Intelligent optimization of the structure of the large section highway tunnel based on adaptive immunity algorithm
How to determine the physical and mechanical parameters of the surrounding rock become obstacle of the theory analysis with tunnel and numerical research because of complexity and unpredictable of tunnel surrounding rock.The vast majority of tunnels in the design of analog experience when they are in accordance with the recommended value. Because of the complicated conditions,great deformation,high stress,multi-variables,high non-linearity,the algorithm for structure optimization and its application in tunnel engineering has just been at the right beginning. However, how to make optimized design of structure for tunnel section, reduce excavation and increase support is a very urgent task now. Artificial intelligence shows extremely strong capability in identifying, expressing and disposing such kind of multiple variables and complicated non-linear relation.In this paper, Regard Adaptive Immunity Algorithm as searching tool to look for the best Network structure for parameters in all room, then, avoid multi-parameter optimization of parameters and a local minimum value of the shortcomings, and improve computing speed and efficiency.It is able to adjust the excavation area and support and protection design in the current design to get the conclusion with practical value by means of optimization of structure surface of the garden tunnel, verifying the advantages of this method in optimization calculation of tunnel engineering at faster calculation speed and higher efficiency and also indicating the improved immune genetic algorithm has practical significance in the stability evaluation of tunnel rock and information design.
adaptive immune genetic tunnle super-large section optimization
Si-Mayuzhou Wang-Xiaomi Jia-Xiaofeng
Deparment of Civil Engineering Nanyang Institute of Technology,Nanyang,China 473004 Deparment of Chemistry and Engineering Nanyang Institute of Technology, Nanyang,China 473004
国际会议
长沙
英文
342-345
2009-10-10（万方平台首次上网日期，不代表论文的发表时间）