A hybrid computational strategy for identification of structural parameters

Koh, C. G.; Chen, Y.F.; Liaw, C.Y.

doi:10.1016/s0045-7949(02)00344-9

Cited by 93 publications

(36 citation statements)

References 27 publications

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“…Koh et al [16][17][18] developed several GA-based divide-and-conquer identification methods. Hybrid strategies such as that by Koh et al [19] include a local search algorithm to refine the solutions found by the standard GA. Luh and Wu [20] applied a GA-based scheme to identify the parameters of a non-linear autoregressive with exogenous inputs system and applied the technique to a coupled liquid-level system and an engine-testing cycle. Hao and Xia [21] showed that the GA approach is able to identify damaged elements even with an imperfect analytical model.…”

Section: Introductionmentioning

confidence: 99%

Output‐only structural identification in time domain: Numerical and experimental studies

Perry

Koh

2008

Earthq Engng Struct Dyn

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SUMMARYBy identifying changes in stiffness parameters, structural damage can be detected and monitored. Although considerable progress has been made in this research area, many challenges remain in achieving robust structural identification based on incomplete and noisy measurement signals. The identification task is made even more difficult if measurement of input force is to be eliminated. To this end, an output-only structural identification strategy is proposed to identify unknown stiffness and damping parameters. A nonclassical approach based on genetic algorithms (GAs) is adopted. The proposed strategy makes use of the recently developed GA-based method of search space reduction, which has shown to be able to accurately and reliably identify structural parameters from measured input and output signals. By modifying the numerical integration scheme, input can be computed as the parameter identification task is in progress, thereby eliminating the need to measure forces. Numerical and experimental results demonstrate the power of the strategy in accurate and efficient identification of structural parameters and damage using only incomplete acceleration measurements.

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Section: Introductionmentioning

confidence: 99%

Output‐only structural identification in time domain: Numerical and experimental studies

Perry

Koh

2008

Earthq Engng Struct Dyn

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“…In these methods, the model of the engineering system is obtained based on the physical principles or using systems identification techniques (Kozin and Natke 1986;Koh et al 2003). Many model-based FDI methods have been developed during last four decades, e.g.…”

Section: Condition Monitoringmentioning

confidence: 99%

Computational monitoring in real time: review of methods and applications

Dyskin

Başarır

Doherty

et al. 2018

Geomech. Geophys. Geo-energ. Geo-resour.

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Continuous monitoring of mechanical parameters determining the state of natural and manmade systems is essential in a wide range of engineering disciplines from mechanical to civil and geotechnical engineering. To be effective, the monitoring response time needs to be commensurate with the characteristic time of variation of the processes being monitored e.g. from seconds as for example in some machinery, to weeks and months for mining excavations and years in the case of structures. The methods of measurement can therefore differ significantly for different applications. In spite of this, the methods of information processing-the computational monitoring-have considerable commonality. This review focuses on the methods of computational monitoring in solid and structural mechanics problems in different engineering applications. The traditional methods of monitoring are reviewed along with the corresponding computational and measurement methods. The basic principles of the computational monitoring in real time are established.

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“…It is often possible to reduce this value by using incorrect values of model parameters for example if (e i,3b ) is opposite in sign to the resultant of other terms in Equation (4). Thus, the location of the global minimum of the distance function is not close to the correct values.…”

Section: Reliability Of System Identificationmentioning

confidence: 99%

“…• p 3 Axial stiffness of the spring at the mid-span (kN/m) • p 4 Rotational stiffness of the spring at the mid-span (kNm)…”

Section: Experiments 2: Feature Selection Using Data Mining Techniquesmentioning

confidence: 99%

Data mining techniques for improving the reliability of system identification

Saitta

Raphael

Smith

2005

Advanced Engineering Informatics

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Abstract:A system identification methodology that makes use of data mining techniques to improve the reliability of identification is presented in this paper. An important aspect of the methodology is the generation of a population of candidate models. Indications of the reliability of system identification are obtained through an examination of the characteristics of the population. Data mining techniques bring out model characteristics that are important.

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A hybrid computational strategy for identification of structural parameters

Cited by 93 publications

References 27 publications

Output‐only structural identification in time domain: Numerical and experimental studies

Output‐only structural identification in time domain: Numerical and experimental studies

Computational monitoring in real time: review of methods and applications

Data mining techniques for improving the reliability of system identification

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