Bridge Damage Detection Research Articles

An effective means for damage detection of bridges using the contact-point response of a moving test vehicle

To further the technique of indirect measurement, the contact-point response of a moving test vehicle is adopted for the damage detection of bridges. First, the contact-point response of the vehicle moving over the bridge is derived both analytically and in central difference form (for field use). Then, the instantaneous amplitude squared (IAS) of the driving component of the contact-point response is calculated by the Hilbert transform, making use of its narrow-band feature. The IAS peaks serve as the key parameter for damage detection. In the numerical simulation, a damage (crack) is modeled by a hinge-spring unit. The feasibility of the proposed method to detect the location and severity of a damage or multi damages of the bridge is verified. Also, the effects of surface roughness, vehicle speed, measurement noise and random traffic are studied. In the presence of ongoing traffic, the damages of the bridge are identified from the repeated or invariant IAS peaks generated for different traffic flows by the same test vehicle over the bridge.

Investigation of dynamic properties of long-span cable-stayed bridges based on one-year monitoring data under normal operating condition

Environmental factors, such as temperature, traffic, and wind, play an important role on the variations of dynamic properties of long-span cable-stayed bridges. The dynamic characteristics of Sutong Cable-Stayed Bridge (SCB), including acceleration and strain responses as well as modal frequencies, are investigated using one-year continuous monitoring data under operating conditions by the structural health monitoring system. The in situ wind characteristics and structural temperature behavior of SCB are also analyzed. More than 99% of the wind speed values are smaller than 16 m/s; and the largest temperature variation of the main girder exceeds 60 °C. Besides, acceleration and strain, root mean square (RMS) data are both normalized using the Z-score standardization method. Relation analysis between the normalized acceleration and strain RMS values is conducted based on the time-history comparison and linear least square fitting. Results show that both of the processed acceleration and strain RMS values could properly describe the variation trend of the traffic load, although variation amplitudes of the two normalized parameters differ from each other. In addition, one-year continuous modal frequencies of SCB are identified using Hilbert–Huang transform method. Variability analysis of the structural modal frequencies due to environmental temperature and operational traffics is then conducted. Results show that temperature is the most important environmental factor for vertical and torsional modal frequencies. The traffic load is the second critical factor especially for the fundamental vertical frequency of SCB. Research results could provide references for damage detection and safety evaluation for similar long-span cable-stayed bridges.

State-of-the-Art Review on Modal Identification and Damage Detection of Bridges by Moving Test Vehicles

In 2004, Yang and co-workers proposed the extraction of bridge frequencies from the dynamic response of a moving test vehicle [Y. B. Yang, C. W. Lin and J. D. Yau, Extracting bridge frequencies from the dynamic response of a passing vehicle, J. Sound Vib. 272 (2004) 471–493] and verified the technique by a field test [C. W. Lin and Y. B. Yang, Use of a passing vehicle to scan the bridge frequencies — An experimental verification, Eng. Struct. 27(13) (2005) 1865–1878]. This technique was extended to construction of mode shapes [Y. B. Yang, Y. C. Li and K. C. Chang, Constructing the mode shapes of a bridge from a passing vehicles: A theoretical study, Smart Struct. Syst. 13(5) (2014) 797–819] and damage identification of bridges. It was referred to as the indirect method for bridge measurement because no vibration sensors are needed for installation on the bridge, but it only requires one or few vibration sensors on the test vehicle. When compared with the conventional direct method that relies fully on the response of the bridge fitted with vibration sensors, the advantage of the indirect method is clear: mobility, economy, and efficiency. Over the past years, many research studies were conducted along the lines of the indirect method for bridge measurement. Significant advances have been made on various aspects of application. This paper represents a state-of-the-art review of the related research works 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.

The Feasibility of Using Laser Doppler Vibrometer Measurements from a Passing Vehicle for Bridge Damage Detection

This paper investigates the feasibility of detecting local damage in a bridge using Laser Doppler Vibrometer (LDV) measurements taken from a vehicle as it passes over the bridge. Six LDVs are simulated numerically on a moving vehicle, collecting relative velocity data between the vehicle and the bridge. It is shown that Instantaneous Curvature (IC) at a moving reference, which is the curvature of the bridge at an instant in time, is sensitive to local damage. The vehicle measures Rate of Instantaneous Curvature (RIC), defined as the first derivative of IC with respect to time. A moving average filter is found to reduce the effects of noise on the RIC data. A comparison of filtered RIC measurements in healthy and damaged bridges shows that local damage can be detected well with noise‐free measurements and can still be detected in the presence of noise.

Research on Damage Identification of Bridge Based on Digital Image Measurement

In recent years, the number of the damage bridge due to excessive deformation gradually increased, which caused significant property damage and casualties. Hence health monitoring and the damage detection of the bridge structure based on the deflection measurement are particularly important. The current conventional deflection measurement methods, such as total station, connected pipe, GPS, etc., have many shortcomings as low efficiency, heavy workload, low degree of automation, operating frequency and working time constrained. GPS has a low accuracy in the vertical displacement measurement and cannot meet the dynamic measured requirements of the current bridge engineering. This paper presents a bridge health monitoring and damage detection technology based on digital image measurement method in which the measurement accuracy is sub-millimeter level and can achieve the 24-hour automatic non-destructive monitoring for the deflection. It can be concluded from this paper that it is feasible to use digital image measurement method for identification of the damage in the bridge structure, because it has been validated by the theoretical analysis, the laboratory model and the application of the real bridge.

A systematic method from influence line identification todamage detection: Application to RC bridges

Ordinary reinforced concrete (RC) and prestressed concrete bridges are two popular and typical types of short- and medium-span bridges that accounts for the vast majority of all existing bridges. The cost of maintaining, repairing or replacing degraded existing RC bridges is immense. Detecting the abnormality of RC bridges at an early stage and taking the protective measures in advance are effective ways to improve maintenance practices and reduce the maintenance cost. This study proposes a systematic method from influence line (IL) identification to damage detection with applications to RC bridges. An IL identification method which integrates the cubic B-spline function with Tikhonov regularization is first proposed based on the vehicle information and the corresponding moving vehicle induced bridge response time history. Subsequently, IL change is defined as a damage index for bridge damage detection, and information fusion technique that synthesizes ILs of multiple locations/sensors is used to improve the efficiency and accuracy of damage localization. Finally, the feasibility of the proposed systematic method is verified through experimental tests on a three-span continuous RC beam. The comparison suggests that the identified ILs can well match with the baseline ILs, and it demonstrates that the proposed IL identification method has a high accuracy and a great potential in engineering applications. Results in this case indicate that deflection ILs are superior than strain ILs for damage detection of RC beams, and the performance of damage localization can be significantly improved with the information fusion of multiple ILs.

Railway bridge damage detection using vehicle-based inertial measurements and apparent profile

The presence of damage in a railway bridge alters its stiffness and consequently, its static and dynamic response to loading. The dynamic response of a railway bridge to loading produces a dynamic response in the passing vehicle, potentially enabling detection of the damage through analysis of the vehicle response. A method is proposed in this paper for the detection of bridge damage through comparison of apparent profiles sensed by the passing vehicle. The apparent profile is a virtual longitudinal profile which, when applied to a vehicle, invokes the same measured response to the crossing of a bridge. A change in the deflection of the bridge due to the presence of damage manifests itself as a change in the apparent profile associated with the measured vehicle response.The Cross Entropy optimisation method is used in this study to determine the apparent profiles that generate a vehicle dynamic response most similar to that of a measured input. ‘Measured’ bogie vertical acceleration signals are generated using a 3-dimensional train-bridge interaction model implemented in Abaqus and used as input to a 2-dimensional algorithm implemented in Matlab. Damage is introduced to the bridge structure by applying a localised reduction in the width of the bottom flange of a bridge beam, simulating the effect of a sudden impact from a vehicle strike. Apparent profiles for a number of damage scenarios are inferred and compared over time to detect damage. The algorithm is also tested for resilience to sensor noise and effectiveness in the presence of track irregularities.

Bridge damage detection using vehicle axle-force information

Moving Force Identification (MFI) is the process of back-calculating the applied axle force histories from bridge measurements. This paper investigates the use of an MFI algorithm to detect the presence of bridge damage by monitoring calculated vehicle applied axle forces. Bridge deflections at three points along the bridge are used as the input to the algorithm. It is found that the combination of mean calculated gross vehicle weight and mean calculated axle weight ratio for a population of similar two-axle vehicles can be combined to indicate the location and severity of damage on a bridge. The bridge is modelled as a simply supported beam and deflections at the quarter point, midpoint and three quarter point are used as the inputs to the MFI algorithm. The method is shown to work best when damage is closer to the centre of the bridge.

Structural Health Monitoring (SHM) of Civil Structures

As newer and more reliable ways of construction were developed, civilization began to spread out further and retain functional infrastructure for longer periods of time.[...]

Wave Number-Based Technique for Detecting Slope Discontinuity in Simple Beams Using Moving Test Vehicle

The wavelength characteristic is a useful clue for locating and assessing the severity of slope discontinuity in beams. In this study, the slope discontinuity of a beam is represented by an internal hinge restrained by elastic springs, and the wavelength of the beam is calculated indirectly from the vertical response of a test vehicle during its travel over the beam. The key parameters of the problem at hand are first unveiled using an approximate, closed-form solution for the response of the vehicle moving at low speeds over the bridge. Then a two-beam element model with slope discontinuity is formulated for the vehicle–bridge interaction (VBI) system for use in numerical simulation. In the examples, the wavenumber-based response of the test vehicle is used to identify the location and severity of the discontinuity in the beam. It is demonstrated that the wavelength-based technique presented herein by using the moving test vehicle as a moving sensor system offers a promising, alternative approach for damage detection in girder type bridges.

Vibration-Based Damage Detection of Bridges under Varying Temperature Effects Using Time-Series Analysis and Artificial Neural Networks

AbstractStructural health monitoring (SHM) has become a very important research area for evaluating the performances of bridges. An important issue with continuous SHM and damage detection of bridg...

Drive-by bridge damage detection using non-specialized instrumented vehicle

Drive-by bridge damage detection using non-specialized instrumented vehicle

Investigation on a curvature-based damage detection method using displacement under moving vehicle

Detection of potential damages is of much significance for aging bridges, which has attracted extensive attention in recent years. In this paper, a damage detection method is proposed utilizing dynamic displacement of a bridge under a moving vehicle. First, the theoretical basis of this method is elaborated. The idea is to use the static component of displacement measurements under a moving vehicle, and to use the calculated curvature change to identify damage in bridges. In order to obtain the static component, a technique is proposed for curvature calculation. Second, the proposed method is verified with two examples. In the first example, a finite element model of a single span bridge under a moving vehicle is used to show reliability of the method. Both vehicle–bridge interaction and road surface roughness are considered in the analysis. Parametric study on damage intensity, data acquisition location, vehicle passing path, and damping ratio provides guidance for application in real bridges. In the second example, a field test on a prestressed concrete viaduct is conducted to calibrate its finite element model. Artificial damage, that is, concrete crack and tendon rupture, was created, and the proposed method is used to identify the damage. Analysis results show capability of the method. Finally, conclusions are drawn, and suggestions are given for application of the proposed method on damage detection of real bridges.

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature.

Damage detection of a truss bridge utilizing a damage indicator from multivariate autoregressive model

This study proposes a damage indicator automatically derived from a set of multivariate autoregressive models estimated from ambient vibration of bridges. The damage indicator evaluates a stochastic distance between a set of reference data (healthy bridge data) and unknown test data. Statistical hypothesis testing based on a probability distribution of the damage indicator was applied for damage detection. A field experiment conducted on an actual steel truss bridge with truss members that were artificially severed was conducted to assess the damage detection efficacy of the proposed damage indicator. Experimentally obtained results showed that the proposed damage indicator enables detection of three damage patterns. The proposed damage indicator effectiveness was also assessed by comparison to the damage sensitive feature from a univariate autoregressive model using experimental data of the same bridge. This comparison also demonstrated the efficacy of the proposed damage indicator obtained from a multivariate linear system.

Bridge Damage Detection Based on Vibration Data: Past and New Developments

Overtime, the structural condition of bridges tends to decline due to a number of degradation processes, such as creep, corrosion and cyclic loading, among others. Considerable research has been conducted over the years to assess and monitor the rate of such degradation with the aim of reducing structural uncertainty. Traditionally, vibration-based damage detection techniques in bridges have focused on monitoring changes to modal parameters and subsequently comparing them to numerical models. These traditional techniques are generally time consuming and can often mistake changing environmental and operational conditions as structural damage. Recent research has seen the emergence of more advanced computational techniques that not only allow the assessment of noisier and more complex data, but also allow research to veer away from monitoring changes in modal parameters alone. This paper presents a review of the current state-of-the-art developments in vibration based damage detection in small to medium span bridges with particular focus on the utilization of advanced computational methods that avoid traditional damage detection pitfalls. A case study of the S101 Bridge is also presented to test the damage sensitivity a chosen methodology.

RETRACTED ARTICLE: Reality of virtual damage identification based on neural networks and vibration analysis of a damaged bridge under a moving vehicle

Presented here is a reality of virtual damage detection and vibration behaviour study of a discrete beam-like bridge with one or several non-propagating edge cracks subjected to a moving vehicle. In this model, the simply supported beam elements are replaced by a range of rigid bars, which are connected by transverse and rotational springs, while the mass and rotational moment of inertia may be lumped at various points along the beam. The adopted vehicle model here is a four degrees-of-freedom, two axes half-vehicle model with tires flexibility and linear suspensions. Damage can be modelled by altering the spring stiffness equation at the crack position according to predictions, which allows the inclusion of simple or complex damage. To simplify, damage is represented here by an open crack, and stiffness of a given element with damage is calculated by fracture mechanics. Both the discrete element and finite element methods are used to investigate vibration analysis of a discrete beam model subjected to a moving vehicle to confirm model feasibility in vibration analysis under a moving vehicle. Besides, some dynamic response laws are obtained. Considering an irregular road profile, the effects of the moving vehicle velocity, the moving vehicle mass, the crack location and the crack depth on dynamic response of a beam-like bridge are analysed by a numerical example, combining a vehicle–bridge coupled vibration MATLAB program with ANSYS. In addition, the neural network is used to identify the damage of the structure. Numerical results of the numerical model predictions, compared with those obtained from the continuous elements beam, support the accuracy of the discrete elements beam model in both cases of undamaged beam and damaged one. The evidence for condition assessment and damage identification of bridge is obtained from this simulation as obtaining the vibrational characteristics of the damaged beam structure subjected to a moving vehicle. And the inversion results show that the neural network method can identify the injury location and injury size of the structure accurately.

Damage detection in railway bridges using Machine Learning: application to a historic structure

This paper presents a method that uses machine learning to detect and localize damage in railway bridges. Results of the method application to a historical bridge are presented and used to validate the proposed algorithm.For the application of this technique, both air temperature and deck accelerations data, measured under railway traffic at several locations on the bridge, are needed. The method consists of four stages: (1) collection of such data in both reference condition (i.e. when the state of preservation of the structure is known) and current one; (2) pre-processing of acceleration time histories aimed at extracting characteristics of the crossing train (i.e. running direction, speed and number of axles); (3) training of Artificial Neural Networks and Gaussian Processes using data collected in reference condition and (4) health classification of the bridge in current condition through the comparison between predicted and measured responses. During stage 3, a set of neural networks is trained to predict deck accelerations under every environmental and operational condition (i.e. air temperature and crossing vehicle characteristics, respectively) assuming the reference state of preservation. Then, in stage 4, the current response is compared with accelerations predicted under current environmental and operational conditions. Changes in the behavior of the structure due to damage are thus detected as a discrepancy between predicted and measured responses.The application of the proposed technique to data collected on San Michele Bridge (1889), in Northern Italy, has shown good agreement with results from previous studies based on mode shape variation. This shows the potential and confirms the possibility of applying the proposed technique to real bridges. This method can thus be used to detect anomalous responses that can be flagged as possible damage as well as give an indication of the location of the decayed structural region.

Optimal sensor placement for damage detection of bridges subject to ship collision

Ship collisions threaten the safety of bridges over navigable waterways in modern times. Postcollision damage and condition assessment is thus of significant importance for decision making on whether closure of bridge to traffic is necessary and for planning the consequent bridge strengthening or retrofitting. Online structural health monitoring systems provide a unique approach to monitor bridge responses during ship collisions and detect the structural damage. The damage information contained in the monitoring data, which is critical for damage detection, however, is largely dependent on the sensor layout. In this paper, an optimal sensor placement method targeting postcollision damage detection of bridges is proposed for selecting the optimal sensor set so that the measured data are most informative for damage detection. The sensor configuration is optimized by a multi-objective optimization algorithm, which simultaneously minimizes the information entropy index for each possible ship-bridge collision scenario. One advantage of the proposed method is that it can handle the uncertainty of ship collision position. It also guarantees a redundancy of sensors for the most informative regions and leaves a certain freedom to determine the critical elements for monitoring. The proposed method is applicable in practice to determine the sensor placement, prior to field testing, with the intention of identifying postcollision damage. The cable-stayed Ting Kau bridge in Hong Kong is employed to demonstrate the feasibility and effectiveness of the proposed method.

Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density.