基于多级神经网络的盾构法隧道施工参数优化  

Construction Parameter Optimization for Shield Tunneling Based on the Multi-Level ANN

作　　者：熊静[1] 喻钢[2] 

机构地区：[1]上海工程技术大学航空运输学院,上海200336 [2]上海大学悉尼工商学院,上海200072

出　　处：《计算机工程与科学》2006年第5期133-135,140,共4页Computer Engineering & Science

摘　　要：在盾构法隧道施工中,合理地设置施工参数、确保地面沉降控制在一定的范围之内是实际施工中最为关注的问题。本文将盾构推进的过程划分成七个阶段,每个阶段用一个神经网络进行模拟,在此基础上构造成多级神经网络,拟合盾构法隧道施工中施工参数与地面沉降之间关系的数学模型。最后根据保证质量、兼顾效益和效率的目标,采用遗传算法进行施工参数的优化匹配,并提出了相应的控制方案。该方法已在多项工程项目中得到使用。结果表明,此方法对工程实际施工有很好的指导作用。Setting appropriate construction parameters to control the ground settlement is the linchpin of shield tunneling. This paper divides the whole shield drive process into seven stages, in which each stage is simulated by an artificial neural network, The multi-level artificial neural network, which is composed by the seven stages, builds the relational model between construction parameters and the ground settlement. According to this model, we optimize the control scheme to ensure quality, increase speed and decrease cost by genetic algorithms. This approach has been applied to many tunneling projects and the results show good prospect.

关 键 词：盾构法隧道 施工参数 多级神经网络 遗传算法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]