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/).

Cracking behavior can reduce soil hydraulic and mechanical properties and is a preferential pathway for water flow and pollutant transportation, resulting in polluted environment, such as application to landfill liners and capping. Recently, researchers have advocated the use of waste materials for clay mixtures using various measurement and analysis methods. Therefore, this study aims to conduct a bibliometric analysis of the scientific literature published between 2002 and 2021 obtained from Scopus to quantitatively identify research trends, key research areas, and future research paths in this field on desiccation and crack behavior using waste materials as landfill liners. The VOS viewer software was used to analyze 41 articles in which the paper selection process was filtered. The results showed that the fly ash mixture's application as a landfill liner could reduce cracking significantly. Furthermore, fractal analysis and X-ray computed tomography measurements have proven to be good candidates for measuring cracks because they are the most accurate for calculating the crack value. Waste materials such as fly ash can be applied as landfill liners with other materials, such as bentonite and coconut coir fibers. This study is beneficial for improving the design and selecting the appropriate materials for landfill liners.