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A novel deep learning-based modelling strategy from image of particles to mechanical properties for granular materials with CNN and BiLSTM

机译:一种新的基于深度学习的模拟策略,从颗粒图像与CNN和Bilstm的颗粒材料的机械性能

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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. (C) 2021 ElsevierB.V. All rights reserved.
机译:通过仅拍摄颗粒的照片来说,了解颗粒材料的机械性能几乎是有用的。这项研究试图通过开发一种基于深入学习的建模策略来解决这一挑战。在该策略中,首先使用卷积神经网络(CNN)作为图像识别算法,用于基于粒度样本的图像和双向长期内记忆(Bilstm)提取粒子信息(粒度分布PSD和形态)。 )神经网络用于培训具有相应粒子信息的再现机械行为的模型和诱导样品的织物演进。使用离散元件法生成样本的图像数据集,并且通过对应样本上的数值测试获得机械性能的数据集与织物演进一起获得。作为初步尝试,为了简单起见,考虑二维双轴样品和初始各向同性织物的试验。通过培训和测试评估所提出的建模策略的可行性和可靠性。所有结果表明,基于CNN的模型的第一部分能够精确地识别粒状样品的PSD,以及颗粒的圆形度和圆度,与连接参数一起使用的机械行为与粒状的诱导织物演进一起随后基于Bilstm的模型的第二部分拍摄。本研究提供了通过利用颗粒材料的图像获得立即粒子和包装信息,机械性能和织物演进的基础和可能的方法。 (c)2021 elsevierb.v。版权所有。

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