[Oral Presentation]基于卷积神经网络的颗粒材料级配特性研究

Organized By

Hohai University

Monash University

Co-organized by

Southeast University

Jiangxi University of Science and Technology

Northeast University

DUT-BSU Scientific Research and Innovation Center

Suzhou University of Science and Technology

State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment

Sponsoring agency

KELI SDEM Software (Dalian) Co., Ltd.

Sugon Smart Computing Information Technology Co., Ltd.

Beijing Paratera Technology Co., Ltd.

Simpop Information Technology Co., Ltd.

Important Dates

Conference Announcement

December 2023, release First Round Announcement
Abstract Submission Deadline
March 31^th, 2024
Paper Submission Deadline
April 30^th, 2024
Registration
From January 1^st, 2024
Conference Schedule
May 31^st (Friday), 2024 On-Site Registration
June 1^st to 3^rd, 2024 Conference

基于卷积神经网络的颗粒材料级配特性研究

ID：116 Submission ID：63 View Protection：ATTENDEE Updated Time：2024-04-10 17:13:15 Hits：670 Oral Presentation

Start Time：Pending （Asia/Shanghai）

Duration：Pending

Session：[No Session] » [No Session Block]

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Abstract

颗粒材料广泛存在于各类自然环境与工程领域中，不同级配分布颗粒集合的力学性能存在显著差异。为快速获取不同级配分布颗粒集合的力学强度指标并探究级配对力学性能的影响机理，本文根据连续级配方程，采用离散元方法生成了100组不同连续级配分布条件下的颗粒材料数值试样并通过双轴压缩试验对其力学性能进行了测试；将压缩后的数值试样图像及相应的力学强度指标作为训练数据，建立了可预测不同级配分布颗粒集合抗剪强度指标的GoogLeNet卷积神经网络模型；通过调整输入图像信息，研究了颗粒实体及孔隙分布，颗粒粒径尺寸，高应力颗粒及力链颗粒信息对模型预测能力的影响；采用迁移学习算法对预测模型进行了优化，增强了模型对不同颗粒形状颗粒集合力学性能的预测能力。

Keywords

级配,卷积神经网络,离散元,颗粒材料

Speaker

巨凯萱

太原理工大学

Submission Author

巨凯萱太原理工大学

赵婷婷太原理工大学

Sponsored By

Hohai University

Panel of Computational Mechanics on Granular Materials, Working Party of Computational Mechanics, Chinese Society of Theoretical and Applied Mechanics

Jiangsu Society of Theoretical and Applied Mechanics