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A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography

文献类型：期刊论文

作者Wu, Kai1,2; Meng, Qingshan1,2; Li, Ruoxin1,2; Luo, Le1,2; Ke, Qin3; Wang, Chi1,2; Ma, Chenghao1,2
刊名JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING
出版日期2024-07-01
卷号16期号:7页码:2790-2800
关键词Coral reef limestone (CRL) Machine learning Pore tensor X-ray computed tomography (CT)
ISSN号1674-7755
DOI10.1016/j.jrmge.2023.10.005
英文摘要Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone (CRL), making it difficult to study mechanical behaviors. With X-ray computed tomography (CT), 112 CRL samples were utilized for training the support vector machine (SVM)-, random forest (RF)-, and back propagation neural network (BPNN)-based models, respectively. Simultaneously, the machine learning model was embedded into genetic algorithm (GA) for parameter optimization to effectively predict uniaxial compressive strength (UCS) of CRL. Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL. The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data. Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density, pore structure, and porosity of CRL are strongly correlated to UCS. However, the P-wave velocity is almost uncorrelated to the UCS, which is significantly distinct from the law for homogenous geomaterials. In addition, the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone (CFL) and coral boulder limestone (CBL), realizing the quantitative characterization of the heterogeneity and anisotropy of pore. The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
资助项目National Natural Science Foundation of China[41877267] ; National Natural Science Foundation of China[41877260] ; Priority Research Program of the Chinese Academy of Sciences[XDA13010201]
WOS研究方向Engineering
语种英语
WOS记录号WOS:001268397700001
出版者SCIENCE PRESS
源URL[http://119.78.100.198/handle/2S6PX9GI/41966]  
专题中科院武汉岩土力学所
通讯作者Meng, Qingshan
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China
3.Wuhan Univ, Inst Engn Risk & Disaster Prevent, State Key Lab Water Resources & Hydropower Engn Sc, Wuhan 430072, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Kai,Meng, Qingshan,Li, Ruoxin,et al. A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography[J]. JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING,2024,16(7):2790-2800.
APA Wu, Kai.,Meng, Qingshan.,Li, Ruoxin.,Luo, Le.,Ke, Qin.,...&Ma, Chenghao.(2024).A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography.JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING,16(7),2790-2800.
MLA Wu, Kai,et al."A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography".JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING 16.7(2024):2790-2800.

入库方式： OAI收割

来源：武汉岩土力学研究所

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