地铁软土层冻融特性试验分析及模糊随机预测

针对南通地铁软土层三类土样进行冻胀和融沉试验,发现冻胀力、冻胀率和融沉系数随着冻结温度降低而增大;在相同的温度条件下,黏土冻融特性最显著,粉质黏土中等,而粉土最弱.受土性、温度、含水率和干密度等因素的综合影响,三类土样的冻融变化规律表现出明显的不确定性.利用两类不同的权值对小波神经网络的激励和输出函数进行修正,利用梯度下降的方法对伸缩和平移参数进行优化.在此基础上,以土性、温度、含水率和干密度为输入量,冻胀率和融沉系数为输出量建立模糊随机小波网络冻融特性预测模型.工程算例表明,模型冻胀和融沉的预测值与具体工程实测值基本吻合,可作为南通地铁及周边地区地下冻结法施工冻融特性预测的有效工具.

Experimental analysis and fuzzy random prediction of freeze-thaw characteristics of soft soil in metro construction

According to the freezing and thawing test of three types of soil samples in the freezing project of Nantong metro, it is found that the frost heaving force, the frost heaving rate and the thawing settlement coefficient increase with the decrease of freezing temperature. Under the same temperature condition, freezing and thawing characteristic of the clay is the most significant, silty clay is the medium, and silt is the weakest. It is also found that under the comprehensive influence of soil type, temperature, water content and dry density, the freezing-thawing change rules of the three types of soil samples are also different, showing obvious uncertainty. Two different weight parameters are used to modify the excitation and output functions of the wavelet neural network, and the scaling and translation parameters are optimized by gradient descent method. On this basis, the prediction model of freezing-thawing characteristics of fuzzy random wavelet network was established with soil type, temperature, water content and dry density as input and freezing-heaving rate and thawing sedimentation coefficient as output. Engineering examples show that the predicted values of frost heaving rate and thawing sedimentation coefficient are basically consistent with the measured values of specific projects, which can be used as an effective tool to predict the freeze-thaw characteristics of underground freezing method in Nantong metro and surrounding areas.