Vehicle-bridge Interaction Research Articles

Comparison of Dynamic Amplification Factor of Deflection and Bending Moment of Highway Continuous Box-Girder Bridges by Mode Superposition.

There are differences between the dynamic deflection and bending moment (strain) in the same section of continuous girder bridges. However, the selection of the response for calculating dynamic amplification factors (DAFs), which are essential for bridge health monitoring and safety assessment, remains controversial. Modes may play a role in the relationship between the deflection DAF and the bending moment DAF in both numerical analysis and field tests. To investigate the distinctions between the DAFs of the deflection and bending moment in a continuous girder bridge, functional expressions of the DAFs were derived, taking into account multi-factor coupling under concentrated forces. The interaction effects of the mode and road surface condition (RSC), vehicle speed, bridge span length, and span number on the deflection DAF, the bending moment DAF, and the ratio of the deflection DAF to the bending moment DAF (RDM) of precast continuous box-girder bridges were analyzed using vehicle-bridge interaction. To ensure the accuracy of the DAF in numerical computations and experimental tests, two types of accuracy indexes and the corresponding cut-off modes were provided. Validation was conducted by performing dynamic load tests on two field bridges. The results indicate that different modes have a significant effect on the RDM of the mid-span section of a bridge. When considering multiple factors, the deflection DAF and bending moment DAF of the mid-span section increased rapidly with the considered modes and then stabilized. Statistically, the RDM of all nine bridges ranged from 1.00 to 1.12, indicating that the deflection DAF was greater than the bending moment DAF. The suggested cut-off modes can be utilized for efficient and accurate calculation of the DAF and response signal fidelity.

Vehicle–Bridge Interaction Dynamic Analysis of Continuous Rigid Frame Composite Box Girder Bridge with Corrugated Steel Webs under Seismic Excitation

To study the vehicle–bridge interaction (VBI) of highway bridges under seismic excitation, a vehicle–bridge couple analysis method based on Ansys is proposed. The 1/2 vehicle model and space beam element model were established to analyze the VBI response of Lanzhou Xiaoshagou bridge. The self-excited excitation of the system is represented by road surface irregularity randomness, while the external excitation is represented by an earthquake. The impact of seismic types, seismic direction, seismic intensity, vehicle speed, and road surface irregularity on the bridge vibration under the vehicle–bridge coupling during an earthquake is thoroughly analyzed. The results reveal that the type of earthquake significantly influences the dynamic response of the bridge, showing a minimum difference of 31.4%. The intensity of the earthquake is positively correlated with the dynamic response of the bridge. Longitudinal and vertical earthquakes have a more noticeable effect on the bridge’s vertical vibration compared to lateral earthquakes. The ratio of the bridge response under vertical or longitudinal seismic excitation to the response of lateral earthquakes ranges from 1.50 to 26.61. Vehicle speed, road irregularity grade, and randomness have a negligible impact on the dynamic response of vehicle–bridge interaction under an earthquake, accounting for less than 3%. These findings indicate that the analysis of earthquake-bridge vibration can simplify the VBI analysis for continuous rigid frame composite box girder bridges with corrugated steel webs under seismic conditions.

Vehicle suspension tuning for bridge-friendliness and influence on coupled vehicle-bridge system frequency

A novel study on the mitigation of bridge response via tuning of vehicle suspension parameters is presented. The coupled vehicle-bridge interaction (VBI) system consists of a quarter car model with a semi-active skyhook suspension. Transmissibility functions of the time-varying VBI system are derived and an extended fixed points principle is applied to obtain optimally tuned values of the vehicle suspension parameters for bridge-friendliness. Performances of both the optimally tuned passive and semi-active suspensions are assessed in terms of the dynamic responses, energetics and interacting force of the VBI system, and are shown to achieve excellent bridge-friendliness. Effects of vehicle suspension parameters on the temporal variation of the coupled bridge frequency are also studied. It is found that the bridge frequency depends strongly on two key physical parameters: vehicle-bridge frequency ratio and vehicle damping ratio. Moreover, variations of the bridge frequency during the vehicle passage are small when the tuned suspensions are used. This new finding may have critical implications in future research in bridge frequency identification using a moving vehicle.

Vehicle-Bridge Interaction Modelling Using Precise 3D Road Surface Analysis.

Uneven road surfaces are the primary source of excitation in the dynamic interaction between a bridge and a vehicle and can lead to errors in bridge weigh-in-motion (B-WIM) systems. In order to correctly reproduce this interaction in a numerical model of a bridge, it is essential to know the magnitude and location of the various roadway irregularities. This paper presents a methodology for measuring the 3D road surface using static terrestrial laser scanning and a numerical model for simulating vehicle passage over a bridge with a measured road surface. This model allows the evaluation of strain responses in the time domain at any bridge location considering different parameters such as vehicle type, lateral position and speed, road surface unevenness, bridge type, etc. Since the time domain strains are crucial for B-WIM algorithms, the proposed approach facilitates the analysis of the different factors affecting the B-WIM results. The first validation of the proposed methodology was carried out on a real bridge, where extensive measurements were performed using different sensors, including measurements of the road surface, the response of the bridge when crossed by a test vehicle and the dynamic properties of the bridge and vehicle. The comparison between the simulated and measured bridge response marks a promising step towards investigating the influence of unevenness on the results of B-WIM.

A novel 3D train–bridge interaction model for monorail system considering nonlinear wheel-track slipping behavior

Variable speed operation of the train cause easily the wheel-track slipping phenomenon, inducing strong nonlinear dynamic behavior of the suspended monorail train and bridge system (SMTBS), especially under an insufficient wheel-track friction coefficient. To investigate the coupled vibration features of the SMTBS under variable speed conditions, a novel 3D train–bridge interaction model for the monorail system considering nonlinear wheel-track slipping behavior is developed. Firstly, based on the D’Alembert principle, the vibration equations of the vehicle subsystem are derived by adequately considering the nonlinear interactive behavior among the vehicle components. Then, a high-efficiency modeling method for the large-scale bridge subsystem is proposed based on the component mode synthesis (CMS) method. The vehicle and bridge subsystems are coupled with a spatial wheel-track interaction model considering the nonlinear wheel-track sliding behavior. Furtherly, by a comprehensive comparison with the field test data, the effectiveness of the proposed method is verified, as well as the reasonable modal truncation frequencies of the bridge subsystem are determined. On this basis, the dynamics performances of the SMTBS are evaluated under different initial braking speeds and wheel-track interfacial adhesion conditions; besides, the nonlinear wheel-track slipping characteristics and their influences on the vehicle–bridge interaction are also revealed. The analysis results indicate that the proposed model is reliable for investigating the time-varying dynamic features of SMTBS under variable train speeds. Both the axle load transfer phenomenon and longitudinal slip of the driving tire would be easy to appear under the braking condition, which would significantly increase the longitudinal vehicle–bridge dynamic responses. To ensure a good vehicle–bridge dynamics performance, it is suggested that the wheel-track interfacial friction coefficient is larger than 0.35.

Dynamic Analysis and Optimization of Vehicle-Bridge Interaction System under Road Roughness and Time Variability of Element Interpolation Function

Vehicle-induced load, as one of the main excitation factors to bridge vibration and fatigue damage, plays a vital role in the analyzing of the vehicle-bridge interaction (VBI) system, but is still limited in both spatial location and numerical value. This paper presents an improved VBI system to investigate bridge vibration and vehicle vibration considering the coherent roughness and multiple lanes excitation. The vehicle and bridge subsystems and coherent roughness road are developed and are further integrated to form the VBI system based on the displacement relationship between wheels and the bridge. Further, the solution method of the VBI system is given, and the calculation platform is constructed. Meanwhile, the natural frequencies and mode shapes, as well as the mid-span displacement of the bridge, are verified with the results calculated in commercial software. Subsequently, the responses of the VBI system are investigated under different degrees of freedom of vehicle vibration, bridge damping ratio, road roughness class, multiple lanes, and velocity. It can be concluded that road roughness and velocity have a complex nonlinear relationship with the vibration of the VBI system. Finally, the Pareto strategy is applied to find the optimized value of suspension stiffness and damping, and the depth, unit mass, and elastic modulus of the bridge to minimize the system vibration. The results show an obvious improvement in the vibration of the VBI system.

Bridge Displacement Prediction from Dynamic Responses of a Passing Vehicle Using CNN‐GRU Networks

Dynamic displacement is an important indicator for bridge safety estimation but is difficult to measure due to economic or technological limitations. Dynamic responses of a passing vehicle include the bridge dynamic response information. This study proposes a framework utilizing artificial neural networks to efficiently and accurately predict bridge displacements from the dynamic response of a passing vehicle. The input and the output of the networks are the vehicle acceleration responses and the bridge dynamic displacement responses, respectively. The implemented framework consists of convolutional neural network (CNN) and gated recurrent units (GRU). CNN is adept at feature extraction in the microcosm of short‐term time series, revealing intricate nuances. As a complement, GRU plays a crucial role in extracting features of macroscopic long‐term time series. The CNN‐GRU network can efficiently extract higher‐order features contained in the input data. Numerical experiments are conducted using the developed vehicle‐bridge interaction (VBI) system model to obtain requisite data for training the deep neural network. The impact of the presence or absence of roadway irregularities and the number of vehicles are discussed, indicating the accuracy of the framework. Furthermore, a laboratory experiment is conducted to further assess the performance of the CNN‐GRU network. Results indicate that the CNN‐GRU network offers an effective alternative for bridge displacement measurements.

Multidamage Detection of Breathing Cracks in Plate‐Like Bridges: Experimental and Numerical Study

Bridges may develop breathing cracks under excessive overloading vehicles, while conventional beam models are ineffective in analyzing the effect of spatial distribution of these cracks. This study proposes a data‐driven detection model with the consideration of spatial distribution of breathing cracks that can detect the multiple damage locations and degrees of breathing cracks in plate‐like bridges. Firstly, a 2D vehicle–bridge interaction model containing breathing cracks is established, and the damage indicator, contact point displacement variation (CPDV), is calculated using vehicle acceleration data. Next, a dataset with CPDV as the input feature is generated using the finite element method to train the CatBoost‐based damage prediction model, which considers the random distribution of single and multiple cracks, as well as the influence of different vehicle speeds. Finally, by calculating the CPDV related to the actual bridge and feeding it into the trained model, the location and degree of the damage can be predicted. The numerical simulation results demonstrate that this approach can accurately detect complex crack information under various vehicle speeds and exhibits robustness against road roughness. A laboratory experiment further confirms the effectiveness, applicability, and feasibility of this method to multiple damage locations and degree of breathing cracks.

Dynamic analysis of a highway bridge beam under the passage of compound heavy trucks with multiple trailers

PurposeRealistic composite vehicles with 2, 3, 5 and 9 axles, consisting of a truck with one or two trailers, are addressed in this paper by computational models for vehicle–bridge interaction analysis.Design/methodology/approachThe vehicle–bridge interaction (VBI) models are formed by sets of 2-D rigid blocks interconnected by mass, damping and stiffness elements to simulate their suspension system. The passage of the vehicles is performed at different speeds. Several rolling surface profiles are admitted, considering the maintenance grade of the pavement. The spectral density functions are generated from an experimental database to form the longitudinal surface irregularity profiles. A computational code written in Phyton based on the finite element method was developed considering the Euler–Bernoulli beam model.FindingsSeveral models of composite heavy vehicles are presented as manufactured and currently travel on major roads. Dynamic amplification factors are presented for each type of composite vehicle.Research limitations/implicationsThe VBI models for compound heavy vehicles are 2-D.Social implicationsThis work contributes to improving the safety and lifetime of the bridges, as well as the stability and comfort of the vehicles when passing over a bridge.Originality/valueThe structural response of the bridge is affected by the type and size of the compound vehicles, their speed and the conservative grade of the pavement. Moreover, one axle produces vibrations that can be superposed by the vibrations of the other axles. This effect can generate not usual dynamic responses.

Moving force identification based on the nonnegative flexible conjugate gradient least square method and experimental verification

Moving force identification (MFI) is an approach aimed at acquiring the time history of unknown moving loads acting on a bridge, which is actually an inverse problem. The accuracy and efficiency of MFI are significantly influenced by the ill-posedness problem during the process of solving the vehicle-bridge interaction system, especially when the vehicle axles getting on or getting off the bridge. The objective of the study is thus to reduce the effect of ill-posedness problem on the accuracy and efficiency of MFI through numerical and experimental methods. A nonnegative flexible conjugate gradient least square (NN-FCGLS) method is first developed through enriching the conjugate gradient least square method with the flexible Krylov subspace technique and nonnegative constraint conditions. Then, the solution of the NN-FCGLS method is continuously updated with the Krylov subspace technique and the Karush-Kuhn-Tucker conditions at each iteration to obtain a stable solution for ill-posed dynamic force identification. Finally, laboratory experiments were carried out with a simply supported aluminum alloy beam. Numerical simulation results show that the NN-FCGLS method is insensitive to vehicle speed and vehicle wheelbase, and has better accuracy, stronger robustness and higher computational efficiency than the time domain method and the conjugate gradient least square method. The relative percent errors of all ten cases do not exceed 7.8 % under the interference of 20 % noise level. The experimental results shows that the correlation coefficients between measured responses and reconstructed responses are larger than 0.92 in all cases. Experimental studies demonstrate the accuracy and efficiency of the proposed NN-FCGLS method in the practice of MFI.

Bayesian Vehicle Load Estimation, Vehicle Position Tracking, and Structural Identification for Bridges with Strain Measurement

Vehicle load estimation and health monitoring of bridges are of great importance for the health monitoring of bridge structure under vehicle loads. Traditional methods for the estimation of vehicle load require the positions of the vehicles. The vehicle position tracking is generally conducted in offline manner and requires the installation of additional sensors. To resolve these problems, we developed a Bayesian probabilistic approach for the online estimation of vehicle loads, vehicle positions, and structural parameters for bridges. The crux is to model the vehicle load vector as a modulated filtered Gaussian white noise due to the fact that the vehicle-bridge interaction forces are in essence the responses of the vehicle-bridge coupled system under the excitation of the road roughness described by Gaussian random field and the constant vehicle weights. Furthermore, the vehicle speed vector is introduced to track the unknown positions of vehicles. There are three appealing features in this approach. First, it allows the simultaneous estimation of vehicle loads, vehicle positions, and structural parameters in an online manner. Second, this method allows for time-varying vehicle speed tracking. Third, the proposed method is applicable to the case with multiple vehicles. Examples for the case where single/multiple vehicles pass across bridges with uniform speeds/variable speeds are presented to demonstrate the feasibility of the proposed method for vehicle load estimation, vehicle position tracking, and bridge structural identification using only strain measurements.

A vehicle-bridge interaction model considering contact patch size and vehicle self-generated excitation – A theoretical study

This paper presents an improved theoretical damped single-axle vehicle-bridge dynamic interaction model to consider the effect of the contact patch size and motor-induced vehicle excitation. The contact patch issue is critical as it determines the minimum time step for simulation and maximum identifiable frequency, while the inclusion of the motor-induced vehicle excitation benefits the design of autonomous self-driven rather than towed vehicles. Estimations of the contact patch size for both the pneumatic tire and solid wheel scenarios are discussed. The contact patch responses, which degenerate into contact point responses when the contact patch size is assumed to be infinitely small, were derived for the first time both from the vehicle and bridge responses to confirm their equivalence. The minimum time step, which determines maximum identifiable frequency but is arbitrarily chosen in literature, is proposed to be determined by the vehicle speed and contact patch length. The procedures to extract multiple bridge triad information including natural frequencies, mode shapes, and damping ratios from the vehicle responses are also presented. Based on extensive parametric analyses, the sinusoidal vehicle excitation becomes more prominent as its amplitude and/or frequency increase and may overshadow the analysis of bridge frequencies of interest. The vehicle acceleration leads to a more accurate extraction of bridge mode shapes and damping ratios than the vehicle displacement since the displacement is dominated by the fundamental mode of bridge vibration. The damping ratio extraction shows an average error of 0.28% from the instantaneous amplitude of the vehicle acceleration signal.

Comparative study of damage modeling techniques for beam-like structures and their application in vehicle-bridge-interaction-based structural health monitoring

Damage modeling techniques are essential tools for revealing the impact pattern of damage on structural responses. Currently, different damage level parameters are defined for different damage modeling techniques, but there is no established equivalence among them. This might plague researchers in their selection of damage modeling techniques for theoretical and numerical studies. To address this challenge, comparative studies of different damage modeling techniques for beam-like structures were conducted, and their impact on vehicle-bridge-interaction (VBI)-based structural health monitoring was analyzed. First, the theoretical basis of four damage modeling techniques, that is, element stiffness loss, element mass increase, cracked beam element, and crack spring element, were analyzed, and equivalent damage level relationships were established based on beam frequencies. Then, a finite element simulation of the VBI system consisting of a two-axle vehicle and a beam-like structure with damage was formulated and verified. Finally, the impacts of different damage modeling techniques with unified damage levels on the dynamic responses were studied, and the feasibilities of damage identification using vehicle and bridge responses under different damage modeling techniques were compared. The results indicated that the dynamic responses generated by different damage modeling techniques varied under the same damage level. The damage modeling technique using the crack spring element posed the most significant impact on the VBI responses, the cracked beam element and element stiffness loss techniques had the equivalent impact, but the element mass increase technique showed a negligible impact. Damage location and severity could be well detected by the cracked beam element and element stiffness loss techniques. However, the dynamic responses generated by the element mass increase technique could not be used to identify the damage. Vehicle responses were more useful than bridge responses for damage identification, demonstrating the advancement of indirect monitoring of bridge health conditions using an instrumented passing vehicle.

Bridge Frequency Identification Using Cross-Power Spectra of Vehicle Vibrations from Multiple Moving Speeds

In recent years, a rapid bridge health monitoring technology has been developed using an instrumental moving vehicle. Using recorded vehicle vibration data, bridge frequencies are identified for bridge health monitoring or finite element model updating. Target bridge frequencies with significant amplitudes in the vehicle’s vibration frequency spectra are expected to be found. However, in the coupled vehicle–bridge interaction (VBI) system, bridge vibration-relevant vehicle dynamics might not be noticeable. The bridge frequency would be difficult to identify because of the potential influence of road roughness. To resolve this difficulty, a novel bridge frequency identification method is proposed to mitigate the negative effects of road roughness. First, theoretical derivations are done to ascertain the VBI system dynamic characteristics considering road surface roughness. Our findings showed that the road roughness-relevant vehicle dynamics are closely related with the traveling speed, whereas the bridge frequency remains approximately constant. Theoretical investigations indicated that cross-power spectra between vehicle dynamics at multiple moving speeds are effective to mitigate the negative effects of road roughness. Presumably, it is feasible to identify the target bridge frequency from the cross-power spectra. Both the dynamic characteristics of the VBI system and the effectiveness of the proposed method for bridge frequency identification were examined using finite element simulations and laboratory experiments. Compared to existing methods, the proposed method is widely applicable to real-world situations and difficulties.

The influence of vehicle dynamics on the time-dependent resonances of a bridge

In bridge structural health monitoring, the response of the bridge while the vehicle is on the bridge, is called a vehicle-bridge interaction (VBI) response. If the vehicle and the bridge are dynamically coupled, the VBI response depends on the bridge’s and the vehicle’s dynamic properties. Therefore, the damage detection techniques based on the bridge resonances become questionable due to the dynamic coupling between the bridge and the vehicle. This study investigates the influence of vehicle dynamics on the bridge’s time-dependent resonances. Vehicle-Induced Delta Frequency (VIDF) represents the changes in the bridge’s time-varying resonances resulting from the vehicle-bridge interaction, while Damage-Induced Delta Frequency (DIDF) accounts for the additional alterations caused by bridge damage. The dynamic interaction between vehicles and bridges (VBIs) is characterized by the frequency ratio between the vehicle (super-system) and the bridge (sub-system). The vehicle frequency is influenced by its dynamics, particularly the suspension systems. Two vehicle models, single suspension and dual suspension vehicles representing passenger trains and freight trains, respectively, are analyzed to assess the significance of vehicle dynamics on VIDF and DIDF. The results demonstrate that both vehicle models experience resonance, which magnifies the dynamic response to damage. However, not all types of vehicles possess the desired dynamic characteristics for effective bridge health monitoring. Trains with single suspension systems exhibit more pronounced changes in the bridge’s frequency response. This characteristic makes them more suitable for effective bridge health monitoring and damage detection.

A Real-Time Hybrid Simulation Method for Vehicle–Bridge Interaction Systems Considering Vehicle Jumping

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[Formula: see text]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[Formula: see text]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.

A Novel Split-Type Two-Axle Vehicle Model for Bridge Indirect Measurement

A split-type two-axle vehicle for bridge indirect measurement is proposed to address the deficiencies of single-axle and conventional two-axle vehicles. At first, the conceptual design of the split-type two-axle vehicle is put forth, and the analytical solution for the vehicle-bridge interaction (VBI) system response is derived. After that, the regularity of the vehicle frequency response function is studied, and the numerical analysis method is utilized to validate the analytical solution. Based on the theoretical findings, the vehicle frequency and speed are studied to investigate their impacts on extracting bridge frequencies from vehicle responses. The results indicate that a peak will form in the frequency response function (FRF) curves when the input frequency is near to the vehicle frequency. The single peak phenomenon will occur when the zero-point condition of the FRF from the contact point to the vehicle body is remarkably close to the second vibration frequency of the vehicle. It is preferable for the first frequency of the vehicle to be higher than that of the bridge since this helps to extract the bridge frequency. On the other hand, at low speeds ([Formula: see text] 43.2km/h), higher vehicle speeds will result in a greater distance between the peaks of the left- and right-shifted frequencies of the bridge, wider frequency bands, and lower spectral resolution, which is not conducive to bridge frequency identification.

A deep‐learning framework for classifying the type, location, and severity of bridge damage using drive‐by measurements

AbstractThis paper proposes a new deep‐learning framework for drive‐by bridge condition monitoring. The proposed approach represents a bridge monitoring regime that enables the presence, type, location, and severity of bridge damage to be identified purely from measurements taken on a passing vehicle, without needing any pre‐measured training data. The computational framework adopts a numerical vehicle–bridge interaction (VBI) model, which is automatically calibrated using only the vehicle vibration measurements. The calibrated model is used to generate labeled training data, eliminating the practical difficulties associated with collecting training data for damaged bridge scenarios. The numerically simulated data are used to train a convolutional neural network that can then classify the actual damage characteristics of a bridge. The method is tested using a laboratory‐scale VBI model, and results show that the algorithm can accurately identify the presence of seized bearings and cracking in the bridge beams. The algorithm shows good accuracy when identifying the type and extent of damage. The location of bearing damage can generally be identified; however, the location of cracking is less accurate at lower damage levels. The proposed framework represents a significant improvement on existing techniques for indirect bridge monitoring.

Bridge Damage Identification Using Rotation Influence Line Under Random Vehicle Loads

As a critical characteristic curve of bridge structures, the influence line (IL) is effective in evaluating the bridge damage and bearing capacity. Contrary to conventional methods using static loading to obtain IL, this study proposes a methodology to determine the rotation influence line (RIL) based on the vehicle–bridge interaction (VBI) under random traffic loads. A key feature is the employment of the machine learning algorithm to determine the RIL from the upper and lower bridge response data induced by traffic flow, which is simulated using the cellular automata (CA) approach. Subsequently, by establishing a relationship between the RIL difference and the structural damage coefficient, this study identifies the bridge damage location and degree effectively. The numerical example results validate the accuracy in identifying both single- and multi-damage cases. The parameter analysis indicates that larger vehicle speed induces higher errors in identifying the RIL and the bridge damage.

Does vehicle‐bridge interaction resemble the effect of tuned‐mass dampers on bridges during earthquakes?

AbstractIn many seismic studies, the coupled vehicle‐bridge system is treated as a tuned‐mass damper (TMD)‐bridge system, and unsurprisingly, the vehicle usually reduces the seismic bridge response. The current study challenges this common belief. To this end, it investigates the influence of running vehicles on the seismic performance of bridges. Specifically, it compares the effect of vehicle‐bridge interaction (VBI) to that of a TMD on bridges subjected to earthquakes. The discussion explores the underlying physical mechanisms of VBI and TMD, and highlights the superficial similarities and the substantial differences between them. In this context, the study presents pertinent numerical simulations for a set of natural earthquake records.