The Establishment of a Rock Creep Model by Using Creep Bodies via an Improved Gene Expression Programming Algorithm
Source abstract
Abstract
Rock creep commonly appears in rock mass engineering, and it should be paid appropriate attention. In this paper, studies on creep constitutive models of rocks were reviewed, and it was found that the types of creep constitutive models can generally be classified into two categories: theoretical formulas and combinations of creep bodies. Moreover, the combination of creep bodies has been used mainly for describing the creep characteristics of rocks; however, creep constitutive models have been constructed based on the subjectivity of studies in most cases, which is not objective or scientific enough. To avoid the subjectivity of establishing the creep constitutive model by using creep bodies, improved gene expression programming was utilized to construct the creep model. To verify the validity of the proposed method, two examples were given, and the corresponding creep constitutive models were obtained. The calculation results indicated that the proposed improved gene expression programming can be applied to establish a creep constitutive model in practice.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Dam Engineering and Safety · Soil and Unsaturated Flow · Reservoir Engineering and Simulation Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Urban Geological Survey and Monitoring Institute of Hunan Province, Changsha 410000, China
School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Funding
science and technology programme of the geological bureau of Hunan province · Department of Natural Resources of Hunan Province, China · Hunan Provincial Natural Science Foundation of China
References (42)
Show source-provided references
1. On the locked in stress, creep and dilatation of rocks and the constitutive equations
2. Modeling of creep in rock materials in terms of material degradation10.1016/S0266-352X(03)00063-6 ↗
3. Elastoplastic damage modelling of argillite in partially saturated condition and application10.1016/j.pce.2006.02.054 ↗
4. An elastoplastic model for unsaturated rocks and concrete10.1016/S0093-6413(02)00306-3 ↗
5. Time-dependent behaviour of deep level tabular excavations10.1007/s006030050028 ↗
6. Micromechanics-based prediction of creep failure of hard rock for long-term safety of high-level radioactive waste disposal system10.1016/S0167-6636(02)00286-7 ↗
7. Rheological Model of Hard Rock Pillar10.1007/BF01042859 ↗
8. The three processes of brittle cystalline rock creep10.1016/0148-9062(94)00048-8 ↗
9. Creep energy damage model of rock graded loading10.1016/j.rinp.2018.12.081 ↗
10. Visco-plastic rheological properties and a nonlinear creep model of soft rocks
11. A new model of shear creep and its experimental verification10.1007/s11043-020-09453-1 ↗
12. Unloading rheological test and model research of hard rock under complex conditions10.1155/2020/3576181 ↗
13. Study on nonlinear viscoelastoc-plastic creep model of deep soft rock
14. A rheological model of sandstones considering response to thermal treatment10.1155/2019/2143748 ↗
15. A damage-mechanism-based creep model considering temperature effect in granite10.1016/j.mechrescom.2013.11.009 ↗
16. Study on time-dependent behaviour of granite and the creep model based on fractional derivative approach considering temperature10.1155/2016/8572040 ↗
17. Creep experiment and rheological model of deep saturated rock10.1016/S1003-6326(13)62488-7 ↗
18. Wang, J.G., Sun, Q.L., Liang, B., Yang, P.J., and Yu, Q.R. (2020). Mudstone creep experiment and nonlinear damage model study under cyclic disturbance load. Sci. Rep., 10.10.1038/s41598-020-66245-w ↗
19. A creep damage constitutive model for a rock mass with nonpersistent joint under uniaxial compression10.1155/2019/4361458 ↗
20. Long-term strength determination and nonlinear creep damage constitutive model of salt rock based on multistage creep tests: Implications for underground natural gas storage in salt cavern10.1002/ese3.617 ↗
21. A modified creep model for cyclic characterization of rock salt considering the effects of the mean stress, half-amplitude and cycle period10.1007/s00603-020-02097-0 ↗
22. Gene expression programming: A new adaptive algorithm for solving problems
23. Roy, R., Koppen, M., Ovaska, S., Furuhashi, T., and Hoffmann, F. (2002). Gene expression programming in problem solving. Soft Computing and Industry-Recent Applications, Springer.10.1007/978-1-4471-0123-9 ↗
24. Genetic programming and gen expression programming for flyrock assessment due to mine blasting10.1016/j.ijrmms.2016.07.028 ↗
25. Steeb, W.H. (2011). The Nonlinear Workbook: Chaos, Fractals, Cellular Automata, Neural Networks, Genetic Algorithms, Gene Expression Programming, Support Vector Machine, Wavelets, Hidden Markov Models, Fuzzy Logic with C++, Java and Symbolic C++ Programs, World Scientific. [3rd ed.].10.1142/8050 ↗
26. Brownlee, J. (2011). Clever Algorithms: Nature-Inspired Programming Recipes, Lulu.
27. Function identification for the intrinsic strength and elastic properties of granitic rock via genetic programming (GP)10.1016/j.cageo.2010.09.002 ↗
28. Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press.
29. Prediction of compressive and tensile strength of limestone via genetic programming10.1016/j.eswa.2007.06.006 ↗
30. Cantu-Paz, E., Foster, J.A., and Deb, K. (2003). Dynamic maximum tree depth—A simple technique for avoiding bloat in tree-based GP. Genetic and Evolutionary Computation—GECCO 2003: Genetic and Evolutionary Computation Conference Chicago, IL, USA, 12–16 July 2003 Proceedings, Part II, Springer.
31. Study on 3D nonlinear visco-elastic-plastic creep constitutive model with parameter unsteady of soft rock based on improved Bingham model
32. The coefficient of determination R-2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded10.1098/rsif.2017.0213 ↗
33. High energy physics event selection with gene expression programming10.1016/j.cpc.2007.10.003 ↗
34. A new approach for predicting and collaborative evaluating the cutting force in face milling based on gene expression programming10.1016/j.jnca.2013.02.004 ↗
35. Soil liquefaction modeling by genetic expression programming and neuro-fuzzy10.1016/j.eswa.2010.09.071 ↗
36. Empirical modeling of plate load test moduli of soil via gene expression programming10.1016/j.compgeo.2010.11.008 ↗
37. Adler, D. (April, January 28). Genetic algorithms and simulated annealing: A marriage proposal. Proceedings of the IEEE International Conference on Neural Networks, San Francisco, CA, USA.
38. A simulated annealing approach to define the genetic structure of populations10.1046/j.1365-294X.2002.01650.x ↗
39. Optimization by simulated annealing10.1126/science.220.4598.671 ↗
40. An associated elastic-viscoplastic constitutive model for sandstone involving shear-induced volumetric deformation10.1016/j.ijrmms.2010.08.022 ↗
41. Improved genetic algorithm for solving TSP
42. A new mechanical model for soft rock rheology