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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.


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Flow chart of immunized genetic algorithm
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Fig4: Flow chart of immunized genetic algorithm

Mentions: To improve the main operations of genetic algorithm, such as creation of initial population, reproduction operation, crossover operation, mutation operation and selection operation, one new immunized genetic algorithm has been proposed by author, whose flow chart is as shown in Fig. 4. The details of the immunized genetic algorithm can be found in reference (Gao 2012).Fig. 4


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

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

Flow chart of immunized genetic algorithm
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: Flow chart of immunized genetic algorithm
Mentions: To improve the main operations of genetic algorithm, such as creation of initial population, reproduction operation, crossover operation, mutation operation and selection operation, one new immunized genetic algorithm has been proposed by author, whose flow chart is as shown in Fig. 4. The details of the immunized genetic algorithm can be found in reference (Gao 2012).Fig. 4

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