首页|A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography
A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography
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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.
Kai Wu、Qingshan Meng、Ruoxin Li、Le Luo、Qin Ke、Chi Wang、Chenghao Ma
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State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences,Wuhan,430071,China
University of Chinese Academy of Sciences,Beijing,100049,China
State Key Laboratory of Water Resources and Hydropower Engineering Science,Institute of Engineering Risk and Disaster Prevention,Wuhan University,Wuhan,430072,China
National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaPriority Research Program of the Chinese Academy of Sciences
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