A Two-Step Drive-By Bridge Damage Detection Using Dual Kalman Filter

Li, Jiantao; Zhu, Xinqun; Law, S.S.; Samali, Bijan

doi:10.1142/s0219455420420067

Cited by 44 publications

(15 citation statements)

References 32 publications

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“…In the right‐hand side of Equation 8,

F_{cp} ((), t)

is the unknown input force to the vehicle system that is directly related to the CP response. The identification of the force is a joint state‐input estimation problem that can be solved by using the Newmark‐β‐based DKF 26 . After F cp ( t) is identified, the CP response can be obtained by

d_{cp} ((), t) goodbreak= F_{cp} ((), t) / k_{t}

The input force of the vehicle system is identified based on a state space representation of Equation 8 using Newmark‐β‐based DKF.…”

Section: Preliminaries For the Proposed Methodsmentioning

confidence: 99%

“…The model vehicle was pulled along the beam with an electric motor. The detailed description of the tests can be found in Li et al 26 …”

Section: Experimental Studymentioning

confidence: 99%

“…The identified contact‐point response enhanced the bridge modal frequency identification, but the method can only deal with the single contact‐point problems. Based on a joint input‐state estimation procedure, Li et al 26 proposed a method using the dual Kalman filter (DKF) for the interaction force identification. The interaction forces between the two‐axle vehicle and bridge were identified using acceleration responses of vehicle axles.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced drive‐by bridge modal identification via dual Kalman filter and singular spectrum analysis

Zhu

Guo

2022

Structural Contr & Hlth

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Summary The drive‐by bridge health monitoring is to assess the bridge condition using the acceleration responses measured on the body or axle of instrumented vehicles. The vehicle responses are greatly affected by the road surface roughness that makes the bridge dynamic information blurred. Instead of direct using vehicle responses for the bridge monitoring, the dynamic response of contact point (CP) between the vehicle and bridge is further explored to enhance the drive‐by bridge modal identification. A novel three‐step framework is proposed to extract the components related to the bridge vibration from vehicle responses. The first step is to identify the input forces of two successive vehicles by solving the combined state‐input estimation problem using dual Kalman filter. The CP responses of two contact points are calculated using the input forces and vehicle parameters. In the second step, the subtraction technique is applied to the identified CP responses of the two instrumented vehicles and the effect of the road surface roughness can be significantly reduced. Finally, an automatic singular spectrum analysis technique (auto SSA) is incorporated to decompose the response residual. Then the mono‐component modes related to the bridge response are extracted from the response residual for drive‐by bridge modal identification and/or the nonstationary characteristic identification of vehicle–bridge interaction (VBI) system. Results of numerical and experimental study demonstrate that the method can significantly suppress the vehicle response component and reduce the effect of road surface roughness to enhance the bridge modal identification.

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“…In the right‐hand side of Equation 8,

F_{cp} ((), t)

d_{cp} ((), t) goodbreak= F_{cp} ((), t) / k_{t}

The input force of the vehicle system is identified based on a state space representation of Equation 8 using Newmark‐β‐based DKF.…”

Section: Preliminaries For the Proposed Methodsmentioning

confidence: 99%

“…The model vehicle was pulled along the beam with an electric motor. The detailed description of the tests can be found in Li et al 26 …”

Section: Experimental Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhanced drive‐by bridge modal identification via dual Kalman filter and singular spectrum analysis

Zhu

Guo

2022

Structural Contr & Hlth

Self Cite

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show abstract

“…The damage indicators based on modal parameters and their derivatives need accurate modal identification from the original vibration signal and the accuracy is compromised by the accidental error of measurement and/or analysis. The popular Kalman filters can effectively eliminate the interference of noise [35]; however, the structural parameter identification method based on eigen perturbation and a Kalman filter still faces many challenges: (1) it can only be used to identify time-invariant structural parameters [36]; (2) for sub-component (location) damage detection of a structure, the accuracy and robustness need to be further improved [37];…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor and Decision-Level Fusion-Based Structural Damage Detection Using a One-Dimensional Convolutional Neural Network

Chen

Liu

et al. 2021

Sensors

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This paper presents a novel approach to substantially improve the detection accuracy of structural damage via a one-dimensional convolutional neural network (1-D CNN) and a decision-level fusion strategy. As structural damage usually induces changes in the dynamic responses of a structure, a CNN can effectively extract structural damage information from the vibration signals and classify them into the corresponding damage categories. However, it is difficult to build a large-scale sensor system in practical engineering; the collected vibration signals are usually non-synchronous and contain incomplete structure information, resulting in some evident errors in the decision stage of the CNN. In this study, the acceleration signals of multiple acquisition points were obtained, and the signals of each acquisition point were used to train a 1-D CNN, and their performances were evaluated by using the corresponding testing samples. Subsequently, the prediction results of all CNNs were fused (decision-level fusion) to obtain the integrated detection results. This method was validated using both numerical and experimental models and compared with a control experiment (data-level fusion) in which all the acceleration signals were used to train a CNN. The results confirmed that: by fusing the prediction results of multiple CNN models, the detection accuracy was significantly improved; for the numerical and experimental models, the detection accuracy was 10% and 16–30%, respectively, higher than that of the control experiment. It was demonstrated that: training a CNN using the acceleration signals of each acquisition point and making its own decision (the CNN output) and then fusing these decisions could effectively improve the accuracy of damage detection of the CNN.

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“…• 17 on numerical analyses [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76]; • five on experimental analyses: four scaled [77][78][79][80] and one full-scale [81]; • 12 on both numerical and experimental analyses: nine with scaled experiments [82][83][84][85][86][87][88][89][90] and three with full-scale [91][92][93].…”

mentioning

confidence: 99%

The Way Forward for Indirect Structural Health Monitoring (iSHM) Using Connected and Automated Vehicles in Europe

et al. 2021

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Europe’s aging transportation infrastructure requires optimized maintenance programs. However, data and monitoring systems may not be readily available to support strategic decisions or they may require costly installations in terms of time and labor requirements. In recent years, the possibility of monitoring bridges by indirectly sensing relevant parameters from traveling vehicles has emerged—an approach that would allow for the elimination of the costly installation of sensors and monitoring campaigns. The advantages of cooperative, connected, and automated mobility (CCAM), which is expected to become a reality in Europe towards the end of this decade, should therefore be considered for the future development of iSHM strategies. A critical review of methods and strategies for CCAM, including Intelligent Transportation Systems, is a prerequisite for moving towards the goal of identifying the synergies between CCAM and civil infrastructures, in line with future developments in vehicle automation. This study presents the policy framework of CCAM in Europe and discusses the policy enablers and bottlenecks of using CCAM in the drive-by monitoring of transport infrastructure. It also highlights the current direction of research within the iSHM paradigm towards the identification of technologies and methods that could benefit from the use of connected and automated vehicles (CAVs).

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A Two-Step Drive-By Bridge Damage Detection Using Dual Kalman Filter

Cited by 44 publications

References 32 publications

Enhanced drive‐by bridge modal identification via dual Kalman filter and singular spectrum analysis

Enhanced drive‐by bridge modal identification via dual Kalman filter and singular spectrum analysis

Multi-Sensor and Decision-Level Fusion-Based Structural Damage Detection Using a One-Dimensional Convolutional Neural Network

The Way Forward for Indirect Structural Health Monitoring (iSHM) Using Connected and Automated Vehicles in Europe

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