Field dry density is a soil compaction characteristic that is useful for geotechnical engineering design. Laboratory methods are laborious and time-consuming. The purpose of this paper is to determine and compare the effectiveness of MLR and SVM models in predicting this essential parameter from the fundamental soil property index level. A dataset of 86 soil samples with various geotechnical qualities was used, containing data such as gravel, sand, fines, liquid limit, and plastic limit. The dataset was split into 80% training and 20% testing. Using $R^{2},$ RMSE, and MSE, the performance of the built MLR and SVM prediction models was thoroughly examined. With an R2 value of 0.988 (on the test set), the SVM model outperforms the MLR model in terms of prediction accuracy for FDD $(R2=0.814)$. Compaction behavior and soil property index properties have a complicated relationship, as seen by the performance gap. According to feature importance analysis, the SVM model's predictions heavily relied on the fines content. According to this study, SVM is a useful method that geotechnical engineers can employ to quickly and affordably estimate compaction parameters in the early phases of site investigations and design optimization.
The performance of foundations located adjacent to natural slopes continues to pose significant technical challenges in geotechnical engineering, attributed to compromised soil stability, reduced bearing capacity, and increased foundation settlement. These phenomena are especially pronounced for ring foundations, which are adopted extensively for axi-symmetric superstructures such as silos, oil tanks, chimneys, and wind turbines. PLAXIS 3D software was used for numerical analysis. Some of the most critical parameters studied are the effect of soil properties and the differential settlement between two different regions on the ring foundation. The internal stress responses of the foundation structure, including the distribution of shear force and bending moment, are also studied. Important soil properties, including the friction angle and elastic modulus, exert comparable influences on foundation performance. The research reveals differential settlement between nodes situated nearest the slope and those positioned farther away. The resultant of bending and shearing forces in the ring foundation is proven to be most greatly influenced by slope geometry. The results are essential for the foundation of modern construction. The data will lead to the development of resilient designs for ring foundations on slopes.
This paper offers a comprehensive review of column-based ground improvement techniques, focusing on their fundamental mechanisms, design principles, construction methods, and field applications. It highlights stone columns and deep soil mixing (DSM) as the most widely used and effective solutions for enhancing the performance of weak and compressible soils. The core principles, including stress redistribution, increased shear strength, and accelerated consolidation, are discussed in detail. The review synthesizes key design parameters such as column geometry, area replacement ratio, and the role of geosynthetic reinforcement and load transfer platforms. It also examines the practical application of these methods through various case studies on embankments, tank foundations, and excavation supports. A dedicated section explores the pivotal role of numerical modeling, especially the finite element method (FEM), and emerging AI-driven approaches like Physics-Informed Neural Networks (PINNs) and surrogate modeling, which are shown to improve predictive accuracy and optimize the design process. Furthermore, the paper addresses critical challenges and limitations, including material variability, installation uncertainties, environmental impacts, and the need for enhanced quality control and long-term monitoring. It concludes by outlining future trends and innovations, such as the adoption of sustainable materials and the integration of machine learning for predictive design and real-time monitoring. This synthesis provides a structured overview of current best practices and offers valuable insights into the future direction of this vital area of geotechnical engineering.