Long-term Bridge Monitoring Research Articles

A Novel Laser and Video-Based Displacement Transducer to Monitor Bridge Deflections.

The measurement of static vertical deflections on bridges continues to be a first-level technological challenge. These data are of great interest, especially for the case of long-term bridge monitoring; in fact, they are perhaps more valuable than any other measurable parameter. This is because material degradation processes and changes of the mechanical properties of the structure due to aging (for example creep and shrinkage in concrete bridges) have a direct impact on the exhibited static vertical deflections. This paper introduces and evaluates an approach to monitor displacements and rotations of structures using a novel laser and video-based displacement transducer (LVBDT). The proposed system combines the use of laser beams, LED lights, and a digital video camera, and was especially designed to capture static and slow-varying displacements. Contrary to other video-based approaches, the camera is located on the bridge, hence allowing to capture displacements at one location. Subsequently, the sensing approach and the procedure to estimate displacements and the rotations are described. Additionally, laboratory and in-service field testing carried out to validate the system are presented and discussed. The results demonstrate that the proposed sensing approach is robust, accurate, and reliable, and also inexpensive, which are essential for field implementation.

Modeling Deformation Induced by Thermal Loading Using Long-Term Bridge Monitoring Data

AbstractAn accurate correlation model between thermal loading and deformation is required for facilitating a reliable deformation-based condition evaluation in bridge service periods. In this paper...

Modelling techniques for structural evaluation for bridge assessment

Load assessment of existing bridges in Australia is evaluated mainly using beam line model and the grillage analogy to examine the structural integrity of bridge components due to live loadings. With the majority of existing bridge networks designed for superseded design vehicular loading, the necessity to utilise more rigorous analysis methods to assess the load effects of bridges is indispensable. In this paper, various vehicular loading cases on a grillage model of a box girder bridge and its equivalent finite element model (FE) are considered, and their applicability for bridge assessment using structural health monitoring (SHM) as defined in the new revision of AS 5100.7 is studied. Based on numerical analyses, it was observed that component-level load effects in the two models have notable differences, irrespective of vehicle speed, position and loading. However, when global-level load responses are compared, the discrepancy in analysis outputs drops dramatically. The modelling ratios developed in this paper are practical and will be applicable with any modelling techniques for bridge assessment under vehicular loading on both a global and component-response basis. It was also observed that FE is more efficient in terms of model updating and damage simulation, and hence more appropriate for implementation of SHM techniques. The proposed flowchart suggested for heavy load assessment incorporates detailed and simple modelling approaches aligned with experimental data obtained by SHM techniques, which can be used for periodic and long-term monitoring of bridges. It can enhance the proper determination of bridge condition states, as any conservative estimation of bridge capacity may result in unnecessary load limitations.

Bridge weigh-in-motion using fibre optic sensors

Bridge weigh-in-motion systems were introduced in the 1970s as a structural health monitoring tool for road bridges. They help bridge operators determine the cause of induced strain in bridges and collect statistics on vehicle weight, class and frequency. There have been over 3000 installations in 25 countries worldwide, which has led to vast improvements in data post-processing. However, existing systems are based on electrical resistance strain gauges, which can be prohibitive in achieving data for long-term monitoring of rural bridges due to power consumption. This paper introduces a new low-power system using fibre optic sensors, which has been pioneered in Northern Ireland. A series of fibre optic sensors were attached to the soffit of an existing integral bridge with a single span of 19 m. The site selection criteria and full installation process are described in this paper. A method of calibration was adopted using live traffic at the bridge site, based on which the accuracy of the system was determined. New methods of axle detection for bridge weigh-in-motion were investigated and verified in the field.

Robust Object-Based Multipass InSAR Deformation Reconstruction

Deformation monitoring by multipass synthetic aperture radar (SAR) interferometry (InSAR) is, so far, the only imaging-based method to assess millimeter-level deformation over large areas from space. Past research mostly focused on the optimal retrieval of deformation parameters on the basis of a single pixel or a pixel cluster. Only until recently, the first demonstration of object-based urban infrastructure monitoring by fusing InSAR and the semantic classification labels derived from optical images was presented by Wang et al. Given such classification labels in the SAR image, we propose a general framework for object-based InSAR parameter retrieval, where the parameters of the whole object are jointly estimated by the inversion of a regularized tensor model instead of pixelwise. Our approach does not assume the stationarity of each sample in the object, which is usually assumed in other pixel cluster-based methods, such as SqueeSAR. In addition, to handle outliers in real data, a robust phase recovery step prior to parameter retrieval is also introduced. In typical settings, the proposed method outperforms the current pixelwise estimators, e.g., periodogram, by a factor of several tens in the accuracy of the linear deformation estimates. Last but not least, for a practical demonstration on bridge monitoring, we present a full workflow of long-term bridge monitoring using the proposed approach.

Quality Evaluation of Fiber-Optic Strain Data Acquired in Long-Term Bridge Monitoring

Quality Evaluation of Fiber-Optic Strain Data Acquired in Long-Term Bridge Monitoring

Innovative Structural Damage Detection of Bridges by Least Squares Adjustment with Constraints

Long-term monitoring of bridges requires the early detection of spreading damage because very often the damage is not really visible. For many decades engineers and scientists relayed on dynamic methods especially modal ones. However, after fundamental tests by the U.S. FHWA in 1993, it became evident that more successful methods should be developed, but no innovation really happened. A substantial innovative detection method started four years ago from Lagrange Multiplier Testing which eventually resulted in experimental verification based on measurement of deflection and curvature of the bridge beam and together the methodological combination of both.

Estimation of main cable tension force of suspension bridges based on ambient vibration frequency measurements

In this paper, a new approach based on the continuum model is proposed to estimate the main cable tension force of suspension bridges from measured natural frequencies. This approach considered the vertical vibration of a main cable hinged at both towers and supported by an elastic girder and hangers along its entire length. The equation reflected the relationship between vibration frequency and horizontal tension force of a main cable was derived. To avoid to generate the additional cable tension force by sag-extensibility, the analytical solution of characteristic equation for anti-symmetrical vibration mode of the main cable was calculated. Then, the estimation of main cable tension force was carried out by anti-symmetric characteristic frequency vector. The errors of estimation due to characteristic frequency deviations were investigated through numerical analysis of the main cable of Taizhou Bridge. A field experiment was conducted to verify the proposed approach. Through measuring and analyzing the responses of a main cable of Taizhou Bridge under ambient excitation, the horizontal tension force of the main cable was identified from the first three odd frequencies. It is shown that the estimated results agree well with the designed values. The proposed approach can be used to conduct the long-term health monitoring of suspension bridges.

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.

A Bayesian approach for vibration-based long-term bridge monitoring to consider environmental and operational changes

This study aims to propose a Bayesian approach to consider changes in temperature and vehicle weight as environmental and operational factors for vibration-based long-term bridge health monitoring. The Bayesian approach consists of three steps: step 1 is to identify damage-sensitive features from coefficients of the auto-regressive model utilizing bridge accelerations; step 2 is to perform a regression analysis of the damage-sensitive features to consider environmental and operational changes by means of the Bayesian regression; and step 3 is to make a decision on the bridge health condition based on residuals, differences between the observed and predicted damage-sensitive features, utilizing 95% confidence interval and the Bayesian hypothesis testing. Feasibility of the proposed approach is examined utilizing monitoring data on an in-service bridge recorded over a one-year period. Observations through the study demonstrated that the Bayesian regression considering environmental and operational changes led to more accurate results than that without considering environmental and operational changes. The Bayesian hypothesis testing utilizing data from the healthy bridge, the damage probability of the bridge was judged as no damage.

Case studies: long-term monitoring of timber bridges

Timber bridges are subjected to climatic changes respectively cycles which result, due to the hygroscopic material behavior, in a change of the moisture content in the wood and thus change of dimensions of the wooden elements. But there is still missing knowledge about the impact of the climatic changes. Thus, the objective was to investigate the material behavior due to climatic changes regarding the moisture content and dimensional changes in relation to the natural material axes and within the cross section. Therefore different traffic timber bridges with big cross sections were long-term monitored within a research project. The results of the moisture contents measured and a comparison between the different measuring points as groups and at different positions are presented. The results confirm that the wood moisture content follows the climatic changes delayed and with smaller amplitude compared to the calculated equilibrium moisture content. It was observed that the change of the moisture content differs over the cross section and along the span of the members. Moreover, due to the monitoring systems, a damage of the bridge could be detected at an early stage and further damage could be prevented.

Field Application Case Studies and Long-Term Monitoring of Bridges Utilizing the Simple for Dead--Continuous for Live Bridge System

The performance of three bridges constructed using the simple for dead load and continuous for live load (SDCL) bridge system for steel girders was monitored during and after construction to compare actual performance with predicted performance. The structure types were a box-girder bridge, an I-girder bridge and a box-girder bridge built using accelerated construction details. During construction, strains and deflections were monitored so that the degree of continuity over the pier could be determined. The design concept assumes that a simply supported condition exists during casting of the concrete deck. However, to provide lateral bracing, the concrete diaphragm—or turndown—over the pier is cast and cured prior to casting the deck. As expected, encasement of the girders provides some continuity over the pier during casting of the deck. The degree of continuity over the pier can be reduced by lowering the height of the construction joint and through the use of crack-inducing details. Long-term monitoring of the structures showed the behavior to be consistent over time with no significant deviations from the predicted bridge behavior. During the initial time period of approximately 18 months, a slight overall change in strain values was observed in concrete elements. The rate of change slowed during this period and eventually ceased. Subsequently, the response of the structure has been consistent with only small seasonal fluctuations observed. These fluctuations are expected and are generally attributable to changes in ambient temperature, relative humidity, incident solar radiation and ground freeze/thaw conditions.

Structural health monitoring of a cable-stayed bridge with Bayesian neural networks

In recent years, there has been an increasing interest in permanent observation of the dynamic behaviour of bridges for long-term monitoring purpose. This is due not only to the ageing of a lot of structures, but also for dealing with the increasing complexity of new bridges. The long-term monitoring of bridges produces a huge quantity of data that need to be effectively processed. For this purpose, there has been a growing interest on the application of soft computing methods. In particular, this work deals with the applicability of Bayesian neural networks for the identification of damage of a cable-stayed bridge. The selected structure is a real bridge proposed as benchmark problem by the Asian-Pacific Network of Centers for Research in Smart Structure Technology (ANCRiSST). They shared data coming from the long-term monitoring of the bridge with the structural health monitoring community in order to assess the current progress on damage detection and identification methods with a full-scale example. The data set includes vibration data before and after the bridge was damaged, so they are useful for testing new approaches for damage detection. In the first part of the paper, the Bayesian neural network model is discussed; then in the second part, a Bayesian neural network procedure for damage detection has been tested. The proposed method is able to detect anomalies on the behaviour of the structure, which can be related to the presence of damage. In order to obtain a confirmation of the obtained results, in the last part of the paper, they are compared with those obtained by using a traditional approach for vibration-based structural identification.

Integration of in-situ load experiments and numerical modeling in a long-term bridge monitoring system on a newly-constructed widened section of freeway in Taiwan

The widening project on Freeway No.1 in Taiwan has a total length of roughly 14 kilometers, and includes three special bridges, namely a 216 m long-span bridge crossing the original freeway, an F-bent double decked bridge in a co-constructed section, and a steel and prestressed concrete composite bridge. This study employed in-situ monitoring in conjunction with numerical modeling to establish a real-time monitoring system for the three bridges. In order to determine the initial static and dynamic behavior of the real bridges, forced vibration experiments, in-situ static load experiments, and dynamic load experiments were first carried out on the newly-constructed bridges before they went into use. Structural models of the bridges were then established using the finite element method, and in-situ vehicle load weight, arrangement, and speed were taken into consideration when performing comparisons employing data obtained from experimental measurements. The results showed consistency between the analytical simulations and experimental data. After determining a bridge\'s initial state, the proposed in-situ monitoring system, which is employed in conjunction with the established finite element model, can be utilized to assess the safety of a bridge\'s members, providing useful reference information to bridge management agencies.

INSAR AND FINITE ELEMENT ANALYSIS OF GROUND DEFORMATION AT LAKE URMIA CAUSEWAY (LUC), NORTHWEST IRAN

Abstract. Precise long-term deformation monitoring of causeways and bridges is of vital task for maintenance and management work related to transportation safety. In this study, we analyse the settlement of Lake Urmia Causeway (LUC), northwest Iran, using observations from InSAR and Finite Element Model (FEM) simulation. For InSAR processing, we analyse 58 SAR images of ENVISAT, ALOS and TerraSAR-X (TSX) using the SBAS technique to assess the settlement of embankments in the years 2003–2013. The InSAR results show deflation on both embankments with a peak velocity of > 5 cm/year in the satellite Line Of Sight (LOS) direction. The InSAR observations are then used to construct a settlement compaction model for the cross section at the distance of 4 km from the most western edge of the causeway, using a 2D Finite Element Model. Our FEM results suggest that settlement of the embankments will continue in the future due to consolidation phenomenon.

Research on Fiber Bragg Grating Sensing Technique for Cable Tension Monitoring of Suspension Bridges

In order to realize long-distance, long-term and on-line cable tension monitoring of suspension bridges, a novel sensor has been designed based on Fiber Bragg Grating (FBG) sensing technique, which comprises of fixtures of the sensor as well as FBG, micro-spring and epoxy resin made large gauge sensing element. Further, following the calibration test for the designed FBG sensor, research on the calibrated sensor for monitoring the cable tension was carried out, whose result shows that, featured by the simplicity of operation and the accurate monitoring data, the designed FBG sensor is capable of monitoring the cable tension of built suspension bridges.

Linear approaches to modeling nonlinearities in long-term monitoring of bridges

In the structural health monitoring of bridges, the effects of the operational and environmental variability challenge the reliability of damage detection performance during long-term monitoring. In order to filter those effects to enhance novelty detection on bridges, in the last decade recourse has been made to so-called machine learning algorithms. Generally, such algorithms are based on purely data-based methods because they only rely on features from the structural response data and they are independent of the structural complexity. However, due to their formulation, some of those algorithms most commonly used are only able to remove linear patterns from the features, which might not be appropriate in cases where the structural stiffness varies, for instance, with the temperature gradient range. Therefore, this paper presents an algorithm based on Gaussian mixture models for damage detection under unknown sources of variability and compares it with well-known linear algorithms and one nonlinear algorithm with the capability to model nonlinearities of the baseline data. The study is performed on standard data sets from the Z-24 Bridge, in Switzerland.

Bridge Deck Load Testing Using Sensors and Optical Survey Equipment

Bridges are under various loads and environmental impacts that cause them to lose their structural integrity. A significant number of bridges in US are either structurally deficient or functionally obsolete, requiring immediate attention. Nondestructive load testing is an effective approach to measure the structural response of a bridge under various loading conditions and to determine its structural integrity. This paper presents a load-test study that evaluated the response of a prefabricated bridge with full-depth precast deck panels in Michigan. This load-test program integrates optical surveying systems, a sensor network embedded in bridge decks, and surface deflection analysis. Its major contribution lies in the exploration of an embedded sensor network that was installed initially for long-term bridge monitoring in bridge load testing. Among a number of lessons learned, it is concluded that embedded sensor network has a great potential of providing an efficient and accurate approach for obtaining real-time equivalent static stresses under varying loading scenarios.

A Study on the Problems with Long-Term Bridge Monitoring Systems, and Suggested Solutions

The first long-term bridge monitoring system in Korea was installed in 1995, and many bridges have been maintained by long-term monitoring systems. Recently, reliability of data and cost effectiveness have been increased by advanced sensor technology (fiber optic sensor, RFID, USN etc) and measuring equipment. In Korea, the large-scale project for the safety network integration for long-term smart monitoring systems for bridge structures started in 2007, and this is the second year of the project. In this system, various innovative sensor types are considered. To increase the effectiveness of this network system, an analysis of the problems with the conventional long-term bridge monitoring system and solution investigations are needed. The biggest problems are low durability and data reliability because of noise, and the lack of data applications techniques. Therefore, in this paper, a brief summary of the projects is presented and the state of bridge monitoring systems in Korea is investigated, and various problems and solutions for these problems are briefly suggested.

Passive Structural Health Monitoring of Connecticut’s Bridge Infrastructure

This paper reports on a long-term bridge monitoring program in Connecticut. Monitoring systems have been designed and installed on a series of different bridges, beginning in 1999. Ambient data are collected from normal traffic, using strain gauges, accelerometers, tiltmeters, and thermocouples. This paper discusses three of the monitored bridges, demonstrating the benefits of using long-term monitoring systems to provide information on the structural performance that is useful both to those responsible for the bridge infrastructure and to researchers interested in bridge monitoring. The extensive data developed from multiyear monitoring have been used to create benchmarks for passive structural health monitoring for each of the three bridges. Relationships and trends discovered for each of the bridges, used for the creation of the benchmarks, and stored in a database, are discussed.