级配
卷积神经网络
发掘
超参数
炸薯条
人工神经网络
人工智能
岩土工程
集合(抽象数据类型)
计算机科学
模式识别(心理学)
工程类
电信
程序设计语言
作者
Yuan-en Pang,Xu Li,Dong Zhang,Qiuming Gong
标识
DOI:10.1016/j.autcon.2024.105414
摘要
A tunnel boring machine (TBM) generates rock chips during excavation, which are crucial for assessing surrounding rock integrity, enhancing excavation efficiency, and evaluating cutter wear. However, traditional methods struggle to identify small rock chips, chips submerged in soil or water, and chips in stacked states. This paper proposes a convolutional neural network (CNN)-based method for directly recognizing the particle size distribution from rock chip images. A dataset of 2520 rock chip images representing 84 particle-size distributions was collected in a laboratory environment. By comparing various CNN architectures and hyperparameters, an optimal model was obtained with a mean absolute error (MAE) of 1.66 × 10−2 and R2 of 0.923 on the test set. The results demonstrate that the proposed method enables the real-time recognition of particle size distribution using rock chip images, which has the potential to significantly improve intelligent auxiliary excavation technology in TBMs.
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