• Structural Engineering and Mechanics
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• 제21권3호
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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.

• Structural Engineering and Mechanics
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• 제29권2호
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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.

• Structural Engineering and Mechanics
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• 제53권2호
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• pp.261-276
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• 2015

Structural damage and moving load identification are the two aspects of structural system identification. However, they universally coexist in the damaged structures subject to unknown moving load. This paper proposed a dynamic response sensitivity-based model updating method to simultaneously identify the structural damage and moving force. The moving force which is equivalent as the nodal force of the structure can be expressed as a series of orthogonal polynomial. Based on the system Markov parameters by the state space method, the dynamic response and the dynamic response derivatives with respect to the force parameters and elemental variations are analytically derived. Afterwards, the damage and force parameters are obtained by minimizing the difference between measured and analytical response in the sensitivity-based updating procedure. A numerical example for a simply supported beam under the moving load is employed to verify the accuracy of the proposed method.

• Structural Engineering and Mechanics
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• 제14권2호
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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.

• Smart Structures and Systems
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• 제17권6호
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• pp.917-933
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• 2016

In a prestressed concrete bridge, the magnitude of the prestress force (PF) decreases with time. This unexpected loss can cause failure of a bridge which makes prestress force identification (PFI) critical to evaluate bridge safety. However, it has been difficult to identify the PF non-destructively. Although some research has shown the feasibility of vibration based methods in PFI, the requirement of having a determinate exciting force in these methods hinders applications onto in-service bridges. Ideally, it will be efficient if the normal traffic could be treated as an excitation, but the load caused by vehicles is difficult to measure. Hence it prompts the need to investigate whether PF and moving load could be identified together. This paper presents a synergic identification method to determine PF and moving load applied on a simply supported prestressed concrete beam via the dynamic responses caused by this unknown moving load. This method consists of three parts: (i) the PF is transformed into an external pseudo-load localized in each beam element via virtual distortion method (VDM); (ii) then these pseudo-loads are identified simultaneously with the moving load via Duhamel Integral; (iii) the time consuming problem during the inversion of Duhamel Integral is overcome by the load-shape function (LSF). The method is examined against different cases of PFs, vehicle speeds and noise levels by means of simulations. Results show that this method attains a good degree of accuracy and efficiency, as well as robustness to noise.

• Structural Engineering and Mechanics
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• 제29권2호
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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.

• 한국구조물진단유지관리공학회 논문집
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• 제10권6호
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• pp.87-94
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• 2006

교량의 설계 및 시공에 있어 적절한 설계하중을 산정하기 위해 차량의 하중을 측정하는 것은 매우 중요하다. 본 논문에서는 이동하중에 대해 다축의 모멘트 영향선을 이용하여 시간에 따라 하중을 식별하는 알고리듬을 제안하였다. 또한 2개 이상의 하중을 식별하는 경우, 시간에 따른 하중 식별 결과가 심한 진동을 하기 때문에 밀도추정함수를 통해 최종 식별하중을 구하는 방법을 제안하였다. 단경간 판형교에 대한 수치예제를 수행하여 제안한 알고리듬 및 방법들을 검증하였다. 수치예제에서는 계측오차와 속도오차에 대한 오차분석을 수행하였다. 또한 제안된 알고리듬을 6m 길이의 강재 모형교량을 이용한 실내실험을 통해 재검증하였다. 속도와 하중의 종류에 따라 하중식별 능력이 달랐으나 개발한 알고리듬이 최대 10% 수준의 오차 내에서 하중을 식별할 수 있음을 확인하였다.

• Computers and Concrete
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• 제20권5호
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• pp.617-625
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• 2017

Prestress force identification (PFI) is crucial to maintain the safety of prestressed concrete bridges. A synergic identification method has been proposed recently by the authors that can determine the prestress force (PF) and the excitation force simultaneously in prestressed concrete beams with good accuracy. In this paper, the ability of this method in the application with prestressed concrete box-girder bridges is demonstrated. A reasonable assumption is made to capture the similarity of the dynamic behavior of the prestressed concrete box-girder bridge and a beam under a certain loading scenario, and the feasibility of this method for application in a prestressed box-girder bridge is affirmed. A comprehensive laboratory test program is conducted, and the effects of PF, excitation, measuring time and uncertainties are studied. Results show that the proposed method can predict the PF and the excitation force in a prestressed concrete box-girder accurately and has a great robustness against uncertainties.

• Smart Structures and Systems
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• 제13권5호
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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.

• Smart Structures and Systems
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• 제23권3호
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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.