Long-Term Structural Health Monitoring of the San Ysidro Bridge

Barr, Paul; Woodward, Clinton; Najera, B.; Amin, Noraziah Mohd

doi:10.1061/(asce)0887-3828(2006)20:1(14)

Cited by 20 publications

(6 citation statements)

References 5 publications

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“…Other studies demonstrate the use of (quasi-)static response measurements of real-world concrete bridges for the inference of structural or load parameters. FE models of bridges were successfully validated or updated based on strain measurements in the studies, 9,10 which serve as baseline models for future SHM throughout the lifetime of the structure. Yang et al 11 showed how a prestressed concrete box girder bridge with installed (quasi-)distributed optical fibers, so called "long gauge" fiber Bragg grating sensors of 1 m, is used as a weigh-in-motion system to identify loads and vehicle speeds.…”

Section: Introductionmentioning

confidence: 99%

Structural and load parameter estimation of a real‐world reinforced concrete slab bridge using measurements and Bayesian statistics

Rózsás

Slobbe

Martini

et al. 2022

Structural Concrete

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This paper describes a static diagnostic load testing and measurement campaign of a reinforced concrete road bridge in Amsterdam. We consider 29 vertical translation sensors and 37 strain sensors. Multiple Bayesian parameter estimations are performed to estimate two structural and two load parameters of a three-dimensional finite element (FE) model. The structural parameters are the concrete elastic modulus of the deck and the rotational spring stiffness at the piers. The load (truck) parameters are the load mass and position, which are estimated from separate measurements. We found that estimating (updating) the two selected structural parameters considerably improves the vertical translation model prediction accuracy over the FE model that was built before the measurement campaign, that is, the R 2 score increases from 0.90 to 0.98. Moreover, the load (truck) mass and position parameters can be estimated with high accuracy and high precision. When all measurements are used, the load mass and position are estimated with an error, respectively, less than 1.5 tonnes and 0.5 m with respect to the ground truth. On the negative side: when only certain strain measurements are used, the 90% posterior credible interval is about 3 m and 10 tonnes off the ground truth. Although the results are promising, they are restricted to the considered case and, more importantly, to operational loading conditions where the loading is well controlled. All data, models, and code are freely available upon request.

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Section: Introductionmentioning

confidence: 99%

Structural and load parameter estimation of a real‐world reinforced concrete slab bridge using measurements and Bayesian statistics

Rózsás

Slobbe

Martini

et al. 2022

Structural Concrete

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“…Alampalli and Lund (2006) presented a case study where measured strain data were used to predict the remaining fatigue life of certain bridge components. Barr et al (2006) used a FEM that was verified by NDT data to calculate more refined load rating factors leading to an eventual increase in load rating for a three-span highway bridge. Chajes and Shenton (2005) present a method for load rating on the basis of the NDT data that includes the investigation of lateral load distribution, support fixity, composite action, and the effect of secondary members.…”

Section: Introductionmentioning

confidence: 99%

Instrumentation, Nondestructive Testing, and Finite-Element Model Updating for Bridge Evaluation Using Strain Measurements

et al. 2012

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A baseline finite element model was developed for bridge management and calibration using nondestructive test data. The model calibration technique was evaluated on the Vernon Avenue Bridge over the Ware River in Barre, Massachusetts. This newly constructed bridge was instrumented throughout its construction phases in preparation for a static truck load test performed before the bridge opening. The strain data collected during the load test was used to calibrate a detailed baseline finite element model in an effort to represent the 3D system behavior of the bridge. Three methods of load ratings were used and compared: (1) conventional method, (2) conventional method updated by using NDT data, and (3) finite element model calibrated with NDT data.

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“…Structural health monitoring is a method which can obtain the structural responses and give the estimation of the working status of the structure, while different types of sensors are arranged on the structure. Recently, the researchers [1][2][3][4][5] put emphasis on realizing objectives and functions of structural health monitoring system using limited measurements of sensors. For example, Ming Liu et al [6] assessed the reliability of bridge through the long-term monitoring measurements of strain sensors under traffic loads and researched the  Corresponding author: Jun Teng, PhD, professor, research field: structural health monitoring.…”

Section: Introduction mentioning

confidence: 99%

Structural Stress Identification Using Fuzzy Pattern Recognition and Information Fusion Technique

Teng¹,

Zhang²,

Lu³

2012

JCEA

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In order to ensure the service security of space structures under wind load, the stress identification method based on the combination of fuzzy pattern recognition and information fusion technique is proposed, in which the measurements of limited strain sensors arranged on the structure are used. Firstly, the structure is divided into several regions according to the similarity and the most unfavorable region is selected to be the key region for stress identification, while the different numbers of the strain sensors are located on the key region and the normal regions; secondly, the different stress distributions of the key region are obtained based on the measurements of the strain sensors located on the key region and the normal regions separately, in which the fuzzy pattern recognition is used to identify the different stress distributions; thirdly, the stress distributions obtained by the measurements of sensors in normal regions are selected to calculate the synthesized stress distribution of the key region by D-S evidence theory; fourthly, the weighted fusion algorithm is used to assign the different fusion coefficients to the selected stress distributions obtained by the measurements of the normal regions and the key region, while the synthesized stress distribution of the key region can be obtained. Numerical study on a lattice shell model is carried out to validate the reliability of the proposed stress identification method. The simulated results indicate that the method can improve identification accuracy and be effective by different noise disturbing.

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Long-Term Structural Health Monitoring of the San Ysidro Bridge

Cited by 20 publications

References 5 publications

Structural and load parameter estimation of a real‐world reinforced concrete slab bridge using measurements and Bayesian statistics

Structural and load parameter estimation of a real‐world reinforced concrete slab bridge using measurements and Bayesian statistics

Instrumentation, Nondestructive Testing, and Finite-Element Model Updating for Bridge Evaluation Using Strain Measurements

Structural Stress Identification Using Fuzzy Pattern Recognition and Information Fusion Technique

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