Bridge Response Data Research Articles

On population-based structural health monitoring for bridges: Comparing similarity metrics and dynamic responses between sets of bridges

Bridges are valuable infrastructure assets that are challenging and expensive to maintain. State-of-the-art data-based bridge SHM solutions look to use bridge response data for condition assessment and damage detection. Data-based SHM methods can be limited in their application as they require large datasets to train models effectively, and most bridges lack the available data for the approaches to work. Further, it would be expensive and unrealistic to collect the required datasets to employ data-based methods to entire bridge networks. Recently, a population-based structural health monitoring (PBSHM) approach was proposed that seeks to leverage the data available for SHM problems by pooling together similar structures with their datasets. The PBSHM approach could be valuable in bridge SHM, enhancing the datasets available for ‘populations of bridges. The PBSHM approach for assessing the similarity of bridges has been considered before and was shown to be useful for identifying similar and different bridge types. However, no data were considered in the previous work, and similarity metrics were only qualified using engineering judgement. For the PBSHM approach to be useful in bridge SHM, there is still a need to check that ‘similar’ bridges have similar responses for transfer learning to be feasible. This paper expands upon previous work and provides originality by investigating if bridges identified as similar also exhibit similar responses. The PBSHM derived similarity metrics convey the topological similarity between structures, and mode shapes are identified as being a topologically sensitive bridge response. Therefore, a modal test campaign is carried out for a set of six real bridges, and Operational Modal Analysis is used to identify modal responses from each of the bridge decks. The Modal Assurance Criterion is used to evaluate the similarity between the mode shapes from pairs of bridges and is subsequently compared to the similarity metrics evaluated between those bridges. The similarity metrics were found to be reflective of the similarities identified between the respective bridges’ mode shapes for bridges of the same and different types. The significance of this finding is that it is an important step towards validating the PBSHM comparison approach for identify similar structures where transfer learning might be attempted.

Intelligent identification of moving forces based on visual perception

The task of identifying moving forces presents a challenging inverse problem for bridge health monitoring and condition assessment. Existing methods for identifying moving loads rely on complex structural response measurement equipment, resulting in inefficiency and elevated costs. To address these challenges, this study introduces an innovative intelligent identification method for moving forces on bridges using visual perception techniques. The proposed method utilizes cameras to capture displacement changes at specific points on the bridge surface to reconstruct moving forces in the time domain. Initially, numerical simulations were conducted to ascertain the feasibility and robustness of identifying moving forces based solely on the structural displacement response. This phase also investigates the influence of different measurement point combinations on identification accuracy. Subsequently, the accuracy of the proposed method in measuring the dynamic displacement response of the structure was carefully evaluated by an outdoor shaking table test. Furthermore, a meticulously designed simply supported beam model was fabricated, followed by the execution of precise vehicle-bridge coupling dynamic experiments. This phase rigorously validates the effectiveness and accuracy of the proposed moving force identification method at varying vehicle speeds. The obtained results substantiate the proposed approach's capability not only to comprehensively capture bridge response data for full-area monitoring but also to consistently and reliably identify information on moving vehicle forces. This study underscores a pioneering application of intelligent visual perception technology in the field of identifying moving forces. The introduced noncontact intelligent identification method is an effective solution for monitoring moving forces on bridges, which has a wide range of applications in the future.

Dynamic Amplification of Railway Bridges under Varying Wagon Pass Frequencies

Train configurations give rise to a primary wagon pass forcing frequency and their multiples. When any one of these frequencies coincides with the natural frequency of vibration of the bridge, a resonant response can occur. This condition can amplify the dynamic response of the bridge, leading to increased levels of displacement, stresses and acceleration. Increased stress levels on critical bridge structural elements increases the rate at which fatigue damage accumulates. Increased bridge acceleration levels can affect passenger comfort, noise levels, and can also compromise train safety. For older bridges the effects of fatigue, and being able to predict the remaining life, has become a primary concern for bridge engineers. Better understanding of the sensitivity of fatigue damage to the characteristics of the passing train will lead to more accurate remaining life predictions and can also help to identify optimal train speeds for a given train–bridge configuration. In this paper, a mathematical model which enables the dynamic response of railway bridges to be assessed for different train configurations is presented. The model is based on the well established closed from solution of the Euler–Bernoulli Beam (EBB) model, for a series of moving loads, using the inverse Laplace–Carson transform. In this work the methodology is adapted to allow different train configurations to be easily implemented into the formulation in a generalised form. A generalised equation, which captures the primary wagon pass frequency for any train configuration, is developed and verified by presenting the results of the bridge response in the frequency domain. The model, and the accuracy of the equation for predicting the primary wagon pass frequency, is verified using independently obtained measured field train–bridge response data. The main emphasis of this work is to enable the practicing engineer, railway operators and bridge asset owners, to easily and efficiently make an initial assessment of dynamic amplification, and the optimal train speeds, for a given bridge and train configuration. This is visually presented in this work using a Campbell diagram, which shows dynamic amplification and compares this with those calculated based on the design code, across a range of train speeds. The diagram is able to identify train speeds at which a resonance response can occur, and the wagon pass frequency, or its multiples, which are causing the increased dynamic amplification. The model is implemented in Matlab and demonstrated by analysing a range of short- to medium-single span simply supported plate girder railway bridges, typically found on the UK railway network, using the standard BS-5400 train configurations. The model does not consider the effects of the train mass and suspension system as this would require a non-closed form numerical solution of the problem which is not practical for the purposes of an initial assessment of the train–bridge interaction problem.

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.

One-Class Convolutional Neural Network (OC-CNN) Model for Rapid Bridge Damage Detection Using Bridge Response Data

This study proposes a numerical investigation for rapid bridge damage detection based on a semi-supervised deep learning (DL) model and a damage index (DI)-based Gaussian process. The proposed damage detection method uses bridge response data (acceleration and displacement data) from various damage scenarios within a simply supported girder bridge subjected to a two-axle moving vehicle load. As for semi-supervised learning, we used a one-class convolutional neural network (OC-CNN) model. This model combines a one-class (OC) classification algorithm with a simple one-dimensional convolutional neural network (1D CNN) configuration. The performance of the proposed OC-CNN model was evaluated through a numerical example of a vehicle-bridge coupling system. The proposed OC-CNN model trained using acceleration data showed promising results for different vehicle weights and speeds. These results offer confidence in using the prediction error loss of the proposed OC-CNN model as an ideal damage-sensitive feature for rapid bridge damage detection. In addition, the Gaussian process used in the DI can classify the prediction error losses resulting from the change induced by different damage severities (10%, 20%, and 30%) and different types of damage scenarios (single damage, double damages, and multiple damages). These results emphasize the potential of the proposed damage detection method to monitor the state of bridges in practical engineering.

Data-driven analytical load rating method of bridges using integrated bridge structural response and weigh-in-motion truck data

Load rating is a widely used approach for evaluating the load-carrying capacity of bridges in an effort to ensure safe bridge operation under expected traffic loads. Load rating often relies on simplified analytical models including empirically derived model parameters that do not reflect bridge-specific information resulting in conservative ratings. To reduce this conservatism, this study proposes a novel data-driven framework that utilizes long-term bridge response data to extract bridge-specific model parameters that can be used within in the Load and Resistance Factor Rating (LRFR) process. The data-driven LRFR (DD-LRFR) framework is empowered by a cyber-physical system (CPS) architecture that uses Internet connectivity to integrate measured bridge responses with truck weights measured by a weigh-in-motion (WIM) station. The CPS architecture uses computer vision of camera images to confirm trucks observed at a WIM station are identical to those observed at a bridge. Bridge response and axle weight data are then used to extract probabilistic models of dynamic load allowances and unit influence lines. The DD-LRFR method is validated using a 20-mile (32.2-km) segment of the I-275 northbound highway in Michigan that is monitored continuously by the CPS architecture. Six girders associated with two bridges along I-275 are rated using the proposed DD-LRFR methodology with rating factors compared to those obtained using conventional and refined load rating methods. The DD-LRFR method yields inventory- and operational-level rating factors that are less conservative than those from the approximate LRFR method and comparable to those using finite element modeling of the bridge.

Cyber-physical system architecture for automating the mapping of truck loads to bridge behavior using computer vision in connected highway corridors

Bridges are critical components of highways ensuring traffic can efficiently travel over obstructions such as bodies of water, valleys, and other roads. Ensuring bridges are in sound structural condition is essential for safe and efficient highway operations. Structural health monitoring (SHM) systems designed to measure bridge responses have been developed to quantitatively track the health of bridges. More recently, SHM systems have also begun to integrate measurement of vehicular loads that create the responses measured. However, precise correlation of traffic loads to bridge responses remains a costly and technically difficult strategy. To address existing technical limitations, a cyber-physical system (CPS) framework is proposed to track truck loads in a highway corridor, to trigger SHM systems to record bridge responses, and to automate the linking of bridge response data with truck weights collected by weigh-in-motion (WIM) stations installed along the corridor but not collocated with the bridges. To link truck weights to bridge responses, computer vision methods based on convolutional neural networks (CNN) are used to automate the detection and reidentification of trucks using traffic cameras. The single-stage CNN object detector YOLO is trained using a customized dataset to identify trucks from camera images at each instrumentation site; high precision is obtained with the YOLO detector exceeding 95% average precision (AP) for an intersection over union (IOU) threshold of 0.75. To reidentify the same truck at different locations in the corridor, this study adopts a CNN-based encoder trained via a triplet network and a mutual nearest neighbor strategy using feature vectors extracted from images at each measurement location. The proposed reidentification method is implemented in the CPS cloud environment and obtains a F1-score of 0.97. The study also explores the triggering of bridge monitoring systems based on visual detection of trucks by a traffic camera installed upstream to the bridges. The triggering strategy proves to be highly efficient with 99% of the triggered data collection cycles capturing truck events at each bridge. To validate, the CPS architecture is implemented on a 20-mile highway corridor that has a WIM station already installed; four traffic cameras and two bridge SHM systems are installed along the corridor and integrated with a CPS architecture hosted on the cloud. In total, over 10,000 trucks are observed at all measurement locations over one year allowing peak bridge responses to be correlated to both measured truck weights and to one another.

Combined Road Roughness and Vehicle Parameter Estimation Based on a Minimum Variance Unbiased Estimator

This paper deals with the simultaneous identification of road roughness and vehicle parameters, considering the effect of vehicle–structure interaction. The proposed technique avoids the use of bridge response data (which has practical implementation difficulties along with the high chances of corruption with environmental noises) and utilizes the vehicle response data (which is relatively easier to record). Further, vehicle calibration is not needed as the roughness is estimated simultaneously. The identification is carried out by the coupling of an unbiased minimum variance estimator with an optimization scheme. This study considers a quarter-car vehicle model and a half-car vehicle model, instrumented to measure the vehicle vibration data. The unbiased minimum variance estimator (MVE) allows a linear temporal evolution of the state variables, incorporating the roughness as an unknown input term such that the need for linearization is avoided, unlike the traditional nonlinear filters. The optimization scheme helps in choosing a set of optimal solutions for the vehicle parameters as designed in the coupled scheme. The best split of the available measurement data to be used in the two schemes (MVE and optimization scheme) is discussed. The effect of different objective functions is also studied. The proposed technique is successful in terms of simultaneously estimating the vehicle parameters, roughness profile and vehicle responses (states) accurately.

Inherent uncertainty in the extraction of frequencies from time-domain signals

One structural health monitoring method used to detect the occurrence of structural damage is the tracking of a structure’s natural frequencies. However, for bridges, this is complicated by the changing environmental and operational conditions, which also have an effect on the natural frequencies. Consequently, much of the research effort has been on trying to develop data-modelling approaches that correct for, or remove, environmental effects so that changes in structural behaviour can be revealed. However, the fact that the process of extracting frequencies from bridge response data sets has in itself some inherent uncertainties that have been largely ignored forms the major interest of this study. In this paper, various methods for extracting frequency data from time-domain signals are reviewed, and their suitability for use in automated approaches is discussed. A selection of these methods was then used to obtain frequencies from continuous acceleration data from a bridge over a 20 d period. Comparisons were then made between the obtained frequencies, and any observed differences are highlighted between the methods.

Nonlinear FE model updating and reconstruction of the response of an instrumented seismic isolated bridge to the 2010 Maule Chile earthquake

SummaryNonlinear finite element (FE) modeling has been widely used to investigate the effects of seismic isolation on the response of bridges to earthquakes. However, most FE models of seismic isolated bridges (SIB) have used seismic isolator models calibrated from component test data, while the prediction accuracy of nonlinear FE models of SIB is rarely addressed by using data recorded from instrumented bridges. In this paper, the accuracy of a state‐of‐the‐art FE model is studied through nonlinear FE model updating (FEMU) of an existing instrumented SIB, the Marga‐Marga Bridge located in Viña del Mar, Chile. The seismic isolator models are updated in 2 phases: component‐wise and system‐wise FEMU. The isolator model parameters obtained from 23 isolator component tests show large scatter, and poor goodness of fit of the FE‐predicted bridge response to the 2010 Mw 8.8 Maule, Chile Earthquake is obtained when most of those parameter sets are used for the isolator elements of the bridge model. In contrast, good agreement is obtained between the FE‐predicted and measured bridge response when the isolator model parameters are calibrated using the bridge response data recorded during the mega‐earthquake. Nonlinear FEMU is conducted by solving single‐ and multiobjective optimization problems using high‐throughput cloud computing. The updated FE model is then used to reconstruct response quantities not recorded during the earthquake, gaining more insight into the effects of seismic isolation on the response of the bridge during the strong earthquake.

Nonlinear FE model updating of seismic isolated bridge instrumented during the 2010 Mw 8.8 Maule-Chile Earthquake

In this paper, nonlinear finite element model updating (FEMU) is performed to study the prediction accuracy of a state-of-the-art finite element (FE) model of a seismic isolated bridge (SIB). The seismic isolator model parameters are updated in two phases: using component-wise and system-wise FEMU. The isolator model parameter values estimated from 23 isolator component tests show a large scatter, and a poor goodness-of-fit (GOF) of the bridge response to the 2010 Maule Earthquake is obtained when most of those parameter sets are used for the isolator elements of the bridge FE model. In contrast, good agreement between the FE predicted and measured bridge response is obtained when the isolator model parameters are calibrated using the bridge response data recorded during the 2010 Mw 8.8 Maule, Chile Earthquake. The updated FE model is then used to reconstruct response quantities not recorded to gain more insight into the effects of seismic isolation during a strong earthquake.

Long-term performance assessment of the Telegraph Road Bridge using a permanent wireless monitoring system and automated statistical process control analytics

The purpose of this study is to advance wireless sensing technology for permanent installation in operational highway bridges for long-term automated health assessment. The work advances the design of a solar-powered wireless sensor network architecture that can be permanently deployed in harsh winter climates where limited solar energy and cold temperatures are normal operational conditions. To demonstrate the performance of the solar-powered wireless sensor network, it is installed on the multi-steel girder bridge carrying northbound I-275 traffic over Telegraph Road (Monroe, Michigan) in 2011; a unique design feature of the bridge is the use of pin and hanger connections to support the bridge main span. A dense network of strain gauges, accelerometers and thermometers are installed to acquire bridge responses of interest to the bridge manager including responses that would be affected by long-term bridge deterioration. The wireless monitoring system collects sensor data on a daily schedule and communicates the data to the Internet where it is stored in a curated data repository. Bridge response data in the repository are autonomously processed to extract truck load events using machine learning, compensate for environmental variations using nonlinear regression and to quantitatively assess anomalous bridge performance using statistical process control.

Experimental analysis of vehicle–bridge interaction using a wireless monitoring system and a two-stage system identification technique

Deterioration of bridges under repeated traffic loading has called attention to the need for improvements in the understanding of vehicle–bridge interaction. While analytical and numerical models have been previously explored to describe the interaction that exists between a sprung mass (i.e., a moving vehicle) and an elastic beam (i.e., bridge), comparatively less research has been focused on the experimental observation of vehicle–bridge interaction. A wireless monitoring system with wireless sensors installed on both the bridge and moving vehicle is proposed to record the dynamic interaction between the bridge and vehicle. Time-synchronized vehicle–bridge response data is used within a two-stage system identification methodology. In the first stage, the free-vibration response of the bridge is used to identify the dynamic characteristics of the bridge. In the second stage, the vehicle–bridge response data is used to identify the time varying load imposed on the bridge from the vehicle. To test the proposed monitoring and system identification strategy, the 180m long Yeondae Bridge (Icheon, Korea) was selected. A dense network of wireless sensors was installed on the bridge while wireless sensors were installed on a multi-axle truck. The truck was driven across the bridge at constant velocity with bridge and vehicle responses measured. Excellent agreement between the measured Yeondae Bridge response and that predicted by an estimated vehicle–bridge interaction model validates the proposed strategy.

Seismic Fragility of Continuous Steel Highway Bridges in New York State

This paper presents results of an analytical seismic fragility analysis of a typical steel highway bridge in New York State. The structural type and topological layout of this multispan I-girder bridge have been identified to be most typical of continuous bridges in New York State. The structural details of the bridge are designed as per New York State bridge design guidelines. Uncertainties associated with the estimation of material strength, bridge mass, friction coefficient of expansion bearings, and expansion-joint gap size are considered. To account for the uncertainties related to the bridge structural properties and earthquake characteristics, 10 statistical bridge samples are established using the Latin Hypercube sampling and restricted pairing approach, and 100 ground motions are simulated numerically. The uncertainties of capacity and demand are estimated simultaneously by using ratios of demands to capacities at different limit states to construct seismic fragility curves as a function of peak ground acceleration and fragility surfaces as a function of moment magnitude and epicentral distance for individual components using nonlinear and multivariate regressions. It has been observed that nonlinear and multivariate regressions show better fit to bridge response data than linear regression conventionally used. To account for seismic risk from multiple failure modes, second-order reliability yields narrower bounds than the commonly used first-order reliability method. The fragility curves and surfaces obtained from this analysis demonstrate that bridges in New York State have reasonably low likelihood of collapse during expected earthquakes.

Pedestrian excitation of bridges

This paper reviews the evidence on dynamic bridge loading caused by moving pedestrians. The phenomenon of ‘synchronization’ by which people respond naturally to an oscillating bridge when this has a frequency close to their natural walking or running frequency is a feature of this phenomenon. By increasing modal damping, synchronization can be prevented, but how much damping is needed in any particular situation? If some simplifying assumptions about how people walk are made, it is possible to predict analytically the minimum damping required to ensure that synchronization does not lead to high vibration levels. The main assumption is that the movement of a pedestrian's centre of mass has two components. One is its natural movement when the person is walking on a stationary pavement. The other is caused by movement of the pavement (or bridge) and is in proportion to pavement amplitude but with a time delay that is arbitrary. When the time delay is a ‘worst case’, pedestrians act as a source of negative damping. This theory supports the adoption of a non-dimensional number which measures the susceptibility of a bridge to pedestrian excitation. Although currently there are not many good bridge response data, predictions using this non-dimensional number are compared with the data that are available and found to be in satisfactory agreement. Both lateral and vertical vibrations are considered.

Bridge-Condition Assessment and Load Rating Using Nondestructive Evaluation Methods

In most cases, bridge-condition assessment is made according to visual inspections, and bridge-load ratings are determined with fairly simple analytical methods and without site-specific, live-load, bridge-response data. As a result, estimates of bridge load-carrying capacity are often quite conservative. The increased weight of today’s trucks compared with design loads that are used for older bridges, combined with the continued aging and deterioration of our nation’s bridges, has resulted in a significant number of them being classified as structurally deficient. Reliable condition assessments are essential to ensure the safety of the traveling public. Furthermore, because load-carrying capacity is often used to prioritize bridges for repair, rehabilitation, and replacement, and because funds for these actions are limited, it is more important than ever that these estimates be as accurate as possible. To achieve this goal, researchers at the University of Delaware have been working with engineers at the Delaware Department of Transportation to develop methods for improving the accuracy of bridge-capacity evaluation through use of nondestructive evaluation techniques. Among the methods currently used are diagnostic load testing and in-service monitoring. These methods are described, and a detailed case study that illustrates the applied methodologies is discussed.