Compaction quality assessment of road subgrades using explainable deep graph learning framework

IF 5.3 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers and Geotechnics Pub Date : 2024-10-09 DOI:10.1016/j.compgeo.2024.106795
Feng Jia, Jie Zhang, Jianjun Shen, Liangfan Wu, Sinuo Ma
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Abstract

Compaction-quality assessment based on machine learning is an attractive topic in road construction research. However, existing methods do not consider the structural information of data when predicting the compaction degree. Thus, an explainable deep graph learning framework is proposed for the intelligent compaction quality assessment of road subgrades. In this method, a multi-domain analysis is first used to extract different indicators from the vibration signals of a vibratory roller. Second, the indicators for the different sampling points are constructed as graph structure data. Finally, an alternating graph-regularized regression network (AGRN) is developed to learn features from the graph data and aggregate the features using a regressor to predict the compaction degree. Through experimental verification, the proposed method displays an improved generalization ability and a high prediction accuracy when compared with other methods. Moreover, Shapley additive explanations (SHAP) are introduced to measure the marginal contributions of indicators for predicting the compaction degree in compaction quality assessments.
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利用可解释深度图学习框架评估路基压实质量
基于机器学习的压实质量评估是道路建设研究中一个极具吸引力的课题。然而,现有方法在预测压实度时并未考虑数据的结构信息。因此,本文提出了一种可解释的深度图学习框架,用于智能评估路基压实质量。在该方法中,首先使用多域分析法从振动压路机的振动信号中提取不同的指标。其次,将不同采样点的指标构建为图结构数据。最后,开发交替图正则化回归网络(AGRN),从图数据中学习特征,并使用回归器汇总特征,预测压实度。通过实验验证,与其他方法相比,所提出的方法显示出更强的泛化能力和更高的预测精度。此外,在压实质量评估中,引入了夏普利加法解释(SHAP)来衡量预测压实度指标的边际贡献。
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来源期刊
Computers and Geotechnics
Computers and Geotechnics 地学-地球科学综合
CiteScore
9.10
自引率
15.10%
发文量
438
审稿时长
45 days
期刊介绍: The use of computers is firmly established in geotechnical engineering and continues to grow rapidly in both engineering practice and academe. The development of advanced numerical techniques and constitutive modeling, in conjunction with rapid developments in computer hardware, enables problems to be tackled that were unthinkable even a few years ago. Computers and Geotechnics provides an up-to-date reference for engineers and researchers engaged in computer aided analysis and research in geotechnical engineering. The journal is intended for an expeditious dissemination of advanced computer applications across a broad range of geotechnical topics. Contributions on advances in numerical algorithms, computer implementation of new constitutive models and probabilistic methods are especially encouraged.
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