a greater impact on the maximum amplitude of VIV. At the same time, the optimized BP model can predict the characteristic parameters of VIV response under unknown wind fields. This paper provides a new idea to study the effect of different incoming flow parameters on the performance of section VIV and further improves people's understanding of VIV system. [ABSTRACT FROM AUTHOR]

The paper deals with the problem of obtaining a given dynamic behavior of simply supported bridge beams from the impact of rolling stock. Resonant vibrations of beams become a reality at high speeds if the train is formed of identical cars, which is typical for passenger trains. The phenomenon of suppression of beam vibrations by a train, known from publications, is possible only with an exact ratio of the length of the car and the beam. This significantly reduces the possibilities of choosing the necessary spans of beams. Consideration of issues of interaction in the "bridge–track—train" system for security purposes requires the involvement of a rather complex mathematical apparatus and appropriate software. The paper proposes a new method for determining the specified dynamic behavior of bridge beams, suitable for any spans and available to engineers at the stage of pre-design assignment of dynamic parameters of beams. The requirements that are necessary and sufficient conditions for preventing the resonance of bridge beams on the HSR are determined. At the same time, it does not require the involvement of software based on a complex mathematical technique. [ABSTRACT FROM AUTHOR]

Simply supported bridges are the main bridge types in many transportation systems, and their damage can significantly reduce their load-carrying capacity. To detect possible damage, the time history and spatial distribution of the static responses of bridges (deflection, rotation, and strain influence lines/deformation curves) have recently been proposed as important indicators due to their good local damage detection ability and low testing cost. This paper attempts to establish connections between different static curve-based damage indicators through the various forms of Maxwell-Betti's law. Damage indicators related to seven static curves are then rewritten as a unified framework. The framework states that all these static curves are directly related to the flexural stiffness distribution of the main girder for the simply supported bridge. Then, the theoretical formulations for the difference between the static curves of bridges in intact and damaged states are derived, and the response difference surfaces (RDSs) are plotted to visualize the sensitivity of different static curves to damage. Sensor placement suggestions for stiffness degradation evaluation are presented based on the damage sensitivity analysis at the end of this paper. The results of this study provide comprehensive theoretical support for static response-based damage identification of simply supported bridges. [ABSTRACT FROM AUTHOR]

Cracking frequently encountered in railway bridges can lead to significant changes in bridge dynamic characteristics and further dynamic performance of train–bridge interaction (TBI) system. It is desirable to develop an effective method to understand the dynamic behavior of coupled train and cracked bridge systems. This paper proposes a framework for coupled vibration analysis of train and cracked bridge systems using multiscale finite element modeling. A multiscale bridge model adopting frictional surface–surface contact to characterize nonlinear breathing phenomenon of the crack is incorporated into the three-dimensional coupled TBI model. Subsequently, a multiloop numerical solution strategy is formulated to solve the established dynamic equation of coupled train and cracked bridge systems. The proposed framework is used to analyze the cracked beams presented in the literature, and the obtained and literature data are compared. Moreover, an actual heavy-haul railway bridge is taken as an illustrative example, in which vehicle dynamic response, bridge vibration, and train running safety index are calculated considering various crack types, train speeds, and track conditions. The results show that apart from the bridge displacement and acceleration, the presence of crack can have a large influence on the vehicle acceleration response, derailment factor, and offload factor. Differences larger than ±10% in the maximum dynamic indices associated with the intact and cracked bridges are observed, indicating the necessity of considering the crack damage in train–bridge coupled dynamic analysis. [ABSTRACT FROM AUTHOR]

When an earthquake occurs, railway bridges will suffer from different degrees of seismic damage, and it is necessary to assess the seismic risk of bridges. Unfortunately, the majority of studies were done on highway bridges without taking into account railway bridge characteristics; hence they are not applicable to railway bridges. Furthermore, current research methods for risk assessment cannot be performed quickly, and suffer from the problems of subjective personal experience, complicated calculations, and time-consuming. This paper we use machine learning for earthquake damage prediction and empirical vulnerability curves to represent risk assessment results, creating a rapid risk assessment procedure. We gathered and tallied seismic damage data from 335 railway bridges that were damaged in the Tangshan and Menyuan earthquakes, found six variables that had a substantial impact on seismic risk outcomes, and categorized the damage levels into five categories. It is essentially a multi-classification and prediction problem. In order to solve this problem, four algorithms were tested: Random Forest (RF) Back Propagation Artiifcial Neural Network (BP-ANN), PSO-Support Vector Machine (PSO-SVM), and K Nearest Neighbor (KNN). It was found that RF is the most effective method, with an accuracy rate of up to 93.31% for the training set and 89.39% for the test set. Then this study describes the new procedure in detail for rapidly assessing seismic risk to 269 bridges chosen at random from the sample pool. Firstly, the seismic damage data of bridges are collated, then the seismic damage rating is predicted using RF, and finally the empirical vulnerability curve is drawn using a two-parameter normal distribution function for the purpose of seismic damage risk assessment. The study's findings can be used as a guide for choosing a machine learning approach and its inputs to build a rapid assessment model for railway bridges. [ABSTRACT FROM AUTHOR]

This paper presents a new way to determine road profile and detect bridge damage using accelerations from a fleet of passing vehicles. Using off-bridge data, a Bayesian approach updates estimates of the road profile and vehicle properties. The profile elevations and vehicle properties are shown to be insensitive to random noise in acceleration measurements. On-bridge data, with recently updated vehicle properties, are used to estimate bridge damage. Bearing damage and local crack damage in a bridge are simulated. For bearing damage, the results show that this method can quantify the damage level of a bearing and infer other bridge properties. For local crack damage, the levels and the location of the damage are inferred from the simulated measurements. [ABSTRACT FROM AUTHOR]

Bridge influence lines can reflect the performance status of bridge structures and are an important tool to evaluate high-speed railway bridges. Because a high-speed train can cause large dynamic response on a bridge, it is difficult to identify the static influence line of the bridge. To extract the static bridge influence lines for bridge evaluation, this paper proposes a low-pass filter design method for static influence line identification. By maximizing the difference in bridge static and dynamic energy, the relationship between the frequency domain characteristics of the bridge dynamic response and the filter parameters is established. Based on this relationship, reasonable values of the low-pass filter parameters can be determined. After filtering the dynamic response, the quasi-static response can be effectively obtained, and the bridge static influence line can be identified through the least square regularization method. Finally, a numerical example of the vehicle–bridge interaction model is used to verify the effectiveness of the proposed method. The results show that the low-pass filter designed through the proposed principle can accurately remove the dynamic effects, and its stability and robustness are good. The research results provide a reliable method for the static influence line identification of high-speed railway bridges. [ABSTRACT FROM AUTHOR]

Bridge influence lines (BILs) and bridge influence surfaces (BISs) are inherent static parameters of bridges which can be extracted from moving vehicle-induced bridge responses. Compared with dynamic parameters, these parameters are directly related to the stiffness and internal forces in each cross-section of a bridge therefore can be considered as an effective bridge metamodel. To accelerate the engineering practice of BIL- and BIS-based bridge evaluation, this paper first briefly reviews the current BIL and BIS field test and identification methods. Then, the bridge evaluation guidelines of China and the United States are introduced as the basis of the evaluation methods. Engineering application scenarios for bridge evaluation, including permit load verification, performance degradation checking, and load carrying capacity evaluation, are summarized, and a multiple-scenario bridge evaluation method is established. At the end of this paper, an evaluation example of a four-span continuous bridge is presented to illustrate the application procedure and verify the effectiveness of the proposed method. The outcomes of this paper provide a promising application method of field test BILs and BISs, which may help bridge engineers more effectively use these parameters in engineering practice. [ABSTRACT FROM AUTHOR]

The vehicle scanning method (VSM), an indirect approach for bridge measurement, has attracted intensive attention since it was proposed by Yang and co-workers in 2004. This method is featured by the fact that no vibration sensors need to be mounted on the bridge, but only one or few vibration sensors are required on the test vehicle. Such an idea has been verified by the field tests, and then quickly extended to construction of mode shapes, identification of damping ratios, and detection of damages for bridges, among others. Compared with the conventional direct method that relies fully on the vibration responses recorded by sensors equipped on the bridge, the advantage of the indirect method is obvious: mobility, economy, and efficiency. Over the years, a rapidly growing number of research works have been conducted along the lines of the VSM for bridge measurement. Particularly, extensive lab experiments and field tests have been carried out worldwide to implement the VSM, resulting in numerous new findings. Moreover, while the technique is still flourishing, it is nourished by inclusion of modern devices such as smartphones, vehicular networks, and cloud. In 2018, a review paper was compiled by two of the authors. To reflect the recent rapid growth of research in this area since then, there exists a need to make an expansion to include the huge number of newly published papers (274 papers in total). As an extension of the 2018 paper, this paper represents a state-of-the-art review of the related researches conducted worldwide. Comments and recommendations will be made at proper places, while concluding remarks including future research directions will be presented at the end of the paper. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of moving force identification on simply supported bridges. When a vehicle moves on a bridge deck, there is a dynamic interaction force between the vehicle and the bridge deck. This dynamic interaction force is crucial for the design and condition assessment of bridges. However, this interaction force cannot be directly measured. Therefore, the indirect measurement method based on the bridge response, namely, the moving force identification method, has become an important issue in evaluating bridge condition. This paper provides a comprehensive review of current research and development activities in bridge moving force identification. First, the development and application of moving force identification are introduced. Next, the theoretical methods of bridge moving force identification are presented. Then, the robustness and accuracy of moving time-varying force identification methods are numerically simulated and compared. Finally, the characteristics of these methods are summarized, which provides a basis for selecting the suitable identification methods under different conditions and prospects of the further development of moving force identification. [ABSTRACT FROM AUTHOR]

The investigation on vehicle–bridge interaction systems (VBIS) plays an important role in evaluating the performance of bridges and/or vehicles. However, due to the limitations of traditional simulation methods and testing methods, the impact of vehicle jumping (VJ) on the VBIS has never been precisely disclosed. In order to accurately and comprehensively reveal the impact of VJ on the dynamic performance of VBIS, one real-time hybrid simulation method (RHSM) for VBIS considering the impact of VJ (VBIS-VJ) is proposed in this paper. In the proposed method, the vehicle system is chosen as the experimental substructure loaded by servo-hydraulic actuator loading system in real-time, whilst the bridge system is chosen as the numerical substructure numerically simulated in one computer. During the vehicle jumping, the vehicle and bridge vibrate freely respectively, otherwise, the experimental substructure and numerical substructure are synchronized at the coupling points in terms of force equilibrium and deformation compatibility in RHSM for VBIS-VJ. Firstly, the principle and numerical models of RHSM for VBIS-VJ are proposed. Furthermore, numerical simulations of RHSM for VBIS-VJ are conducted for verifying the feasibility of the proposed method. Finally, the influence of jumping position, jumping height, and driving speed on the VBIS is disclosed by hybrid simulations. It is shown that compared with the ever-known investigation methods, the proposed method could economically and accurately disclose the dynamic performance of VBIS-VJ. It is shown from the simulation results that compared with the referenced results, the quantitative errors of the proposed method with quarter vehicle model and half vehicle model are less than 2.5152% and 3.5599% respectively, which prove the effectiveness and accuracy of proposed RHSM for VBIS-VJ. The peak displacement of the bridge at a quarter-span and mid-span exceeds the three-quarters span by about 1.9–2.9 mm. The influence of jumping position of a quarter span and midspan of bridge is much huger than that in the three-quarters span. The dynamic response of bridge increases with the increase the jumping height, but the deformation of the bridge is reduced when the jumping height is more than 0.05 m. Meanwhile, the requirements for experimental loading equipment are acquired through the hybrid simulations, which could be used as a reference for selecting the loading system of the RHSM for VBIS-VJ. [ABSTRACT FROM AUTHOR]

High-speed railways are “lifeline projects” that shoulder the heavy responsibility of transporting relief supplies and medical forces for the first time after earthquakes. To ensure the train’s safety after earthquakes, it is of great urgency to ascertain a post-earthquake speed threshold. To that end, a target model for seismic irregularity emerges as a key parameter. In this paper, a null phase assumption-based technique for the mutual conversion between evolutionary power spectral density and non-stationary signal was proposed. Taking a high-speed railway track-bridge system as the research object, the target model of seismic irregularities was constructed based on the proposed technique. The rationality of the target model of seismic irregularities was verified, and the construction parameter settings were discussed. Moreover, a simplified frequency-domain fitting method for the target model of seismic irregularities was proposed based on the spectral decomposition theory. According to the research findings, the null phase assumption-based technique is capable of performing interconversion between seismic irregularity and its evolutionary power spectral density with satisfactory accuracy. It is recommended to set the minimum number of seismic irregularities, spatial sampling interval, hop size, and length of window function as 50, 0.25m, 1, and 40 to 100, respectively. [ABSTRACT FROM AUTHOR]

The field load test is a direct and effective method for evaluating the performance of bridge structures. However, the existing bridge static load tests on site are costly, inefficient, and obstruct traffic; moreover, improper loading may also cause some damage to the bridge structure. This paper proposes a random model update method based on bridge dynamic load tests and the Bayesian inference as an alternative to the static load test. The Gaussian process model was used instead of the finite element model to reduce the cost of model calculation. Furthermore, choose the Markov Chain Monte Carlo (MCMC) method based on delay rejection adaptive Metropolis algorithm for Bayesian inference to improve the speed of the Bayesian method inferring the posterior probability density of updated parameters. First, the parameters to be updated for the bridge structure analysis model were determined based on the global sensitivity analysis method. Second, a uniform design sampling method was used to establish the Gaussian process optimization model to update the random model of the bridge structure. Finally, a reinforced concrete truss arch bridge was used to verify the correctness of the static load results of the bridge predicted by the random model update method based on dynamic load testing and Bayesian inference. The results show that the predicted results of the bridge static load test based on the dynamic load test and Bayesian reasoning method have an excellent agreement with the measured results, and this method can effectively overcome the adverse effects of the existing bridge static load test. [ABSTRACT FROM AUTHOR]

This paper proposes using spatially distributed TMD-type counterweights for the multiple-mode vibration control of irregular bridges subjected to wind loads. In particular, the governing equations of the bridge with TMD-type counterweights that are spatially distributed at any transverse and/or longitudinal positions of the bridge girder are established. Four objective functions are formulated and compared, and an optimum design method is developed to obtain the optimal design parameters of the TMD-type counterweights for each objective. Subsequently, an irregular cable-stayed bridge equipped with counterweight blocks is chosen as the prototype to demonstrate the control effectiveness of TMD-type counterweights. The results show that the proposed method is feasible for the multiple-mode vibration control of irregular bridges, and TMD-type counterweights show good control efficiency and robustness performance, providing an attractive alternative to traditional counterweights in the vibration control, especially multiple-mode vibration control, of irregular bridges. [ABSTRACT FROM AUTHOR]

This paper proposed an indirect bridge frequency identification and damage detection method based on the virtual contact-point (CP) responses of railway vehicles moving over the bridge. The virtual CP response is acquired to reduce the inverse effects of the track irregularity and vehicle dynamic components on the indirect bridge monitoring of the vehicle–bridge interaction (VBI) system. By combining the virtual CP responses of multiple connected vehicles, the first three bridge frequencies can be identified when vehicles run at high speed. The synchrosqueezing transform (SST) is then adopted to extract bridge-related dynamic components from the virtual CP response for bridge modal identification. The time-frequency (TF) representation of the virtual CP response using SST is further analyzed for bridge damage detection. The spectral entropy (SE) of the TF representation is introduced to quantify TF changes and serves as a damage index for bridge damage localization. Numerical studies are conducted to investigate the effects of measurement noise, damage conditions, and track irregularity on the effectiveness of the proposed method. The results demonstrate that SE is a good indicator for damage detection, and that the proposed method combined with wavelet denoising has good anti-noise performance and can well locate bridge damage using virtual CP response. [ABSTRACT FROM AUTHOR]

This paper presents a novel strategy for indirectly identifying the natural frequency of a bridge via multiple sensors on an ordinary vehicle. It does not require specifically designed cars or multiple cars as the excitation sources, as many earlier studies have used, but only one normal vehicle, providing practicability. A signal processing method incorporating cross-spectrum estimation can extract the bridge frequency among complex and varied frequency components in the vehicle spectrogram. The bridge natural frequency as a common vibration component among different sensor responses is identified as a clear peak that occurs in the cross-spectrum. The methodology is validated by an experimental study using a steel beam and a scale truck model with an engine to construct the vehicle–bridge interaction system. With this approach, the first mode frequency of the bridge is successfully identified under various bridge conditions. In addition, the performance of the proposed strategy in bridges with and without road roughness is discussed, and the influence of vehicle weight on the success rates of frequency identification is also confirmed. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm to solve vehicle–bridge interaction (VBI) problems. It describes the VBI system in a linear second-order differential-algebraic equation (DAE) manner, incorporating the motions of both the vehicle and the bridge subsystems, together with the contact forces. On account of the conjugation between the VBI system's motions and contact forces, the key feature of the proposed algorithm is to solve the DAE with the the algebraic method of elimination by substitution, avoiding the singularity of the differential operator. It enables a versatile and consensus solution scheme for different types of contact mechanisms, including the rigid contact model with constraints, the elastic contact model with the compliance method, and the creep tangential contact model, as well as different wheel–rail profiles. Combined with an appropriate numerical integration scheme, it allows an explicit expression of the contact forces, subsequently leading to the partitioned non-iterative analysis of the motions of both the vehicle and the bridge subsystems. In addition, by incorporating the dynamic condensation of wheels' degrees of freedom into those of upper components in a matrix form, i.e. a reduced system approach, the proposed algorithm can avoid the numerical drift associated with the kinematic constraints on the acceleration level. To demonstrate the accuracy of the proposed approach, this study first examines a benchmark example of a single degree of freedom sprung mass model traversing a simply supported bridge. Further, it considers a more realistic problem of one and eight train vehicles crossing three three-span continuous bridges. The comparisons and the agreements with existing algorithms prove the accuracy and computational efficiency of the proposed analysis scheme. [ABSTRACT FROM AUTHOR]

Using wind tunnel tests, the aerodynamic characteristics of a suspended monorail transport (SMT) are systematically investigated under crosswinds for the first time. Two practical vehicle–bridge combination forms (single-line and double-line) are considered. As a contrast, single vehicle and bridge models are also studied to further reveal their aerodynamic characteristics. The mean and root mean square (RMS) wind pressure coefficients, aerodynamic coefficients and power spectrum density (PSD) distributions are used to numerically investigate the effects. The paper confirms that: (1) the mean wind pressure coefficients of the single vehicle and bridge models are close to unity at the center point of the windward side and gradually decrease from the center point to both sides. The maximum value of the RMS wind pressure coefficients mainly occurs at the top or bottom surface of the testing models due to the effect of vortex shedding; (2) the aerodynamic coefficients and the mean and RMS wind pressure coefficients are significantly increased for both the vehicle and bridge models when considering the aerodynamic coupling effects; (3) the aerodynamic characteristics of the bridge can be significantly affected by the location of the running vehicle, but the fluid flow can maintain relative stability during the meeting of the two vehicles. [ABSTRACT FROM AUTHOR]

This paper removes the subjective judgment by proposing a technique for automatically identifying the bridge frequencies using singularity spectrum analysis and K -means clustering. First, the vehicle-bridge contact-point acceleration newly derived from is used to avoid the adverse effect brought by vehicle's frequency. Next, the contact signal is processed by singular spectrum analysis to remove the noises. Then, the first several principal components are processed by the FFT to extract peak frequencies. Finally, the K -means clustering algorithm is adopted to group the peak frequencies into clusters, with their centroids denoting the bridge frequencies. They are verified against those extrapolated from the 1st bridge frequency. Moreover, the current technique was assessed by studies on factors including pavement roughness, vehicle velocity, ongoing traffic, and vehicle stiffness, along with a field test on a bridge to demonstrate its capability. One feature of the current method is that the errors for higher frequencies predicted are not higher than lower frequencies. [ABSTRACT FROM AUTHOR]

In this paper, a train–track–bridge (TTB) interaction model that can account for coach-coupler effect is presented for stochastic dynamic analysis of a train traveling over a bridge. Based on the vector form intrinsic finite element (VFIFE) method, both the bridge and non-ballasted track are discretized into a set of mass particles connected by massless beam elements, in which the fasteners that fixed the tracks on the bridge deck are modeled as a series of linear spring-dashpot units. The multi-body train car is regarded as seven mass particles (1 for car body, 2 for bogies and 4 for wheelsets) connected by parallel spring-dashpot units. Considering the random nature of rail irregularities, the Karhunen–Loéve expansion (KLE) method is used to simulate the vertical profile of the tracks. To calculate the mean and standard deviation of the stochastic response of the TTB system, the point estimated method (PEM) based on the Gaussian integration and dimension reduction method is adopted. The proposed VFIFE–TTB interaction model is then applied to stochastic resonance analyses of a train moving on a bridge. It is shown that the present VFIFE–TTB model is able to analyze the dynamic interaction of the TTB system simply and efficiently. The influence of rail irregularity-induced stochastic vibration on the train and bridge would become significant once the resonant vibration takes place on the TTB system. [ABSTRACT FROM AUTHOR]

Two factors are critical to the effectiveness of the vehicle scanning method for bridge frequencies. One is the frequency of the test vehicle itself. This can be eliminated by using the vehicle–bridge contact point response calculated from the vehicle response. The other is the surface roughness of the bridge, which can be removed by using the residual response of two connected vehicles. In this paper, it is demonstrated for the first time that both vehicle's frequency and surface roughness can be simultaneously eliminated using the contact residue of two connected vehicles. Theoretically, a formulation is presented for both the contact response and residues. In the numerical study, the contact response is demonstrated to outperform the vehicle response as more bridge frequencies can be identified, while the contact residue is verified to work well for various surface roughnesses, vehicle spacings, and bridge damping ratios. For damped bridges with rough surfaces, the contact residue enables us to extract the first three bridge frequencies. [ABSTRACT FROM AUTHOR]

Suspended monorail transportation (SMT) plays an important role in alleviating the urban traffic pressure, and its vehicle–bridge dynamic features are significantly different from those of the traditional railway. To grasp the coupled vibration features of suspended monorail train–bridge system (SMTBS), this paper presents a comprehensive experimental investigation on the vehicle–bridge vibrational responses under different operating conditions. First, based on the Chengdu SMT test line in China, a full-scale field measurement of the coupled vibration responses of the SMTBS is elaborately conducted under constant speed conditions. Then, the vibrational responses of the SMTBS are analyzed in the time and frequency domains to reveal its coupled vibration features and vibration transmission characteristics. Further, considering an extreme train operating condition, the vibrational responses of the SMTBS are tested and analyzed under train emergency braking; and the vibration features of the vehicle and bridge are examined for emergency braking, along with several key indexes evaluated for the train braking performance. Results show that the vibrational accelerations transmitted from the frame to the center pin and then to the carbody will be significantly decreased in turn, and the vibrational dominant frequencies of the bogie, center pin, and carbody mainly fall with 0–100 Hz, 0–50 Hz, and 0–20 Hz, respectively. Under moving train loads, the box beam produces plentiful high-frequency vibrations and the vibrations transmitted from the driving track to the top plate are drastically reduced. The train braking significantly intensifies the car-body longitudinal vibration; however, it has small influences on the car-body vertical and lateral vibrations. [ABSTRACT FROM AUTHOR]

Bridge damping ratios are extracted via the skillful use of the single-degree-of-freedom (DOF) test vehicle for the first time in this paper. Central to the simultaneous retrieval of the first few frequencies and damping ratios from the contact (point) response of the bridge is the use of the variational mode decomposition (VMD) and random-decrement technique (RDT). Closed-form solutions are newly derived for the vehicle and contact responses of the damped bridge and validated later numerically. Using the proposed method, one calculates first the mono-component from the contact response by the VMD; then extracts the free-decay response for each mode by the RDT; and finally identifies the frequency and damping ratio by the Hilbert transform. The parametric study confirms that: (1) the contact response outperforms vehicle's response in retrieving bridge frequencies and damping ratios; (2) the first few frequencies can be identified with robustness for reasonable levels of road roughness, vehicle speed, bridge damping and noise; (1) good result is obtained for the first damping ratio, in spite of the traditional uncertainty existing with damping; and (2) ongoing traffic can enhance the proposed method for bridge identification. [ABSTRACT FROM AUTHOR]

Among large-span bridges, arch bridges have relatively high stiffness, which may lead to large dynamic amplification factors (DAFs). The DAFs suggested by current codes mostly originate from common simply supported beam bridges. Previous DAF studies on dynamic vehicle–bridge solutions for arch bridges have mainly focused on one or two side-by-side vehicles. However, complex traffic flows with randomness rather than one or two vehicles act on bridges. DAFs that consider the effects of random traffic flows have not previously been reported. In this study, a random traffic–bridge vibration solution was established to explore the DAFs of arch bridge components. The randomness of the traffic parameters and road roughness was explored. The randomness of the external excitation, including traffic flow and road roughness, resulted in random dynamic responses and DAFs of the arch bridge components. Normal distributions could be used to fit the DAF distributions of each arch bridge component under random vehicle spacing, weight, speed, and road roughness. Under random traffic parameters, the coefficients of variation of the DAFs exceeded the 5% accepted level. The shortest suspenders were more sensitive to the randomness of the traffic flow parameters. Both the mean and coefficient of variation of the DAFs increased with worsening of the road conditions. The influence of the randomness of the road roughness on the DAFs of arch bridges must be considered, particularly for the shortest suspender. The 95% upper confidence limits of the DAFs for all components may be greater than the suggested values in the code. [ABSTRACT FROM AUTHOR]

This study investigates variations in the Humber Bridge's natural frequency and presents waveforms of individual vibration modes using acceleration response data collected on February 1, 2012. Three synchrosqueezed transform analysis methods were primarily employed to process the numerical multi-component and closely-spaced signal to identify the most suitable techniques for real-world applications. The improved multisynchrosqueezing transform and Fourier synchrosuqeezed transform were chosen for analyzing the bridge's monitored vertical acceleration at Humber. The time-frequency representations and instantaneous frequencies (IFs) generated by these methods are well-aligned, revealing less than a 5% change in the bridge's vertical natural frequency, with mode 1 exhibiting an 11% variation. The monitored vertical acceleration was then reconstructed, and the results demonstrated the high accuracy of the recovered data, with an L-Infinite norm of 0.0013. Finally, the reconstructed waveforms of each mode, along with the corresponding IFs and spectra, provided a convenient means for further insight into the acceleration data. This study presents new findings, such as the waveform of each vibration mode from the monitored acceleration of the bridge, previously unreported in bridge engineering, and validates the feasibility of cutting-edge synchrosqueezed transform methods for accurately tracking the bridge's frequency variations under operational conditions. It lays a foundation for further analysis of the bridge's monitored signals. [ABSTRACT FROM AUTHOR]

For rail-cum-road bridges with single-level arrangement, there are not only dynamic interference, but also aerodynamic interference between trains and vehicles, which means that the train-induced winds generated by high-speed trains are non-negligible for vehicles. In this work, the test method of moving vehicle model is used to test the aerodynamic characteristics of vehicles under train-induced winds. Then, the model of coupled vibration of train–vehicle–bridge system considering train-induced winds is established. Finally, taking a rail-cum-road bridge with single-level arrangement as an example, the coupled vibration of train–vehicle–bridge system considering train-induced winds is analyzed, and the influence of train-induced winds, lateral spacing between trains and vehicles on train–vehicle–bridge system is discussed. The results show that train-induced winds decrease with the increase of the lateral spacing between trains and vehicles. Without considering train-induced winds, the moving train has a greater impact on the vertical response of the vehicle, and a smaller impact on the lateral response of the vehicle. When considering train-induced winds, the lateral response of the vehicle increases significantly, but decreases with the increase of the lateral spacing. In addition, road conditions and the setting of anti-dazzle plates can change traffic safety under train-induced winds. [ABSTRACT FROM AUTHOR]

Moving force identification (MFI) is one of the challenging tasks in bridge structural health monitoring (SHM). Especially when the vehicles get on and off the bridge, or cross bridge expansion joints and speed humps, the impact of vehicle loads on bridge structures is commonly focused and the accuracy of MFI results still needs further enhancements. To address this issue, a new MFI framework is proposed by using both group sparsity theory and compressed sensing (CS) in this study. Specifically, with the help of the relationship between moving vehicle loads and bridge responses induced by the traffic, a redundant dictionary based on CS is used to establish the motion equation of the vehicle–bridge system in conjunction with classical theory of MFI. A group structure on the sparse coefficient vector of each moving force tends to be divided into different groups (no overlapping), where sparse coefficient vectors and measurement matrices are divided in different groupings and eventually reconstructed for unknown vectors of moving forces. Additionally, the effect of different ratios of number of groups to sparsity (g/k) on the MFI results is considered. Finally, numerical simulations and experimental verifications are carried out to assess the performance and capability of the proposed framework. The illustrated results show that the group lasso could precisely reconstruct the moving forces more accurately after proper grouping. The proposed new MFI framework outperforms the traditional L2 regularization or CS methods with a higher identification accuracy and a good robustness to measurement noises, which can be effectively used for the MFI problem in practice. [ABSTRACT FROM AUTHOR]

Based on the moving-window correlation coefficient of signals, a damage detection method is proposed for bridge structures. The signals collected by two sensors during the vehicle crossing the bridge are intercepted as windowed pair time series, and the damage index is defined based on the correlation of them. The damage vector is composed of damage indices from the beginning to end according to the moving direction of the window, and the damage location can be determined by the peak information of all the damage vectors which are obtained by different sensor pairs. First, the damage detection method and corresponding detection steps are introduced. Then, the proposed method is validated by numerical simulation, and the influence of moving window length, moving step, the number of sensors, deviation of vehicle parameters and road-surface roughness on damage localization are discussed, respectively. Finally, the proposed method is validated by experiments using a two-axis vehicle and a steel–concrete composite beam. The results show that the proposed method can effectively identify the damage location. High-pass filtering of the raw data to remove trend items and wavelet noise reduction can significantly improve the accuracy of damage detection, and can accurately locate the damage in the presence of road-surface roughness. The damage detection result is not sensitive to vehicle parameter deviation, and the damage location can still be accurately identified when the vehicle weight or speed deviates 50% from the vehicle parameters in the reference data. In addition, the proposed method can make full use of the damage information between sensors, and can accurately identify the damage location with limited sensors. The number of sensors can be flexibly determined considering both accuracy and economy. [ABSTRACT FROM AUTHOR]

Since the bridge is often treated as the uniform beam for simplicity in most numerical studies of vehicle-bridge interaction, this study proposes a non-uniform vehicle-bridge interaction system by incorporating a three-mass vehicle model in a non-uniform bridge for wider applications, in which non-uniform beam elements of constant width and varying depth are considered. For clarity, the inclined ratios of the entire bridge and one beam element are separately defined in order to describe the non-conformity in computation while both mass and stiffness matrices are re-formulated to comply with the finite element sign convention. As the natural frequencies of a non-uniform bridge cannot be accessed directly, the vehicle scanning method is first adopted to obtain the bridge frequencies. Then, the parametric study is conducted by considering vehicle damping, bridge damping, and pavement irregularity. In addition to the vehicle frequency, the numerical results show that the proposed vehicle-bridge interaction system is able to scan the first four bridge frequencies with desired accuracy subject to pavement irregularity. Concerning the pitching effect of the vehicle, it is shown that the locations for installing sensors are actually affected by both the geometry and the cross-sectional geometry of the bridge in the concern of achieving high resolution of frequency identification. [ABSTRACT FROM AUTHOR]