Displacement Response Of Bridge Research Articles

Data-based feature representation of traffic flow for predicting bridge displacement responses with ensemble learning model

Data-based feature representation of traffic flow for predicting bridge displacement responses with ensemble learning model

Evaluation of fatigue damage in steel girder bridges using displacement influence lines

This paper presents a systematic method for the evaluation of fatigue damage in an existing girder bridge, utilizing the response induced by a moving vehicle. At first, a detailed and accurate finite element model was developed through a two-steps validation process using extracted modal parameters from measured accelerations and bridge displacement responses due to a moving vehicle with known axle weights. Considering real damage scenarios, three commonly found fatigue damages at the welded connections of the gusset plates, soleplates and the crossbeams were numerically simulated including their initiations and subsequent propagations. The effects of these fatigue damage were evaluated through a dynamic analysis and measuring the bridge displacement responses to a moving vehicle thus, portraying the actual in-situ measurement conditions. The static displacement influence lines (DILs) obtained from the measured signals were used as a potential damage indicator against these fatigue damages. A minimal change was observed in DILs due to incipient fatigue cracks; however, significant changes were observed when they propagated substantially, leading to fracture of the main members. For the localization of fatigue cracks, a damage index was defined by estimating the changes in DILs. Finally, the accuracy of the proposed damage detection scheme was checked with respect to the number of sensors and their locations.

Structural damage detection of the bridge under moving loads with the quasi-static displacement influence line from one sensor

As the bridges continue to age, it is imperative to identify structurally deficient bridges at an early stage to prevent unexpected maintenance needs. A quasi-static method is developed to identify structural damage based on the influence line (IL) from one sensor and an empirical Bayesian threshold estimator. Initially, the empirical Bayesian threshold estimator is utilized to preprocess the measured bridge displacement response to eliminate vehicle-induced dynamic effects. Subsequently, a regularization technique using Elastic Net and displacement response is used for IL estimation, and the Elastic Net that integrates with both L1 and L2 norms is adopted to establish the objective function of IL estimation to deal with the ill-posedness of the inverse problem for specific vehicle configurations. Finally, according to the mechanical relationship between the moving load and the displacement IL, an IL area-based difference index is established to identify the presence of the damage rapidly, raising a flag that leads to a more in-depth investigation to gather information related to structural integrity. The feasibility of the proposed method is verified with the numerical model and laboratory tests. This study introduces innovative contributions in the following three aspects: (1) Elastic Net that integrates with both L1 and L2 norms is adopted to establish the objective function for IL estimation, (2) A single sensor provides a cost-effective and less computationally expensive solution than multi-sensor methods, (3) Compared to other wavelet-based methods, the comparative studies demonstrate that empirical Bayesian threshold estimator can identify damage more clearly.

Effects of stud connection failure on displacement responses of partially damaged bridges

Stud connections in composite bridges are traditionally modeled with rigid links or surface constraints, without considering potential failures. In this study, the effects of stud connection failure were investigated to determine if the failure behaviors were required for a partially damaged bridge or not. Detailed finite-element (FE) bridge models, including stud connection failure behaviors and material inelasticity, were developed to analyze the displacement responses of the bridge. The accuracy of the FE bridge models was verified using full-scale bridge fracture test results obtained from other studies. The analysis results show that the stud connection failure can significantly affect the bridge displacement responses even if a bridge girder suffers partial damage.

Displacement response estimation of a cable-stayed bridge subjected to various loading conditions with one-dimensional residual convolutional autoencoder method

Displacement is an essential indicator of the functioning and safety of long-span cable-supported bridges under operational conditions. However, displacement estimation is challenging as these bridges are simultaneously subjected to various loading conditions such as temperature, wind, and vehicles. This article investigates an approach for estimating bridge displacement responses under multiple loads using a residual autoencoder model. Monitoring data of a cable-stayed bridge are collected to validate the proposed approach, including comprehensive measurements of the various loads and the displacement responses. Characteristics of temperature, wind, and vehicle loads are taken as the input, and the displacement responses at the mid-span of the main girder and top of the two pylons are taken as the output. The results showed the effectiveness of the proposed approach with an accuracy higher than 95%, which clearly outperformed other models such as long short-term memory networks in accuracy and efficiency. The effects of different types of loads are also investigated, and the wind load is found to be the most influential. Furthermore, multistep ahead prediction is carried out using the proposed approach, and good accuracy is achieved even 5 min ahead. The proposed approach can shed light on early warning of the malfunction of the bridge.

Dynamic displacement response analysis of bridge in the impact vibration of slight and frequent vehicle bump

To study the impact of vehicle moving on bridge, the bump occurring under the determined limit conditions of vehicle from no bump to the beginning of bump was named as slight and frequent vehicle bump. With a 1/4 simplified vehicle model and the Euler-Bernoulli bridge model, the required minimum impact height for the bump and the effect of bump impact on the vertical dynamic mid-span displacement response of bridge were figured out in different vehicle speed, axle load and wheel diameter. The simulation analysis by ANSYS software, with HSFLD242 unit simulating air inside the tire and MASS21 simulating the car weight held by tires, verified the results of the minimum impact height. The effect of bump impact on the vertical dynamic mid-span displacement response of bridge was also verified by observation using the TCQN-6A bridge deflection detector on the mid-span dynamic deflection of a 25-metre simply supported beam bridge.

Mechanism Study of Vehicle–Bridge Dynamic Interaction under Near-Fault Ground Motions

The effect of vehicle on the seismic responses of highway bridge has usually been neglected under the assumption that the possibility of vehicles crossing highway bridge or viaduct with a concurrent earthquake is extremely low. Furthermore, there is no in phase movement of vehicle and bridge was observed in past earthquake events. However, in the heavily trafficked urban area this is probably not the case. The mechanism of vehicle–bridge dynamic interaction still remains unclear. Therefore, an analytical vehicle–bridge model with contact elements is built to study the dynamic interaction and the seismic responses of vehicle–bridge system when subjected to near-fault ground motions. In this vehicle–bridge model, vehicle and highway bridge are coupled through Coulomb friction forces in the horizontal directions and through Hertz impact force in the vertical direction, respectively. The horizontal Coulomb friction forces varying with the variation of vertical Hertz impact force under earthquake excitations. Analysis results reveal that vehicle sometimes has a significant influence on the vertical seismic displacement response of bridge and mass ratio of vehicle to bridge is one of the primary factors governing the effect of vehicle on the seismic responses of bridge. Influence of vehicle frequency and damping ratio on the seismic responses of bridge are both related to mass ratio and become significant with the increasing of mass ratio. However, for the responses of the vehicle itself, influence of vehicle frequency and damping ratio is significant only when the mass ratio is small. Due to the weak coupling of vehicle and bridge in the horizontal directions, horizontal displacements of the vehicle can reach several meters, hence possibly endanger the traveling safety of vehicle on highway bridge.

Iterative linear optimization method for bridge weigh-in-motion systems using accelerometers

The static bridge weigh-in-motion (BWIM) systems are mostly based on strain measurements and are particularly suited for stiff short-span bridges. Recently, the BWIM systems based on acceleration measurements are developed for long-span bridges because of the portability and low-cost of accelerometers as compared to strain gauges. Although, these BWIM systems can estimate the gross vehicle weights (GVWs) with high accuracy, but they fail to identify the weights of individual axles accurately especially for vehicles with closely spaced axles. In this paper, an iterative linear optimization problem (ILOP) was proposed to accurately identify the individual axle weights and GVWs of vehicles traversing a bridge. A BWIM system consisting of only microelectromechanical system (MEMS) accelerometers was employed in an in-service steel girder bridge having multiple lanes. The proposed method used the bridge displacement responses as the measured responses which were determined from the recorded acceleration data. The information about the vehicle speed, number of axles and axle spacings were obtained by identifying the peaks in the recorded acceleration data. The effectiveness and accuracy of the proposed method were demonstrated through field tests using the four-axle test vehicles with closely spaced axles. The results showed that the axle weights of vehicles with closely spaced axles could be identified with much better accuracy by the proposed method as compared to classic BWIM systems which are based on Moses’ original algorithm.

Development and Field Testing of a Time-Synchronized System for Multi-Point Displacement Calculation Using Low-Cost Wireless Vision-Based Sensors

This paper presents a contactless multi-point displacement measurement system using multiple synchronized wireless cameras. Our system makes use of computer vision techniques to perform displacement calculations, which can be used to provide a valuable insight into the structural condition and service behavior of bridges under live loading. The system outlined in this paper provides a low-cost durable solution, which is rapidly deployable in the field. The architecture of this system can be expanded to include up to ten wireless vision sensors, addressing the limitation of current existing solutions limited in scope by their inability to reliably track multiple points on medium- and long-span bridge structures. Our multi-sensor approach facilitates multi-point displacement and additional vision sensors for vehicle identification and tracking that could be used to accurately relate the bridge displacement response to the load type in the time domain. The performance of the system was validated in a series of controlled laboratory tests. This paper will significantly advance current vision-based structural health monitoring systems, which can be cost prohibitive and provides a rapid method of obtaining data which accurately relates to measured bridge deflections.

Determination Method of Bridge Rotation Angle Response Using MEMS IMU.

To implement steel bridge maintenance, especially that related to fatigue damage, it is important to monitor bridge deformations under traffic conditions. Bridges deform and rotate differently under traffic load conditions because their structures differ in terms of length and flexibility. Such monitoring enables the identification of the cause of stress concentrations that cause fatigue damage and the proposal of appropriate countermeasures. However, although bridge deformation monitoring requires observations of bridge angle response as well as the bridge displacement response, measuring the rotation angle response of a bridge subject to traffic loads is difficult. Theoretically, the rotation angle response can be calculated by integrating the angular velocity, but for field measurements of actual in-service bridges, estimating the necessary boundary conditions would be difficult due to traffic-induced vibration. To solve the problem, this paper proposes a method for determining the rotation angle response of an in-service bridge from its angular velocity, as measured by a inertial measurement unit (IMU). To verify our proposed method, field measurements were conducted using nine micro-electrical mechanical systems (MEMS) IMUs and two contact displacement gauges. The results showed that our proposed method provided high accuracy when compared to the reference responses calculated by the contact displacement gauges.

Output-only damage detection using vehicle-induced displacement response and mode shape curvature index

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

Model Updating of Railway Bridge Using In Situ Dynamic Displacement Measurement under Trainloads

To address the need for monitoring aging railway bridges, this paper presents a finite-element (FE) model updating approach using time-domain optimization based on in situ measurement of the bridge’s dynamic displacement histories under trainloads. Field tests are performed on a short-span plate girder railway bridge to measure its displacement response to freight trainloads with different speeds using a low-cost remote vision sensor recently developed by the authors’ group. A FE model of the bridge is developed considering train–track–bridge dynamic interactions. Sensitivity analysis is carried out to investigate the intrinsic effects of parameters of the train, track, and bridge subsystems on the dynamic response of the bridge. It is found that the bridge displacement response is primarily sensitive to the stiffness of the bridge, whereas the acceleration response is affected by many other parameters. This justifies that the bridge dynamic displacement is more suited than acceleration for updating the railway bridge stiffness. Consequently, a model updating approach is proposed using the measurement of bridge displacement response to trainloads. This approach is applied to the short-span bridge to first identify the train speed, followed by the bridge stiffness by minimizing the error between the measured and computed displacement time histories. Furthermore, this paper investigates the frequency characteristics from both the measured and computed displacements. The analysis shows that the trainload does not excite the bridge’s natural modes of vibration because the frequency is much lower than the bridge’s natural frequencies. Therefore, it is difficult to use the modal identification-based FE model updating methods based on the dynamic response measurement. This is commonly the case for short-span railway bridges, which tend to have high natural frequencies. The proposed periodic displacement measurement and time-domain FE model updating can be developed into an effective tool for long-term structural health monitoring of short-span railway bridges.

Validation of Fault Rupture-Response Spectrum Analysis Method for Curved Bridges Crossing Strike-Slip Fault Rupture Zones

AbstractIn response to the bridge damages observed in past earthquakes and the absence of practical analysis methods, a simplified analysis procedure named the fault rupture-response spectrum analysis (FR-RSA) method was developed in this research as an alternative to the onerous nonlinear response history analysis (RHA) method for bridges crossing strike-slip earthquake fault rupture zones. For validation of the FR-RSA method, this research investigated seismic responses of two representative curved bridges in California, including one three-span bridge and one four-span bridge. The selected bridges were analyzed using both the FR-RSA method and the nonlinear RHA method. Ground motion, earthquake fault rupture location, and fault orientation were varied in the analyses to expand the validation work under a broad range of the parameters. Result comparisons show that the FR-RSA method provides estimates of peak bridge displacement response that are close enough to the exact nonlinear RHA results in all con...

Multi-Axle Moving Train Loads Identification on Continuous Bridge from Bridge Displacement Responses

A new method for multi-axle moving train loads identification on continuous bridge is presented in this paper. In order to improve the accuracy and efficiency for moving loads identification, both cubic spline interpolation technique and dynamic displacement influence line technique were employed. The time history displacements of the measurement stations under unit force were calculated with cubic spline interpolation technique based on the finite element model of the bridge. The dynamic displacement influence line was obtained to be used for identifying the moving train loads with simulated annealing genetic algorithm by minimizing the errors between the measured displacements and the reconstructed displacements from the moving train loads. A series of comparative studies were carried out to investigate effects of different span numbers with the same length, different length with the same span number and measurement noise on the proposed method. The result shows that the proposed method is an accurate and efficient method for multi-axle moving train loads identification on continuous bridge.

Estimation of bridge displacement responses using FBG sensors and theoretical mode shapes

Bridge vibration displacements have been directly measured by LVDTs (Linear Variable Differential Transformers) or laser equipment and have also been indirectly estimated by an algorithm of integrating measured acceleration. However, LVDT measurement cannot be applied for a bridge crossing over a river or channel and the laser technique cannot be applied when the weather condition is poor. Also, double integration of accelerations may cause serious numerical deviation if the initial condition or a regression process is not carefully controlled. This paper presents an algorithm of estimating bridge vibration displacements using vibration strains measured by FBG (Fiber Bragg Grating) sensors and theoretical mode shapes of a simply supported beam. Since theoretically defined mode shapes are applied, even high modes can be used regardless of the quality of the measured data. In the proposed algorithm, the number of theoretical modes is limited by the number of sensors used for a field test to prevent a mathematical rank deficiency from occurring in computing vibration displacements.89The proposed algorithm has been applied to various types of bridges and its efficacy has been verified. The closeness of the estimated vibration displacements to measured ones has been evaluated by computing the correlation coefficient and by comparing FRFs (Frequency Response Functions) and the maximum displacements.

Analysis of Dynamic Displacement Response of Bridge with Impact of Standard Energy

Conventional method is getting static displacement calculated by dynamic displacement and impact coefficient aim at making the detection of the bridge faster and more precise. But in fact, it is too difficult to ensure the dynamic displacement under the influence of roughness of the bridge deck. For the reasons given above, this paper simulates the bumping testing on the cross section of midspan by explicit dynamic FEM. A vehicle passes through the cross tie which is higher than the highest unevenness, and then gives an impact load to the cross section of the midspan for getting the maximum dynamic displacement of the cross section. This method can eliminate the influence of roughness of the bridge deck and it is a new pathway of bridge dynamic testing.

Monitoring temperature effect on a long suspension bridge

The Tsing Ma Bridge in Hong Kong is a long suspension bridge. A wind and structural health monitoring system (WASHMS) has been installed in the bridge and operated by the Hong Kong Highways Department since 1997. The WASHMS is devised to carry out the monitoring of environmental status, traffic loads, bridge features and bridge responses. The environmental status includes temperature environment monitored by temperature sensors, whereas the bridge responses contain displacement responses recorded by displacement transducers, level sensing stations, and global positioning systems (GPS). Bridge displacement responses are, however, induced by a combination of four major types of loadings due to wind, temperature, highway, and railway. This investigation focuses on the temperature environment and the predominating temperature effect on the Tsing Ma Bridge. The main features and the pertinent monitoring system of the Tsing Ma Bridge are first introduced. The data collected from the four types of sensors are pre-processed. The statistics of ambient air temperature, effective temperature and displacement response of the bridge are then figured out based on the measurement data. The statistical relationship between the effective temperature and the displacement of the bridge is finally established. These results are useful for monitoring temperature effects on the Tsing Ma Bridge. Copyright © 2009 John Wiley & Sons, Ltd.

Dynamic Response of Suspension Bridge to Typhoon and Trains. I: Field Measurement Results

A wind and structural health monitoring system (WASHMS) was installed in the Tsing Ma suspension bridge in Hong Kong in 1997. On September 16, 1999, Typhoon York, which was the strongest typhoon since 1983 and the typhoon of the longest duration on record, crossed over Hong Kong. All vehicles, except trains, were prohibited from running on the bridge for 2.5h. This event provided a distinctive opportunity to examine the existing analytical models for predicting dynamic response of long suspension bridges to high winds and running trains. The field measurement data recorded by the WASHMS during this event were therefore analyzed and the four particular cases were identified and discussed in this paper. The four particular cases identified included: (1) the bridge without any vehicles; (2) the bridge with one train; (3) the bridge with two trains running in opposite directions; and (4) the bridge with three running trains. The mean wind speed was almost perpendicular to the bridge alignment in the four cases to facilitate the comparison with analytical results. For each case, wind characteristics, bridge acceleration responses, and bridge displacement responses were analyzed using the measurement data from anemometers, accelerometers, and level sensing systems, respectively. The number of trains running on the bridge, train speed, and train location were also identified using the measurement data from strain gauges. The measurement results clearly demonstrated the dynamic behavior of the bridge with running trains during high winds. The measurement results obtained will also be used to verify the analytical model developed by the authors in the companion paper.