开发基于半监督联合训练的路基压实质量实时评估方法

IF 4.9 2区 工程技术 Q1 ENGINEERING, CIVIL Transportation Geotechnics Pub Date : 2024-10-22 DOI:10.1016/j.trgeo.2024.101412
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引用次数: 0

摘要

目前用于实时评估路基压实质量的智能方法主要依赖于有监督的机器学习算法。然而,砂锥试验的稀缺性极大地阻碍了模型性能的发挥,从而严重限制了智能路基压实的适用性。本文提出了一种半监督协同训练算法,用于在施工过程中实时评估路基压实度,利用未标记数据提高模型性能。基于不同路基场景下的压实数据集,本文提出了 PSO-XGB-Co-training KNN-PLS 算法(PSO-XGB-CoKP)来训练非标记数据,其平均平方误差(MSE)降低了 20.4%。通过采用不同的回归因子作为协同训练子模型并增加回归因子的数量,对半监督协同训练算法进行了修改。通过对精确度和计算成本的杠杆作用对模型进行了优化,并通过灵敏度研究推荐了最佳数据增量。这项研究为利用不平衡和小样本数据集开发可靠的智能方法提供了另一种方法,用于评估工程实践中的路基压实质量。
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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.
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来源期刊
Transportation Geotechnics
Transportation Geotechnics Social Sciences-Transportation
CiteScore
8.10
自引率
11.30%
发文量
194
审稿时长
51 days
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.
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