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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 deep multi-point displacements of −780  floor haulage roadway in the Xieyi mine. a Top, b left side, c right side
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Fig11: Comparison of deep multi-point displacements of −780 floor haulage roadway in the Xieyi mine. a Top, b left side, c right side

Mentions: To verify the model identification results, the deep multi-point displacements can be computed by the FEM with the identified model. The results are shown in Fig. 11. For comparison, the stable displacement of each monitoring point is utilized.Fig. 11


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 deep multi-point displacements of −780  floor haulage roadway in the Xieyi mine. a Top, b left side, c right side
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig11: Comparison of deep multi-point displacements of −780 floor haulage roadway in the Xieyi mine. a Top, b left side, c right side
Mentions: To verify the model identification results, the deep multi-point displacements can be computed by the FEM with the identified model. The results are shown in Fig. 11. For comparison, the stable displacement of each monitoring point is utilized.Fig. 11

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