基于WOA-BP神经网络的超低温冻土抗压强度预测模型研究

为获得超低温冻土抗压强度预测模型, 探究超低温状态下冻土的物理性质及力学性质的变化, 对含水率19%, 22%, 25%和28%的低液限黏土土样进行?180 °C ~ ?10 °C的单轴压缩强度试验, 并测量?80 °C ~ ?10 °C土样的未冻水含量, 建立基于WOA-BP神经网络和BP神经网络的预测模型, 探究含水率、温度、未冻水含量与超低温冻土抗压强度关系. 预测结果表明: 含水率、温度、未冻水含量与超低温冻土抗压强度存在复杂的非线性关系, 特别是在?180 °C ~ ?80 °C区间内, 现有的线性拟合公式已无法准确预测该区间内冻土抗压强度; 基于WOA-BP神经网络预测模型的整体预测效果较好, 其绝对误差平均值为1.167 MPa, 相对误差平均值为7.62%, BP神经网络预测模型的绝对误差平均值为8.462 MPa, 相对误差平均值为47.99%. 基于鲸鱼优化算法的BP神经网络预测模型预测误差明显小于BP神经网络预测模型及线性拟合值, 更接近实测值. 该预测模型具有较高精确度, 能有效解决超低温冻土抗压强度与其影响因素间复杂的非线性关系, 可为人工冻结技术在地层应急工程中的应用提供参考. 

PREDICTION MODEL OF COMPRESSIVE STRENGTH OF ULTRA LOW TEMPERATURE FROZEN SOIL BASED ON WOA-BP NEURAL NETWORK1)

In order to obtain the prediction model of compressive strength of ultra-low temperature frozen soil and explore the changes of physical and mechanical properties of frozen soil under ultra-low temperature, the uniaxial compressive strength test of ?180 °C ~ ?10 °C was carried out on the low liquid limit clay soil samples with water content of 19%, 22%, 25% and 28%, and the unfrozen water content of ?80 °C ~ ?10 °C soil samples was measured. Using the above data, a prediction model based on WOA-BP neural network and BP neural network was established to explore the relationship between moisture content, temperature, unfrozen water content and compressive strength of ultra-low temperature frozen soil. The prediction results show that there is a complex nonlinear relationship between moisture content, temperature, unfrozen water content and the compressive strength of ultra-low temperature frozen soil, especially in the range of ?180 °C ~ ?80 °C, the existing linear fitting formula can not accurately predict the compressive strength of frozen soil in this range. The overall prediction effect of the prediction model based on WOA-BP neural network is good. The average absolute error is 1.167 MPa and the average relative error is 7.62%. The average absolute error of BP neural network prediction model is 8.462 MPa and the average relative error is 47.99%. The prediction error of BP neural network prediction model based on whale optimization algorithm is significantly less than that of BP neural network prediction model and linear fitting value, and is closer to the measured value. The prediction model has high accuracy and can effectively solve the complex nonlinear relationship between the compressive strength of ultra-low temperature frozen soil and its influencing factors. It can provide a reference for the application of artificial freezing technology in stratum emergency engineering.