基于超声波速的冻融盐渍土强度预测模型构建

Construction of strength prediction model for freeze-thaw saline soil based on ultrasonic velocity

现有宏观控制因素作用下对冻土宏观强度的研究主要采用试验手段,在实践中已取得较好的成果。室内外试验普遍存在试验周期长、成本高的特点,随着新技术手段的涌现,探索更为简便的方法和构建预测模型,成为了科研工作者长期努力的方向。同时宏观控制因素对冻土宏观强度的影响借助土体内部特征这一媒介发挥作用。由于超声波是岩土介质物理力学性质等相关信息的良好载体,利用超声波检测具有的无损、快速、简便等特点,可反映土体内部特征。因此,本文从两种思路出发,设计出包含不同类型参数的强度预测模型:思路1,宏观控制性因素到宏观强度特性;思路2,宏观控制性因素到超声波速反映的土体内部特征再到宏观强度特性。基于此,首先通过试验获取不同含盐量土体经历不同冻融循环次数的超声波速和单轴抗压强度作为基本数据,再将思路1参数设置为试验控制变量,将思路2参数设置为纵、横波速构造的超声特征参数群,即联合两思路参数作为模型输入,建立单轴抗压强度的BP神经网络预测模型,并采用缺省因子检验法评估该模型。结果表明,随冻融次数、含盐量增加,单轴抗压强度总体减小;冻融初期波速波动明显,中期趋缓,后期恢复至初始值附近,且控制性因素作用下强度随波速增加呈阶梯式下降状态。而将经灰色关联度及粗糙集优化的思路2参数作响应土体内部特性的最优子序列建立的BP模型,平均绝对误差小于0.05 kPa,决定系数大于0.96,各参数敏感性指数平均为1.4251。同时敏感性分析成功验证了控制性因素、最优子序列在模型构建过程的假设地位,单个控制性参数对单轴抗压强度的影响大于单个超声特征参数。此外,模型精度较高、实用性较强,可为冻土模型的强度预测及参数选取提供参考。

The existing research on the macroscopic strength of frozen soil under the influence of macroscopic control factors mainly relies on experimental methods,and has achieved good results based on actual conditions.Generally speaking,both indoor and outdoor tests have shortcomings such as long cycle time and high cost.With the emergence of new technological means,exploring simpler methods and building predictive models has been a long-term endeavor of scientific researchers.At the same time,the influence of macroscopic control factors on the macroscopic strength of frozen soil is exerted through the medium of the internal characteristics of the soil.Since ultrasonic waves are a good carrier of relevant information such as the physical and mechanical properties of rock and soil media,ultrasonic testing can reflect the internal characteristics of the soil due to its non-destructive,fast and simple characteristics.Therefore,this paper designs a strength prediction model containing different types of parameters based on different ideas.Idea 1-macro-controlling factors to macro-strength characteristics,idea 2-macro-controlling factors to internal soil characteristics reflected by ultrasonic wave velocity and then to macro-strength properties.Through experiments,the ultrasonic wave velocity and uniaxial compressive strength of soils with different salt contents undergoing different freeze-thaw cycles were obtained as basic data.The experimental control variables are used as idea 1 parameters,the ultrasonic characteristic parameter group constructed with compressional and shear wave velocities is used as idea 2 parameters,and the combined two ideas parameters are used as model input.A BP neural network prediction model for uniaxial compressive strength was established,and the prediction model was evaluated using the default factor test method.Tests show that as the number of freezing and thawing times and the salt content increase,the uniaxial compressive strength decreases overall.The wave velocity fluctuates significantly in the early stages of freezing and thawing,slows down in the middle stage,and returns to near the initial value in the later stage.Under the action of controlling factors,the uniaxial compressive strength decreases step by step as the wave velocity increases.The idea 2 parameters after gray correlation and rough set optimization are used to establish a BP neural network model for the optimal subsequence responding to the internal characteristics of the soil.The average absolute error of the model is less than 0.05 kPa,the coefficient of determination is greater than 0.96,and the average sensitivity index of each parameter is 1.4251.Sensitivity analysis successfully verified the assumed status of controlling factors and optimal subsequences in the model building process.A single controllable parameter has a greater impact on uniaxial compressive strength than a single ultrasonic characteristic parameter.The 29 parameters can be divided into four levels according to their contribution weight to the model.In the subsequent dimensionality reduction and feature selection of the number of parameters,the fourth level parameters should be discarded first,and the third level parameters should be optimized through parameter construction innovation and data sample expansion.This can reduce the number of overall parameters and increase the contribution weight,thereby better optimizing the model.The BP neural network model of uniaxial compressive strength established based on the different ideas of ultrasonic testing has strong predictive ability and good interpretability of model parameters.The ultrasonic characteristic parameter group under the influence of control factors plays an important role in the construction of the strength model.It also verifies the reliability and effectiveness of the BP neural network model in predicting the uniaxial compressive strength of saline soil.The model has high accuracy and strong practicability,and can provide a reference for strength prediction and parameter selection of frozen soil models.