基于动三轴试验的软黏土软化系数的分析与预测

Prediction and statistical analysis of softening index of soft clay based on dynamic triaxial test

在长期循环动力荷载下,软黏土这类工程性质不良的土的变形和动弹模量等力学参数会产生变化。为了讨论长期动力荷载对软黏土的作用,收集了软黏土在动三轴试验中的动力特征参数,分析了软化系数、累积塑性应变与循环应力比CSR的关系。基于逻辑回归分类算法,以土体是否破坏作为分类预测标准进行了参数影响分析。结果表明,CSR越小,频率越大,软化系数越大,土体越不容易破坏,但频率的作用相对于CSR的影响较弱。进一步利用随机森林模型将软黏土的动静力特征相结合,对不容易直接获取的软化系数进行回归预测,模型训练集精度为0.93,测试集精度达0.79,通过显著性水平检验。模型表明,累积塑性应变相对于原状土或重塑土、超固结比OCR、加载波形、频率、CSR和根据变形曲线判断的土体状态类型(稳定/破坏)等指标对软化系数的预测有较大影响,体现了机器学习技术的良好应用前景。

Under dynamic loading,the deformation and dynamic modulus of weak soil,such as soft clay,can change.This study aims to investigate the effect of long-term dynamic loads on soft clay by collecting dynamic parameters through dynamic triaxial testing.The analysis focuses on exploring the relationship between the softening index,cumulative plastic strain and cyclic stress ratio(CSR).The data exhibits a certain concentration,prompting the utilization of machine learning methods.Using a logistic regression classifier to determine soil stability,it is found that soil is less likely to collapse with lower CSR,higher loading frequency and increased softening index.The effect of frequency is observed to be less significant than that of CSR.To establish a connection between dynamic parameters and the static behavior of soil samples,a random forest model is used to predict the softening index,which is challenging to obtain directly.The prediction results demonstrate a good fit of the model and pass the significance test.The model achieves a precision of 0.93 for the training set and 0.79 for the test set.It is revealed that cumulative plastic strain plays a crucial role in predicting the softening index,outweighing other factors such as undisturbed or remolded soil,overconsolidation ratio(OCR),loading waveform,frequency,CSR and soil state type(stability/failure)based on deformation curves.This suggests promising prospects for applying the trained model and machine learning techniques.