Feng Jia, Jie Zhang, Jianjun Shen, Liangfan Wu, Sinuo Ma
{"title":"Compaction quality assessment of road subgrades using explainable deep graph learning framework","authors":"Feng Jia, Jie Zhang, Jianjun Shen, Liangfan Wu, Sinuo Ma","doi":"10.1016/j.compgeo.2024.106795","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"176 ","pages":"Article 106795"},"PeriodicalIF":5.3000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Geotechnics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0266352X24007341","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
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.
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.