论文标题
使用机器学习来阐明粒度分布与颗粒材料的机械行为之间的隐藏相关性
Use of Machine Learning for unraveling hidden correlations between Particle Size Distributions and the Mechanical Behavior of Granular Materials
论文作者
论文摘要
使用数据驱动的框架来预测多分散颗粒材料的密集包装的宏观机械行为。离散元素方法DEM用于生成92,378个球体包装，涵盖了许多不同种类的粒径分布，PSD，位于2个粒径内。这些包装受到三轴压缩，并将相应的应力 - 应变曲线拟合到邓肯 - 变双曲模型。多元统计分析未能成功将模型参数与源自PSD衍生的常见岩土技术和统计描述符联系起来。相比之下，经过数百个DEM模拟训练的人工神经网络（NN）方案能够以相当精确的速度预测所有这些PSD的模型参数的值。尽管训练数据中存在噪声，但这是实现的。 NN揭示了颗粒材料的PSD与其宏观机械行为之间存在隐藏相关性。
A data-driven framework was used to predict the macroscopic mechanical behavior of dense packings of polydisperse granular materials. The Discrete Element Method, DEM, was used to generate 92,378 sphere packings that covered many different kinds of particle size distributions, PSD, lying within 2 particle sizes. These packings were subjected to triaxial compression and the corresponding stress-strain curves were fitted to Duncan-Chang hyperbolic models. A multivariate statistical analysis was unsuccessful to relate the model parameters with common geotechnical and statistical descriptors derived from the PSD. In contrast, an artificial Neural Network (NN) scheme, trained with a few hundred DEM simulations, was able to anticipate the value of the model parameters for all these PSDs, with considerable accuracy. This was achieved in spite of the presence of noise in the training data. The NN revealed the existence of hidden correlations between PSD of granular materials and their macroscopic mechanical behavior.
