{"title":"开发基于半监督联合训练的路基压实质量实时评估方法","authors":"","doi":"10.1016/j.trgeo.2024.101412","DOIUrl":null,"url":null,"abstract":"<div><div>The current intelligent methodologies for real-time evaluation of subgrade compaction quality predominantly rely on supervised machine learning algorithms. However, the scarcity of sand cone test significantly impedes model performance, thereby severely limiting the applicability of intelligent subgrade compaction. This paper proposes a semi-supervised co-training algorithm for real-time evaluation of subgrade compactness during the construction procedure, leveraging unlabeled data to enhance the model performance. Based on the compaction datasets from various subgrade scenarios, the proposed PSO-XGB-Co-training KNN-PLS (PSO-XGB-CoKP) algorithm is utilized to train the unlabeled data, boasting a 20.4% reduction in Mean Squared Error (MSE). The semi-supervised co-training algorithm is modified by employing different regressors as co-trained sub-models and increasing the number of regressors. The model is optimized by levering the accuracy and computational cost, and an optimal data augmentation volume is recommended through the sensitivity study. This study provides an alternative approach for leveraging unbalanced and small-sample datasets to develop a reliable intelligent methodology for evaluating subgrade compaction quality in engineering practice.</div></div>","PeriodicalId":56013,"journal":{"name":"Transportation Geotechnics","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Developing a real-time compaction quality assessment methodology for subgrade based on semi-supervised co-training\",\"authors\":\"\",\"doi\":\"10.1016/j.trgeo.2024.101412\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The current intelligent methodologies for real-time evaluation of subgrade compaction quality predominantly rely on supervised machine learning algorithms. However, the scarcity of sand cone test significantly impedes model performance, thereby severely limiting the applicability of intelligent subgrade compaction. This paper proposes a semi-supervised co-training algorithm for real-time evaluation of subgrade compactness during the construction procedure, leveraging unlabeled data to enhance the model performance. Based on the compaction datasets from various subgrade scenarios, the proposed PSO-XGB-Co-training KNN-PLS (PSO-XGB-CoKP) algorithm is utilized to train the unlabeled data, boasting a 20.4% reduction in Mean Squared Error (MSE). The semi-supervised co-training algorithm is modified by employing different regressors as co-trained sub-models and increasing the number of regressors. The model is optimized by levering the accuracy and computational cost, and an optimal data augmentation volume is recommended through the sensitivity study. This study provides an alternative approach for leveraging unbalanced and small-sample datasets to develop a reliable intelligent methodology for evaluating subgrade compaction quality in engineering practice.</div></div>\",\"PeriodicalId\":56013,\"journal\":{\"name\":\"Transportation Geotechnics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-10-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Geotechnics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214391224002332\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Geotechnics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214391224002332","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Developing a real-time compaction quality assessment methodology for subgrade based on semi-supervised co-training
The current intelligent methodologies for real-time evaluation of subgrade compaction quality predominantly rely on supervised machine learning algorithms. However, the scarcity of sand cone test significantly impedes model performance, thereby severely limiting the applicability of intelligent subgrade compaction. This paper proposes a semi-supervised co-training algorithm for real-time evaluation of subgrade compactness during the construction procedure, leveraging unlabeled data to enhance the model performance. Based on the compaction datasets from various subgrade scenarios, the proposed PSO-XGB-Co-training KNN-PLS (PSO-XGB-CoKP) algorithm is utilized to train the unlabeled data, boasting a 20.4% reduction in Mean Squared Error (MSE). The semi-supervised co-training algorithm is modified by employing different regressors as co-trained sub-models and increasing the number of regressors. The model is optimized by levering the accuracy and computational cost, and an optimal data augmentation volume is recommended through the sensitivity study. This study provides an alternative approach for leveraging unbalanced and small-sample datasets to develop a reliable intelligent methodology for evaluating subgrade compaction quality in engineering practice.
期刊介绍:
Transportation Geotechnics is a journal dedicated to publishing high-quality, theoretical, and applied papers that cover all facets of geotechnics for transportation infrastructure such as roads, highways, railways, underground railways, airfields, and waterways. The journal places a special emphasis on case studies that present original work relevant to the sustainable construction of transportation infrastructure. The scope of topics it addresses includes the geotechnical properties of geomaterials for sustainable and rational design and construction, the behavior of compacted and stabilized geomaterials, the use of geosynthetics and reinforcement in constructed layers and interlayers, ground improvement and slope stability for transportation infrastructures, compaction technology and management, maintenance technology, the impact of climate, embankments for highways and high-speed trains, transition zones, dredging, underwater geotechnics for infrastructure purposes, and the modeling of multi-layered structures and supporting ground under dynamic and repeated loads.