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Elastic-plastic model identification for rock surrounding an underground excavation based on immunized genetic algorithm.

Gao W, Chen D, Wang X - Springerplus (2016)

Bottom Line: Many constitutive models for rock mass have been proposed.In this model identification study, a generalized constitutive law for an elastic-plastic constitutive model is applied.Therefore, the entire computation efficiency of model identification will be improved.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, College of Civil and Transportation Engineering, Hohai University, 1 Xikang Road, Nanjing, 210098 China.

ABSTRACT
To compute the stability of underground engineering, a constitutive model of surrounding rock must be identified. Many constitutive models for rock mass have been proposed. In this model identification study, a generalized constitutive law for an elastic-plastic constitutive model is applied. Using the generalized constitutive law, the problem of model identification is transformed to a problem of parameter identification, which is a typical and complicated optimization. To improve the efficiency of the traditional optimization method, an immunized genetic algorithm that is proposed by the author is applied in this study. In this new algorithm, the principle of artificial immune algorithm is combined with the genetic algorithm. Therefore, the entire computation efficiency of model identification will be improved. Using this new model identification method, a numerical example and an engineering example are used to verify the computing ability of the algorithm. The results show that this new model identification algorithm can significantly improve the computation efficiency and the computation effect.

No MeSH data available.


Related in: MedlinePlus

Comparison of NOFs for GA, FGA and IGA
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Fig6: Comparison of NOFs for GA, FGA and IGA

Mentions: To simply evaluate the computational efficiency of the GA, FGA and IGA, the number of objective function evaluations during the search for the optimum, which is denoted by NOF and represents the computation time required by the optimization algorithm, is applied. The NOFs of the GA, FGA and IGA methods are given in Fig. 6.Fig. 6


Elastic-plastic model identification for rock surrounding an underground excavation based on immunized genetic algorithm.

Gao W, Chen D, Wang X - Springerplus (2016)

Comparison of NOFs for GA, FGA and IGA
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4940315&req=5

Fig6: Comparison of NOFs for GA, FGA and IGA
Mentions: To simply evaluate the computational efficiency of the GA, FGA and IGA, the number of objective function evaluations during the search for the optimum, which is denoted by NOF and represents the computation time required by the optimization algorithm, is applied. The NOFs of the GA, FGA and IGA methods are given in Fig. 6.Fig. 6

Bottom Line: Many constitutive models for rock mass have been proposed.In this model identification study, a generalized constitutive law for an elastic-plastic constitutive model is applied.Therefore, the entire computation efficiency of model identification will be improved.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, College of Civil and Transportation Engineering, Hohai University, 1 Xikang Road, Nanjing, 210098 China.

ABSTRACT
To compute the stability of underground engineering, a constitutive model of surrounding rock must be identified. Many constitutive models for rock mass have been proposed. In this model identification study, a generalized constitutive law for an elastic-plastic constitutive model is applied. Using the generalized constitutive law, the problem of model identification is transformed to a problem of parameter identification, which is a typical and complicated optimization. To improve the efficiency of the traditional optimization method, an immunized genetic algorithm that is proposed by the author is applied in this study. In this new algorithm, the principle of artificial immune algorithm is combined with the genetic algorithm. Therefore, the entire computation efficiency of model identification will be improved. Using this new model identification method, a numerical example and an engineering example are used to verify the computing ability of the algorithm. The results show that this new model identification algorithm can significantly improve the computation efficiency and the computation effect.

No MeSH data available.


Related in: MedlinePlus