利用 CNN 回归模型对沙粒的圆度和球度进行分类,缓解数据不平衡问题

IF 5.6 1区 工程技术 Q1 ENGINEERING, GEOLOGICAL Acta Geotechnica Pub Date : 2024-09-21 DOI:10.1007/s11440-024-02410-z
Donghwi Kim, Heejung Youn
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引用次数: 0

摘要

确定砂颗粒的形状参数有助于了解砂的岩土特性。本研究旨在利用人工智能(AI)确定朱门津砂的圆度和球度。测试使用的数据集包括 1000 张朱门镇砂的砂粒图像。训练集包括约 28,000 张图像,由合成数据(5,000 张图像)和额外的数据增强技术组合而成,以解决数据不平衡问题。与确定圆度和球度的传统方法不同,本研究提出了一种将回归模型与卷积神经网络(CNN)相结合的模型,使用 ResNet 和 DenseNet 作为骨干网络。根据 DenseNet169 模型预测值与真实值之间的判定系数 (R2) 对结果进行评估,发现圆度的 R2 为 0.695,球度的 R2 为 0.979。当使用训练有素的模型根据 Krumbein 和 Sloss 图表进行分类时,DenseNet169 模型表现出最高的准确率(73.6%)、精确率(77.9%)和召回率(77.2%)。人工智能预测与人类评估之间的比较显示,人类分类存在相当大的差异,这取决于观察者,而人工智能模型在确定圆度和球度方面始终表现出强劲的性能。
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Classifying roundness and sphericity of sand particles using CNN regression models to alleviate data imbalance

Determining the shape parameters of sand particles helps to understand the geotechnical properties of sand. This study aims to determine the roundness and sphericity of Jumunjin sand utilizing artificial intelligence (AI). A dataset comprising 1000 sand particle images from Jumunjin sand was used for testing. The training set included approximately 28,000 images, created through a combination of synthetic data (5000 images) and additional data augmentation techniques to address data imbalance issues. Unlike traditional methods for determining roundness and sphericity, this research proposes a model that combines a regression model with a convolutional neural network (CNN), using ResNet and DenseNet as the backbone networks. The results, evaluated based on the coefficient of determination (R2) between the predicted values using the DenseNet169 model and the true values, yielded an R2 of 0.695 for roundness and 0.979 for sphericity. When classifying based on the Krumbein and Sloss chart using the trained model, the DenseNet169 model demonstrated the highest accuracy (73.6%), precision (77.9%), and recall (77.2%). A comparison between AI predictions and human evaluations revealed considerable variation in human classification, depending on the observers, whereas the AI model consistently exhibited robust performance in determining both roundness and sphericity.

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来源期刊
Acta Geotechnica
Acta Geotechnica ENGINEERING, GEOLOGICAL-
CiteScore
9.90
自引率
17.50%
发文量
297
审稿时长
4 months
期刊介绍: Acta Geotechnica is an international journal devoted to the publication and dissemination of basic and applied research in geoengineering – an interdisciplinary field dealing with geomaterials such as soils and rocks. Coverage emphasizes the interplay between geomechanical models and their engineering applications. The journal presents original research papers on fundamental concepts in geomechanics and their novel applications in geoengineering based on experimental, analytical and/or numerical approaches. The main purpose of the journal is to foster understanding of the fundamental mechanisms behind the phenomena and processes in geomaterials, from kilometer-scale problems as they occur in geoscience, and down to the nano-scale, with their potential impact on geoengineering. The journal strives to report and archive progress in the field in a timely manner, presenting research papers, review articles, short notes and letters to the editors.
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