各向异性
过度拟合
抗压强度
人工神经网络
人工智能
多孔性
支持向量机
堆积密度
机器学习
材料科学
地质学
计算机科学
数学
土壤科学
岩土工程
物理
复合材料
量子力学
土壤水分
作者
Kai Connie Wu,Qingshan Meng,Ruoxin Li,Le Luo,Ke Qin,Chi Wang,Chenghao Ma
标识
DOI:10.1016/j.jrmge.2023.10.005
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. © 2023 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/).
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