{"title":"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","authors":"Donghwi Kim, Heejung Youn","doi":"10.1007/s11440-024-02410-z","DOIUrl":null,"url":null,"abstract":"<div><p>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 (<i>R</i><sup>2</sup>) between the predicted values using the DenseNet169 model and the true values, yielded an <i>R</i><sup>2</sup> 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.</p></div>","PeriodicalId":49308,"journal":{"name":"Acta Geotechnica","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classifying roundness and sphericity of sand particles using CNN regression models to alleviate data imbalance\",\"authors\":\"Donghwi Kim, Heejung Youn\",\"doi\":\"10.1007/s11440-024-02410-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 (<i>R</i><sup>2</sup>) between the predicted values using the DenseNet169 model and the true values, yielded an <i>R</i><sup>2</sup> 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.</p></div>\",\"PeriodicalId\":49308,\"journal\":{\"name\":\"Acta Geotechnica\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Geotechnica\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11440-024-02410-z\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, GEOLOGICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Geotechnica","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s11440-024-02410-z","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
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.
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.