Identification of Running Load and Beam System

Hoshiya, Masaru; Maruyama, Osamu

doi:10.1061/(asce)0733-9399(1987)113:6(813)

Cited by 44 publications

(11 citation statements)

References 8 publications

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“…To meet the objectives of the study, the solution of the inverse problem using a time domain system identification technique is more appropriate (Hoshiya and Maruyama, 1987). Koh et al (1991Koh et al ( ,1995 were the first to formulate a sub-structural identification method to tackle the numerical problems associated with identifying large structural systems in time domain.…”

Section: Introductionmentioning

confidence: 99%

Defect identification under uncertain blast loading

Katkhuda

Haldar

2006

Optim Eng

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A novel finite element-based system identification procedure is proposed to detect defects in existing frame structures when excited by blast loadings. The procedure is a linear time-domain system identification technique where the structure is represented by finite elements and the input excitation is not required to identify the structure. It identifies stiffness parameter (EI/L) of all the elements and tracks the changes in them to locate the defect spots. The similar procedure can also be used to monitor health of structures just after natural events like strong earthquakes and high winds. With the help of several numerical examples, it is shown that the algorithm can identify defect-free and defective structures even in the presence of noise in the output response information. The accuracy of the method is much better than other methods currently available even when input excitation information was used for identification purpose. The method not only detects defective elements but also locate the defect spot more accurately within the defective element. The structures can be excited by single or multiple blast loadings and the defect can be relatively small and large. With the help of several examples, it is established that the proposed method can be used as a nondestructive defect evaluation procedure for the health assessment of existing structures.

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Section: Introductionmentioning

confidence: 99%

Defect identification under uncertain blast loading

Katkhuda

Haldar

2006

Optim Eng

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“…The Kalman filter, which incorporates both model and measurement uncertainties to achieve an optimal estimate of state variables with minimum error covariance matrix, is another possible choice to address the problem of simultaneous identification of system and excitation characteristics in some particular cases. For example, Hoshiya and Sutoh [7] developed a procedure to evaluate the parameters of a simply supported beam and characteristics of the running load on it based on the extended Kalman filter with a weighted global iteration (KF-WGI). In 1994, Benedetii and Gentile [8] presented a two-phase frequency domain method for structures subjected to ground motion, where the responses measurements at two locations of the system are assumed to be available and requirements for input information is elaborately avoided by using the ratio of the amplitude of Fourier transforms of the two recorded signals.…”

Section: Introductionmentioning

confidence: 99%

A statistical average algorithm for the dynamic compound inverse problem

Li¹,

Chen²

2003

Computational Mechanics

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A new method named as Statistical Average Algorithm is developed for solving the structural dynamic compound inverse problem, which means to identify structural parameters with unknown input or to inverse input time history with unknown structural parameters. By taking the mechanical characteristic of the ground motion as an additional condition, an iterative algorithm based on the least-squares technique is developed so that the input process and structural parameters can be correctly determined using only output measurements. The procedure of the proposed method is discussed in detail, and a mathematical proof is presented as well in the appendix. A practical input situation such as earthquake, ambient vibration is considered in the numerical examples to verify the accuracy, reliability and robustness of the proposed algorithm. Considering the background of the practical application, both of the noise-free and noise-included output responses are considered in the numerical examples. In all cases, the proposed method identifies the structural parameters and reconstructs the input process rationally.

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“…However, these techniques measure only static axle loads. Hoshiya and Maruyama [7] suggested an advanced method for identi"cation of a moving load running on beams. O'Connor and Chan [8] suggested another advanced force identi"cation method*the interpretative method I (IMI), which is able to measure both dynamic and static axle forces of multi-axle systems.…”

Section: Introductionmentioning

confidence: 99%