剪胀型土剪胀特性的大数据深度挖掘与模型研究

Deep mining of big data and model tests on dilatancy characteristics of dilatant soils

土的剪胀性是建立本构模型的重要基础,而当前建立的剪胀模型揭示其共同规律不够,这也是现有的本构模型不能良好反映土体变形机制的重要原因。基于Hadoop+Spark计算平台,提出了一种全局优化性强,收敛性快,计算稳定的(distributed levenberg marquardt regression)DLMR大数据特征深度挖掘算法。利用剪胀型土的大量剪胀特性试验数据,根据该算法和剪胀型土的基本力学特性,得到了剪胀型土的剪胀性大数据特征,发现了剪胀率与应力、应变以及应力增量存在明显的非线性特征,并分别建立了它们之间的相关性函数。在此基础上,构建了可以反映剪胀型土剪胀特性共同规律的剪胀模型。通过模型的比较,本文模型明显优于修正剑桥模型下剪胀模型的改进式和Rowe模型。通过模拟不同应力路径下剪胀型土的常规三轴压缩试验数据,表明本文模型能够良好反映不同应力路径下的剪胀性。

The dilatancy of soils is an important basis for constitutive models, and the current dilatancy models do not fully reveal their common laws, which is also an important reason why the existing constitutive models cannot well reflect the deformation mechanism of soils. Based on the Hadoop and Spark computing platform, a distributed Levenberg Marquardt regression(DLMR) algorithm for deep mining of big data with strong global optimization, fast convergence and computational stability is proposed. Based on a large number of experimental data of dilatancy characteristics of dilatant soils, according to the DLMR algorithm and the basic mechanical properties of soils, the big data characteristics of dilatancy of dilatant soils are obtained. It is found that there are obvious nonlinear characteristics between dilatancy ratio and stress, strain and stress increment, and the correlation functions between them are established respectively. On this basis, a dilatancy model which can reflect the common law of dilatancy characteristics of dilatant soils is constructed. Through model comparison, it is shown that the proposed model is superior to the dilatancy model of modified Cambridge model and Rowe model. By simulating the triaxial compression experimental data of dilatant soils under different stress paths, it is shown that the new model can well reflect the dilatancy under different stress paths.