• Title/Summary/Keyword: moving force identification

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Moving force identification from bridge dynamic responses

  • Yu, Ling;Chan, Tommy H.T.
    • Structural Engineering and Mechanics
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    • v.21 no.3
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    • pp.369-374
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    • 2005
  • A big progress has been made for moving force identification from bridge dynamic responses in recent years. Current knowledge and the potentials on moving force identification methods are reviewed in this paper under main headings below: background of moving force identification, experimental verification in laboratory and its application in field.

A MOM-based algorithm for moving force identification: Part II - Experiment and comparative studies

  • Yu, Ling;Chan, Tommy H.T.;Zhu, Jun-Hua
    • Structural Engineering and Mechanics
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    • v.29 no.2
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    • pp.155-169
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    • 2008
  • A MOM-based algorithm (MOMA) has been developed for moving force identification from dynamic responses of bridge in the companion paper. This paper further evaluates and investigates the properties of the developed MOMA by experiment in laboratory. A simply supported bridge model and a few vehicle models were designed and constructed in laboratory. A series of experiments have then been conducted for moving force identification. The bending moment and acceleration responses at several measurement stations of the bridge model are simultaneously measured when the model vehicle moves across the bridge deck at different speeds. In order to compare with the existing time domain method (TDM), the best method for moving force identification to date, a carefully comparative study scheme was planned and conducted, which includes considering the effect of a few main parameters, such as basis function terms, mode number involved in the identification calculation, measurement stations, executive CPU time, Nyquist fraction of digital filter, and two different solutions to the ill-posed system equation of moving force identification. It was observed that the MOMA has many good properties same as the TDM, but its CPU execution time is just less than one tenth of the TDM, which indicates an achievement in which the MOMA can be used directly for real-time analysis of moving force identification in field.

Moving force identification from bending moment responses of bridge

  • Yu, Ling;Chan, Tommy H.T.
    • Structural Engineering and Mechanics
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    • v.14 no.2
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    • pp.151-170
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    • 2002
  • Moving force identification is a very important inverse problem in structural dynamics. Most of the identification methods are eventually converted to a linear algebraic equation set. Different ways to solve the equation set may lead to solutions with completely different levels of accuracy. Based on the measured bending moment responses of the bridge made in laboratory, this paper presented the time domain method (TDM) and frequency-time domain method (FTDM) for identifying the two moving wheel loads of a vehicle moving across a bridge. Directly calculating pseudo-inverse (PI) matrix and using the singular value decomposition (SVD) technique are adopted as means for solving the over-determined system equation in the TDM and FTDM. The effects of bridge and vehicle parameters on the TDM and FTDM are also investigated. Assessment results show that the SVD technique can effectively improve identification accuracy when using the TDM and FTDM, particularly in the case of the FTDM. This improved accuracy makes the TDM and FTDM more feasible and acceptable as methods for moving force identification.

A drive-by inspection system via vehicle moving force identification

  • OBrien, E.J.;McGetrick, P.J.;Gonzalez, A.
    • Smart Structures and Systems
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    • v.13 no.5
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    • pp.821-848
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    • 2014
  • This paper presents a novel method to carry out monitoring of transport infrastructure such as pavements and bridges through the analysis of vehicle accelerations. An algorithm is developed for the identification of dynamic vehicle-bridge interaction forces using the vehicle response. Moving force identification theory is applied to a vehicle model in order to identify these dynamic forces between the vehicle and the road and/or bridge. A coupled half-car vehicle-bridge interaction model is used in theoretical simulations to test the effectiveness of the approach in identifying the forces. The potential of the method to identify the global bending stiffness of the bridge and to predict the pavement roughness is presented. The method is tested for a range of bridge spans using theoretical simulations and the influences of road roughness and signal noise on the accuracy of the results are investigated.

A MOM-based algorithm for moving force identification: Part I - Theory and numerical simulation

  • Yu, Ling;Chan, Tommy H.T.;Zhu, Jun-Hua
    • Structural Engineering and Mechanics
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    • v.29 no.2
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    • pp.135-154
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    • 2008
  • The moving vehicle loads on a bridge deck is one of the most important live loads of bridges. They should be understood, monitored and controlled before the bridge design as well as when the bridge is open for traffic. A MOM-based algorithm (MOMA) is proposed for identifying the timevarying moving vehicle loads from the responses of bridge deck in this paper. It aims at an acceptable solution to the ill-conditioning problem that often exists in the inverse problem of moving force identification. The moving vehicle loads are described as a combination of whole basis functions, such as orthogonal Legendre polynomials or Fourier series, and further estimated by solving the new system equations developed with the basis functions. A number of responses have been combined, some numerical simulations on single axle, two axle and multiple-axle loads, being either constant or timevarying, have been carried out and compared with the existing time domain method (TDM) in this paper. The illustrated results show that the MOMA has higher identification accuracy and robust noise immunity as well as producing an acceptable solution to ill-conditioning cases to some extent when it is used to identify the moving force from bridge responses.

Development of Moving Force Identification Algorithm Using Moment Influence Lines at Multiple-Axes and Density Estimation Function (다축모멘트 영향선과 밀도추정함수를 사용한 이동하중식별 알고리듬의 개발)

  • Jeong, Ji-Weon;Shin, Soobong
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.10 no.6
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    • pp.87-94
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    • 2006
  • Estimating moving vehicle loads is important in modeling design loads for bridge design and construction. The paper proposes a moving force identification algorithm using moment influence lines measured at multi-axes. Density estimation function was applied to estimate more than two wheel loads when estimated load values fluctuated severely. The algorithm has been examined through simulation studies on a simple-span plate-girder bridge. Influences of measurement noise and error in velocity on the identification results were investigated in the simulation study. Also, laboratory experiments were carried out to examine the algorithm. The load identification capability was dependent on the type and speed of moving loads, but the developed algorithm could identify loads within 10% error in maximum.

Bridge weigh-in-motion through bidirectional Recurrent Neural Network with long short-term memory and attention mechanism

  • Wang, Zhichao;Wang, Yang
    • Smart Structures and Systems
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    • v.27 no.2
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    • pp.241-256
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    • 2021
  • In bridge weigh-in-motion (BWIM), dynamic bridge response is measured during traffic and used to identify overloaded vehicles. Most past studies of BWIM use mechanics-based algorithms to estimate axle weights. This research instead investigates deep learning, specifically the recurrent neural network (RNN), toward BWIM. In order to acquire the large data volume to train a RNN network that uses bridge response to estimate axle weights, a finite element bridge model is built through the commercial software package LS-DYNA. To mimic everyday traffic scenarios, tens of thousands of randomized vehicle formations are simulated, with different combinations of vehicle types, spacings, speeds, axle weights, axle distances, etc. Dynamic response from each of the randomized traffic scenarios is recorded for training the RNN. In this paper we propose a 3-stage Bidirectional RNN toward BWIM. Long short-term memory (LSTM) and attention mechanism are embedded in the BRNN to further improve the network performance. Additional test data indicates that the BRNN network achieves high accuracy in estimating axle weights, in comparison with a conventional moving force identification (MFI) method.

The Sensitivity Analysis and Safety Evaluations of Cable Stayed Bridges Based on Probabilistic Finite Element Method (확률유한요소해석에 의한 사장교의 민감도 분석 및 안전성 평가)

  • Han, Sung-Ho;Cho, Tae-Jun;Bang, Myung-Seok
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.11 no.1
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    • pp.141-152
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    • 2007
  • Considering uncertainties of random input data, it is more reasonable to use probabilistic method than the conventional deterministic method for the design of structures or for the assessment of the responses of structures, which are designed as safe even under extreme loads. Therefore, to assess the quantitative effects of the constructed cable stayed bridge by the input random variables, a sensitivity analysis is studied. Using perturbation method, an analysis program is developed for the iterative probabilistic finite element analyses and sensitivity analyses of the cable stayed bridge, except the initial shape analysis. Monte-Carlo Simulations were used for the verification of the developed program. The results of sensitivity analysis shows the governing effects of external loads. Because the results also provide the sensitive effects of the stiffness of members and the magnitudes of prestressing force of cables, the developed

Identification of moving train loads on railway bridge based on strain monitoring

  • Wang, Hao;Zhu, Qingxin;Li, Jian;Mao, Jianxiao;Hu, Suoting;Zhao, Xinxin
    • Smart Structures and Systems
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    • v.23 no.3
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    • pp.263-278
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    • 2019
  • Moving train load parameters, including train speed, axle spacing, gross train weight and axle weights, are identified based on strain-monitoring data. In this paper, according to influence line theory, the classic moving force identification method is enhanced to handle time-varying velocity of the train. First, the moments that the axles move through a set of fixed points are identified from a series of pulses extracted from the second derivative of the structural strain response. Subsequently, the train speed and axle spacing are identified. In addition, based on the fact that the integral area of the structural strain response is a constant under a unit force at a unit speed, the gross train weight can be obtained from the integral area of the measured strain response. Meanwhile, the corrected second derivative peak values, in which the effect of time-varying velocity is eliminated, are selected to distribute the gross train weight. Hence the axle weights could be identified. Afterwards, numerical simulations are employed to verify the proposed method and investigate the effect of the sampling frequency on the identification accuracy. Eventually, the method is verified using the real-time strain data of a continuous steel truss railway bridge. Results show that train speed, axle spacing and gross train weight can be accurately identified in the time domain. However, only the approximate values of the axle weights could be obtained with the updated method. The identified results can provide reliable reference for determining fatigue deterioration and predicting the remaining service life of railway bridges.