Limits...
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

Optimization objective function of model identification for underground engineering
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4940315&req=5

Fig1: Optimization objective function of model identification for underground engineering

Mentions: Because model identification is a very complicated optimization problem (Gao and Liu 2009), whose objective function can be shown as in Fig. 1, traditional optimization techniques have some shortcomings. The relationship between objective function and the optimization method can be described as in Fig. 2 (Gao and Liu 2009). From Fig. 2, the global optimization is one suitable method to solve model identification problem. Therefore, some important contributions to global optimization algorithms have been achieved. Su et al. (2008) identified the structure and parameters of the rheological constitutive model of the surrounding rocks of the Jinping tunnels in China using a differential evolution algorithm. Feng et al. (2006) identified a visco-elastic model of surrounding rocks in the Goupitan hydroelectric power station in China using genetic programming and a modified particle swarm optimization algorithm. Meier et al. (2007) have performed model identification of surrounding rocks for tunneling by particle swarm optimization. Sha et al. (2011) presented three methods to identify the constitutive model of rocks based on a genetic algorithm, a back propagation neural network and genetic programming and compared all methods. Surajit and Wathugala (1996) calibrated a constitutive model using genetic algorithms. Gao (2007) proposed a method to identify the rheological constitutive model for rock mass of an underground roadway based on the fast-convergent genetic algorithm. Moreover, Gao et al. (2004) presented an identification method for the surrounding rock of an underground power house based on a new intelligent bionics algorithm. Because these global optimization algorithms comprise random search algorithms, their computational efficiency is very low. This low efficiency is problematic when the model identification is extremely complicated. In this study, a new method to identify the constitutive model based on an immunized genetic algorithm is proposed. The elastic–plastic constitutive model is the main model of geo-materials which has been comprehensively evaluated (Nakai 2012; Zheng et al. 2002). In the last years, many studies (Cui et al. 2015; Lee and Pietruszczak 2008; Spiezia et al. 2016; Zhang et al. 2012) have been carried out investigating the elasto-plastic behavior of rock surrounding underground excavations. And those researches have proved that the elastic–plastic constitutive model can describe the mechanical behaviour of rock surrounding underground excavation very well. Therefore, the elastic–plastic constitutive model is analyzed in this study.Fig. 1


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

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

Optimization objective function of model identification for underground engineering
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: Optimization objective function of model identification for underground engineering
Mentions: Because model identification is a very complicated optimization problem (Gao and Liu 2009), whose objective function can be shown as in Fig. 1, traditional optimization techniques have some shortcomings. The relationship between objective function and the optimization method can be described as in Fig. 2 (Gao and Liu 2009). From Fig. 2, the global optimization is one suitable method to solve model identification problem. Therefore, some important contributions to global optimization algorithms have been achieved. Su et al. (2008) identified the structure and parameters of the rheological constitutive model of the surrounding rocks of the Jinping tunnels in China using a differential evolution algorithm. Feng et al. (2006) identified a visco-elastic model of surrounding rocks in the Goupitan hydroelectric power station in China using genetic programming and a modified particle swarm optimization algorithm. Meier et al. (2007) have performed model identification of surrounding rocks for tunneling by particle swarm optimization. Sha et al. (2011) presented three methods to identify the constitutive model of rocks based on a genetic algorithm, a back propagation neural network and genetic programming and compared all methods. Surajit and Wathugala (1996) calibrated a constitutive model using genetic algorithms. Gao (2007) proposed a method to identify the rheological constitutive model for rock mass of an underground roadway based on the fast-convergent genetic algorithm. Moreover, Gao et al. (2004) presented an identification method for the surrounding rock of an underground power house based on a new intelligent bionics algorithm. Because these global optimization algorithms comprise random search algorithms, their computational efficiency is very low. This low efficiency is problematic when the model identification is extremely complicated. In this study, a new method to identify the constitutive model based on an immunized genetic algorithm is proposed. The elastic–plastic constitutive model is the main model of geo-materials which has been comprehensively evaluated (Nakai 2012; Zheng et al. 2002). In the last years, many studies (Cui et al. 2015; Lee and Pietruszczak 2008; Spiezia et al. 2016; Zhang et al. 2012) have been carried out investigating the elasto-plastic behavior of rock surrounding underground excavations. And those researches have proved that the elastic–plastic constitutive model can describe the mechanical behaviour of rock surrounding underground excavation very well. Therefore, the elastic–plastic constitutive model is analyzed in this study.Fig. 1

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