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.
Highlights
The back analysis method is an important method in underground engineering
Back analysis can be divided into two types of analyses: parameter identification and model identification (Gao and Liu 2009)
Parameter identification based on measured displacements has been the most common back analysis method for underground engineering (Feng et al 2000; Maier and Gioda 1982; Rechea et al 2008; Sharifzadeh et al 2013; Yazdani et al 2012)
Summary
The back analysis method is an important method in underground engineering Since it was proposed in the 1970s, numerous studies have been performed (Gao and Liu 2009; Wang and Li 1993; Sakurai and Takeuchi 1983). The structure of the constitutive model determined by the prior knowledge is generally more complex than the assumed simple model used in parameter identification. Based on the displacement measurements of an underground roadway, Liu (2011) identified the visco-elastic constitutive model of rock mass based on the traditional nonlinear optimum technique. Yang and Wang (2009) presented a numerical model to identify the unknown equivalent constitutive model in the elastic layered rock mass of an underground opening by the Gauss–Newton technique. 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
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