It will be practically useful to know the mechanical properties of granular materials by only taking a photo of particles. This study attempts to deal with this challenge by developing a novel deep learning-based modelling strategy. In this strategy, the convolutional neural network (CNN) as image identification algorithm is first used to extract the particle information (particle size distribution PSD and morphology) based on the image of a granular sample, and the bidirectional long short-term memory (BiLSTM) neural network is employed to train the model of reproducing mechanical behaviours and induced fabric evolutions of the sample with corresponding particle information. The datasets of images of samples are generated using discrete element method, and the datasets of mechanical properties together with fabric evolutions are obtained through numerical tests on corresponding samples. As a preliminary attempt, two-dimensional biaxial samples and tests with initially isotropic fabric are considered for the sake of simplicity. The feasibility and reliability of the proposed modelling strategy are evaluated through training and testing. All results indicate that the first part of the model based on CNN is capable of accurately identifying PSD of a granular sample, as well as circularity and roundness of particles, using which as connecting parameters the mechanical behaviours together with induced fabric evolutions of granular materials are subsequently well captured by the second part of the model based on BiLSTM. This study provides a basis and a possible way to obtain immediately particle and packing information, mechanical properties and fabric evolutions by leveraging images of granular materials.

仅通过拍摄颗粒照片来了解粒状材料的机械性能将在实践中很有用。这项研究试图通过开发一种新颖的基于深度学习的建模策略来应对这一挑战。在这种策略中，卷积神经网络（CNN）作为图像识别算法首先使用基于颗粒样本的图像提取颗粒信息（颗粒大小分布PSD和形态），然后使用双向长短期记忆（BiLSTM）神经网络训练再现机械行为并诱导的模型。样品的织物结构演变以及相应的颗粒信息。采用离散元法生成样品图像数据集，并通过对相应样品进行数值测试，获得力学性能与织物演变的数据集。作为初步尝试，为简单起见，考虑使用二维各向同性的样品并使用初始各向同性的织物进行测试。通过培训和测试来评估所提出的建模策略的可行性和可靠性。颗粒的圆度，随后将其用作连接参数，将机械行为与颗粒材料的诱导织物演化一起，由基于BiLSTM的模型的第二部分很好地捕获。这项研究为利用颗粒材料的图像立即获得颗粒和堆积信息，机械性能和织物演变提供了基础和可能的方法。