【作者】 杨全亮；

【导师】 梁青槐；

【作者基本信息】 北京交通大学 ， 道路与铁道工程， 2008， 硕士

【摘要】 盾构法施工技术在地铁工程建设中逐步得到广泛应用,但国内外对盾构掘进参数间关系规律的研究相对滞后,有必要就典型地层条件和线路条件下掘进参数内在规律进行研究,对合理掘进参数的选取做出预测,并对施工过程中极易出现的各类典型故障做出诊断,从而保证施工过程的安全,降低工程造价,减少地铁施工的不利影响。为了认识各主要参数的内在规律,即要掌握参数在特定外部条件下的分布情况,明确参数间函数关系及参数影响因素,本文采用数理统计方法对上述参数关系规律加以研究,并得出如下结论:对于特定的线路、地层条件而言,各掘进参数呈近似正态分布;与室内模型试验结论相比,各实测参数间定量关系较弱,很难以显式函数加以描述;沿线地质条件对于选取的全部掘进参数均会产生显著影响,而线路条件只会对部分掘进参数产生显著影响,对于掘进参数的影响程度地质条件要大于线路条件。基于上述分析结论,需要采用其他分析方法来对参数间关系规律加以研究。通过能够实现模糊信息处理的人工神经网络方法,本文将掘进参数分为两类:一类为体现外部因素条件的输入变量;另一类为体现盾构系统响应输出变量。而对于掘进参数内在规律的研究问题即可转化为通过输入变量参数来预测输出变量参数的问题。研究结果表明,神经网络对掘进参数进行预测的方法是可行的,特别对于复杂地质条件下的盾构试验性推进过程,本文提供了一种较为理想的掘进参数预测方法。目前,对于盾构法施工的风险评估更加注重地层与支护结构状态变化的研究,而对于盾构系统本身,特别是对于体现掘进状态变化的参数数据的研究还相对较少。本文通过对盾构施工过程中典型故障的归类分析,将各类故障通过由掘进参数构成的状态向量加以描述,使盾构掘进过程中的故障诊断问题转化为复杂系统工作状态的模式分类问题,并凭借神经网络模式识别技术,完成对施工中典型故障的诊断。通过对数据诊断结果的检验可知,神经网络对于施工过程中的某些典型故障具有较好的识别能力,因而对掘进过程具有一定的指导意义。更多还原

【Abstract】 The shield tunneling method is more and more applied into metro projects. But the study on the regularity of tunneling parameters has lagged. It is necessary to study the regularity of parameters according to some geological condition and alignment condition, to forecast the proper parameters, and to diagnose typical failures that are inclined to occur in the process, so as to assure the process of security and lower the cost and the adverse effect of metro projects.In order to obtain the regularity of parameters, that is, to acquire the distribution of parameters on some conditions, to find out functional relationship and influencing factors for parameters, mathematical statistics is an effective way. The statistic conclusions go as follows: all the parameters appear approximately normal distribution on the conditions appointed; comparing with conclusions derived from model tests, all the parameters measured have a too weak quantitative relation to describe by explicit formulation; the geological conditions will have greater effects on all the parameters than the alignment conditions will do.According to the statistic conclusions above, the study on the regularity of tunneling parameters needs other means. The means of Artificial Neural Networks (ANN) is competent for disposal of problems with fuzzy information. The tunneling parameters can be grouped in two: one is input variable as outer conditions, and the other is output variable as system response. Then, the problem on the parameter regularity can be converted into the problem on forecasting the output variable by the input variable. The study conclusion is that, the parameter-forecasting method for shield tunneling is feasible, especially for the testing advance under complicated geological conditions.At present, comparing with the study on the state of stratum and structure for risk estimation for shield tunneling, less attention has been paid on the shield system, particularly on the tunneling parameters which reflect working state. Some kinds of typical failures can be described by state vectors composed of tunneling parameters after typical failures are classified. So, the problem of diagnosing of failures in the tunneling process can be converted into that of pattern discrimination of complex systems. By means of ANN pattern discrimination techniques, the typical failures in constructions can be diagnosed. By testing the diagnosis results, ANN can diagnose some typical failures well, and can offer a good guidance for the shield tunneling.更多还原