基于SSA–BP方法土冻胀率影响因素敏感性分析

SENSITIVITY ANALYSIS OF FACTORS AFFECTING SOIL FROST HEAVE RATE BASED ON SSA–BP METHOD

土体冻胀率影响因素敏感性分析是人工冻土重要研究内容之一,对工程建设具有重要意义。人工冻土冻胀率影响因素众多,且各影响因素彼此耦合。选用甘肃地区砂壤土、黏土、壤土的实验数据,采用麻雀搜索算法优化反向传播算法(back propagation, BP)神经网络建立预测模型,在此基础上提出一种冻胀率影响因素敏感性计算方法。结果表明优化预测模型的预测精度远高于传统BP神经网络预测模型,且冻胀率各影响因素在砂壤土、黏土、壤土中最敏感的分别为冻结速率、初始干容重、初始含水率,为防范工程冻胀危害提供了重要参考。

Sensitivity analysis of factors affecting soil frost heave rate is one of the most important research contents of artificial frozen soil, which is of great significance to engineering construction. There are many factors influencing the frost heave rate of artificial frozen soil, and those influencing factors are coupled together. The experimental data of sandy loam, clay and loam in Gansu area are selected, and the sparrow search algorithm is used to optimize the back propagation(BP) neural network to establish a predictive model. On this basis,a method for calculating the sensitivity of the frost heave rate affecting factors is proposed. The results show that the prediction accuracy of the optimized prediction model is much higher than that of the traditional BP neural network prediction model. It also reals that the most sensitive factors affecting the frost heave rate in sandy loam, clay, and loam are frost penetration rate, initial dry density in the freezing part and initial water content in the freezing part. This research provides an important reference for preventing engineering frost heave hazards.