Prediction of soil degree of compaction based on machine learning: a case study of two fine-grained soils
文献类型：期刊论文
| 作者 | Ran, Yuling1,2; Bai, Wei1; Kong, Lingwei1; Fan, Henghui2; Yang, Xiujuan2; Li, Xuemei3 |
| 刊名 | ENGINEERING COMPUTATIONS
 |
| 出版日期 | 2023-11-24 |
| 页码 | 22 |
| 关键词 | Degree of compaction Fine-grained soils Moisture content Electrical conductivity Temperature Machine learning |
| ISSN号 | 0264-4401 |
| DOI | 10.1108/EC-06-2023-0304 |
| 英文摘要 | Purpose - The purpose of this paper is to develop an appropriate machine learning model for predicting soil compaction degree while also examining the contribution rates of three influential factors: moisture content, electrical conductivity and temperature, towards the prediction of soil compaction degree.Design/methodology/approach - Taking fine-grained soil A and B as the research object, this paper utilized the laboratory test data, including compaction parameter (moisture content), electrical parameter (electrical conductivity) and temperature, to predict soil degree of compaction based on five types of commonly used machine learning models (19 models in total). According to the prediction results, these models were preliminarily compared and further evaluated.Findings - The Gaussian process regression model has a good effect on the prediction of degree of compaction of the two kinds of soils: the error rates of the prediction of degree of compaction for fine-grained soil A and B are within 6 and 8%, respectively. As per the order, the contribution rates manifest as: moisture content > electrical conductivity >> temperature.Originality/value - By using moisture content, electrical conductivity, temperature to predict the compaction degree directly, the predicted value of the compaction degree can be obtained with higher accuracy and the detection efficiency of the compaction degree can be improved. |
| 资助项目 | Hubei Provincial Natural Science Foundation of China[2023AFB835] ; National Natural Science Foundation of China[41772339] |
| WOS研究方向 | Computer Science ; Engineering ; Mathematics ; Mechanics |
| 语种 | 英语 |
| WOS记录号 | WOS:001108989700001 |
| 出版者 | EMERALD GROUP PUBLISHING LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/39977]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Bai, Wei |
| 作者单位 | 1.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan, Peoples R China 2.Northwest Agr & Forestry Univ, Coll Water Resources & Architectural Engn, Yangling, Peoples R China 3.Zhejiang Inst Hydraul & Estuary, Zhejiang Inst Marine Planning & Design, Hangzhou, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ran, Yuling,Bai, Wei,Kong, Lingwei,et al. Prediction of soil degree of compaction based on machine learning: a case study of two fine-grained soils[J]. ENGINEERING COMPUTATIONS,2023:22. |
| APA | Ran, Yuling,Bai, Wei,Kong, Lingwei,Fan, Henghui,Yang, Xiujuan,&Li, Xuemei.(2023).Prediction of soil degree of compaction based on machine learning: a case study of two fine-grained soils.ENGINEERING COMPUTATIONS,22. |
| MLA | Ran, Yuling,et al."Prediction of soil degree of compaction based on machine learning: a case study of two fine-grained soils".ENGINEERING COMPUTATIONS (2023):22. |
入库方式： OAI收割
来源：武汉岩土力学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。