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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

Some yield function curves by the generalized form of the yield function
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Fig3: Some yield function curves by the generalized form of the yield function

Mentions: Because the Eq. (10) is the generalized form of the yield functions, with the different parameters, mainly α and K, the different yield functions can be obtained (Zheng et al. 2002). These yield functions include almost all widely used yield functions, such as Mohr–Coulomb (M–C) yield criterion, Mises yield criterion, two improved Mises yield criterions (exterior angle circle and interior angle circle), Drucker–Prager (D–P) yield criterion, Tresca yield criterion, three Zienkiewicz–Pande (Z–P) yield criterions (elliptic curve, hyperbolic curve and parabolic curve) and twin-shear yield criterion, etc. The detailed specific relationship between different parameters and different yield functions can be found in reference (Zheng et al. 2002). The curves of the Eq. (10) for some generally used yield functions are shown in Fig. 3.Fig. 3


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

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

Some yield function curves by the generalized form of the yield function
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: Some yield function curves by the generalized form of the yield function
Mentions: Because the Eq. (10) is the generalized form of the yield functions, with the different parameters, mainly α and K, the different yield functions can be obtained (Zheng et al. 2002). These yield functions include almost all widely used yield functions, such as Mohr–Coulomb (M–C) yield criterion, Mises yield criterion, two improved Mises yield criterions (exterior angle circle and interior angle circle), Drucker–Prager (D–P) yield criterion, Tresca yield criterion, three Zienkiewicz–Pande (Z–P) yield criterions (elliptic curve, hyperbolic curve and parabolic curve) and twin-shear yield criterion, etc. The detailed specific relationship between different parameters and different yield functions can be found in reference (Zheng et al. 2002). The curves of the Eq. (10) for some generally used yield functions are shown in Fig. 3.Fig. 3

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