基于免疫学原理的遗传算法在土石坝反分析中的应用
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摘要
土石坝是传统的坝型,结构简单,易取材,是世界大多数国家坝工建设的主要坝型。其安全性分析方法的研究是目前坝工研究的热点。利用原位观测资料进行大坝材料反分析,进而预测大坝实际的工作性状是进行大坝安全性评价的重要手段,因而进行土石坝反演分析理论和方法的研究具有十分重要的意义。
本文在阅读了大量文献的基础上,深入地讨论了土石坝材料模型参数反演分析的原理,并探讨了基于现场观测信息反演分析土体模型参数的具体方法,开发了基于免疫学原理的遗传算法的土石坝反分析程序。最后结合三峡茅坪溪沥青混凝土心墙坝工程实例,通过使用坝体在填筑阶段所取得的现场观测成果,进行了沥青混凝土邓肯张E—μ模型参数的反演分析。反演结果验证了本文所建议的理论与方法是可行的,开发的程序是有效性的。具体工作如下:
1.在遗传算法原理的基础上,结合非线性排序策略和最优保存策略,用FORTRAN语言编制了改进的遗传算法程序。从优化一个简单函数的实例入手,分析该程序的可行性。
2.遗传算法随机搜索能力强,但是其收敛方向无法控制、易陷入局部最优解,且收敛慢。而基于免疫学原理的遗传算法利用浓度调节机制,可以克服遗传算法的上述缺点,文章用简单函数进行程序测试,验证了该算法的有效性。
3.在已有反演分析方法的基础上,结合免疫遗传算法基本原理,开发了基于免疫学原理的遗传算法反分析程序,并应用于三峡茅坪溪沥青混凝土心墙坝的心墙材料力学模型参数反演分析。
4.利用沥青混凝土材料的反演参数值,对三峡茅坪溪沥青混凝土心墙坝进行二维有限元计算,预测了大坝实际运行的工作性态,为大坝安全运用提供依据。
Embankment dam which material can be got easily is the main style during many countries on the earth. its research of security analytical method is a hot issue.Using the in-situ observation data to back-analyze the dam material parameter, and forecast the actual running state of dam is one of the main methods of safety evaluation.So it is of great significance to study the theories and methods of back-analysis.In this paper, after reading lots of correlation literatures, the back-analysis principle.of the Embankment Dam material parameters are lucubrated and a material method of back-analysis model parameter which is based on on-site observation date is discussed, and the back-analysis of genetic algorithms based on Immunology principle is empoldered combined with the projiet example of maopingxi dam.Useing the stress observation date at the bottorm of Asphalt-concrete Core wall during the constructing.period to back-analysis the core wall's model parameter.the result of this project have verified the genetic algorithms based on Immunology principle used in embankment dam back-analysis to be effective.The main contents are as follows:1.Basing on the SGA and uniting non-line taxis strategy , the improved genetic alogorithm program is presented in fortran language , and its feasibility is analyzed by a simple function.2.The GA based on immunology principle which uses the composition regulation conquered GA's defect sach as the way of converging can not control , the speed of converging is slow , and this method's validity has been tested by simple function.3.Basing on the existing back-analysis method , empoldered the genetic algorithms Based on Immunology principle , and used in the core wall model parameters back-analysis of the maopingxi project to test it's validity.4.Wanted to forecast work behavior of the dam , the 2-D FEM analysis has been carried on the project of maopingxi dam.In this process the back-analysis values is used.
本文在阅读了大量文献的基础上,深入地讨论了土石坝材料模型参数反演分析的原理,并探讨了基于现场观测信息反演分析土体模型参数的具体方法,开发了基于免疫学原理的遗传算法的土石坝反分析程序。最后结合三峡茅坪溪沥青混凝土心墙坝工程实例,通过使用坝体在填筑阶段所取得的现场观测成果,进行了沥青混凝土邓肯张E—μ模型参数的反演分析。反演结果验证了本文所建议的理论与方法是可行的,开发的程序是有效性的。具体工作如下:
1.在遗传算法原理的基础上,结合非线性排序策略和最优保存策略,用FORTRAN语言编制了改进的遗传算法程序。从优化一个简单函数的实例入手,分析该程序的可行性。
2.遗传算法随机搜索能力强,但是其收敛方向无法控制、易陷入局部最优解,且收敛慢。而基于免疫学原理的遗传算法利用浓度调节机制,可以克服遗传算法的上述缺点,文章用简单函数进行程序测试,验证了该算法的有效性。
3.在已有反演分析方法的基础上,结合免疫遗传算法基本原理,开发了基于免疫学原理的遗传算法反分析程序,并应用于三峡茅坪溪沥青混凝土心墙坝的心墙材料力学模型参数反演分析。
4.利用沥青混凝土材料的反演参数值,对三峡茅坪溪沥青混凝土心墙坝进行二维有限元计算,预测了大坝实际运行的工作性态,为大坝安全运用提供依据。
Embankment dam which material can be got easily is the main style during many countries on the earth. its research of security analytical method is a hot issue.Using the in-situ observation data to back-analyze the dam material parameter, and forecast the actual running state of dam is one of the main methods of safety evaluation.So it is of great significance to study the theories and methods of back-analysis.In this paper, after reading lots of correlation literatures, the back-analysis principle.of the Embankment Dam material parameters are lucubrated and a material method of back-analysis model parameter which is based on on-site observation date is discussed, and the back-analysis of genetic algorithms based on Immunology principle is empoldered combined with the projiet example of maopingxi dam.Useing the stress observation date at the bottorm of Asphalt-concrete Core wall during the constructing.period to back-analysis the core wall's model parameter.the result of this project have verified the genetic algorithms based on Immunology principle used in embankment dam back-analysis to be effective.The main contents are as follows:1.Basing on the SGA and uniting non-line taxis strategy , the improved genetic alogorithm program is presented in fortran language , and its feasibility is analyzed by a simple function.2.The GA based on immunology principle which uses the composition regulation conquered GA's defect sach as the way of converging can not control , the speed of converging is slow , and this method's validity has been tested by simple function.3.Basing on the existing back-analysis method , empoldered the genetic algorithms Based on Immunology principle , and used in the core wall model parameters back-analysis of the maopingxi project to test it's validity.4.Wanted to forecast work behavior of the dam , the 2-D FEM analysis has been carried on the project of maopingxi dam.In this process the back-analysis values is used.
引文
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[3] G. Gioda, G. Maier. Direct search solution of an inverse problem in elasto-plasticity: identification of cohesion, friction angle and in-situ stress by pressure tunnel test. Int J Num Methods in Eng, 1980, 15: 1823～1848
[4] G. Gioda, L. Jurina. Numerical identification of soil-structure interaction pressures. International Journal for Numerical and Analytical Methods in Geomechanics, 1981, 5(1): 33～56
[5] S. Sakurai, K. Takeuchi. Back analysis of measured displacement of tunnels. Rock Mechanics and Rock Engineering, 1983, 16(3): 173～178
[6] A. Cividini, G. Maier, A. Nappi. Parameter estimation of a static geotechnical model using a bayes' approach. Int J Rock Mech Min Sci & Geomech Abstr, 1983, 20(5): 215～226
[7] A. Asaoka, M. Matsuo. An inverse problem approach to the prediction of multi-dimensional consolidation behavior. Soils and Foundations, 1984. 3, 24(1): 49～62
[8] G. Gioda, S. sakurai. Back analysis procedures for the interpretation of field measurements in geomechanics. International Journal for Numerical and Analytical Methods in Geomechanics, 1987, 11(6): 555～583
[9] S. Sakurai, S. Akutagawa. Some aspects of back analysis in Geotechnical engineering, in: Eurock' 93. Safety and environmental issues in rock engineering. Proc. symposium, Lisba, 1993. Vol. 2, ed L. Ribeiro, E. Sousa & N. F. Grossmann, (Balkema), 1995: 1133～1140
[10] M. Hoshiya, A. Sutoh. Kalman filter-finite element method in identification. Journal of Engineering Mechanics, 1993. 2, 119(2): 197～210
[11] A. R. Simpaon, S. D. Priest. The application of genetic algorithms to optimization problems in geotechnics, 1993, 15(1): 1～19
[12] C. G. Koh, L. M. See. Identification and uncertainty estimation of structural parameters. Journal of Engineering Mechanics, 1994. 6, 120(6): 1219～1236
[13] M. Suzuki, K. Ishii. Parameter identification and probabilistic prediction of settlement of embankment. Stuctural Safety, 1994. 4, 14: 47～59
[14] P. S. Theocaris, P. D. Panagiotopoulos. Generalized hardening plasticity approximated via anisotropic elasticity: a neural network approach. Computer Methods in Applied Mechanics and Engineering, 1995, 125: 123～139
[15] A. Ledesma, A. Gens, E. E. Alosno.. Parameter and variance estimation in geotechnical back analysis using prior information. International Journal for Numerical & Analytical Methods in Geomechanics, 1996, 20(2): 119～141
[16] A. Ledesma, A. Gens, E. E. A1onso. Estimation of parameters in Geotechnical Backanalysis-Ⅰ. Maximum Liklihood Approach. Computers and Geotechnics, 1996, 18(1): 1～27
[17] A. Gens, A. Ledesma, E. E. A1onso. Estimation of parameters in Geotechnical. Backanalysis-Ⅱ. Application to a Tunnel Excavation Problem. Computers and Geotechnics, 1996, 18(1): 29～46
[18] D. A. Tomonari. Implicit constitutive modeling for visco-platicity using neural networks. Int J Num Methods in Eng, 1998, 43: 195～219
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