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

作　　者：Kai Wu Qingshan Meng Ruoxin Li Le Luo Qin Ke ChiWang Chenghao Ma 

机构地区：[1]State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences,Wuhan,430071,China [2]University of Chinese Academy of Sciences,Beijing,100049,China [3]State Key Laboratory of Water Resources and Hydropower Engineering Science,Institute of Engineering Risk and Disaster Prevention,Wuhan University,Wuhan,430072,China

出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》2024年第7期2790-2800,共11页岩石力学与岩土工程学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.41877267 and 41877260);the Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA13010201).

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.

关 键 词：Coral reef limestone(CRL) Machine learning Pore tensor X-ray computed tomography(CT) 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TU43[自动化与计算机技术—控制科学与工程]