Journal of Engineering Geology

Effect of the Soil-Water Characteristic Curve (SWCC) parameters on the slope stability of an earth dam in steady state and rapid drawdown

seyed ali asghari pari

This study investigates the effect of Soil-Water Characteristic Curve (SWCC) parameters on the slope stability of an earth dam under steady-state and rapid drawdown conditions. Given the importance of unsaturated soil behavior in earth dams, this research employs principles of unsaturated soil mechanics to analyze the influence of SWCC parameters on water flow rate and slope stability.The results indicate that parameters a and n positively enhance the flow rate, while an increase in parameter m reduces it. In slope stability analysis, parameters of SWCC showed negligible effects on the downstream slope stability, whereas an increase in m caused a slight reduction in the safety factor. Under rapid drawdown conditions, all parameters initially led to a decrease in the safety factor, but stability was restored after 10 days. Additionally, accounting for the unsaturated unit weight of the soil improved the safety factor in both steady-state and rapid drawdown scenarios. These findings highlight the critical role of unsaturated soil conditions in the design and stability analysis of earth dams.

Uncertainty quantification of ucs prediction in sedimentary rocks using petrographic features: a machine learning–monte carlo approach

Seyyed Mahmoud Fatemi Aghda

The evaluation of mechanical strength, particularly the uniaxial compressive strength (UCS) of rocks, plays a critical role in the design and performance prediction of surface and underground structures, significantly impacting project costs and safety in engineering applications. Traditional laboratory testing methods for UCS assessment are destructive, time-consuming, and expensive, while indirect methods often lack reliability due to rock heterogeneity. This study addresses these limitations by developing advanced machine learning frameworks that integrate petrographic features with conventional rock properties to predict UCS and quantify associated uncertainties. The research utilized a comprehensive dataset from sedimentary rocks collected along Iran's southern coastlines (Persian Gulf and Gulf of Oman), encompassing mechanical properties (UCS, Brazilian tensile strength, point load index, porosity, ultrasonic pulse velocity), durability indices (Los Angeles abrasion, slake durability, aggregate impact value), and detailed petrographic characteristics derived from thin-section analysis. Three complementary approaches were implemented: (1) hybrid Neural Network-Gradient Boosting regression (ANN-GBR), (2) AutoML-optimized Random Forest, and (3) Monte Carlo simulation-based uncertainty quantification. Key petrographic features including immature and mature clastic textures, the mineral composition (quartz, chert) were used as input parameters alongside alongside laboratory testing to improve the prediction of UCS.The influence of these petrographic features on the rock’s microstructure and microcrack propagation contributes to reducing model uncertainty and enhances the reliability of predictions in complex and heterogeneous rock conditions. The AutoML-optimized Random Forest model demonstrated exceptional predictive performance with R² = 0.9884, RMSE = 0.5732 MPa, and MAPE = 3.6%, significantly outperforming traditional empirical methods. The ANN-GBR hybrid approach achieved R² = 0.9412 with RMSE = 1.385 MPa, while Monte Carlo simulations provided robust probabilistic assessments through 95% confidence intervals and systematic bias identification. Feature importance analysis revealed that soundness parameters and mineralogical composition are the most influentialpredictors, emphasizing the critical role of micro-scale petrographic properties in determining macroscopic mechanical behavior.

Extending geotechnical classification using excavation stability data in fine-grained alluvial deposits

Emad Namavar

Accurate geotechnical classification is essential for designing excavations in urban environments, where soil behavior is greatly affected by excavation-induced stresses. This study improves the geotechnical characterization of fine-grained alluvial deposits belonging to the youngest sedimentary unit (Unit D) in Rieben’s classification system. A comprehensive investigation was conducted through borehole drilling, Standard Penetration Tests (SPT), pressuremeter testing, and laboratory experiments including triaxial, uniaxial, and direct shear tests. Excavation stability was evaluated using the Morgenstern–Price method under both short-term and long-term conditions. Based on the geotechnical parameters and slope stability simulations, Unit D was subdivided into three distinct zones (D1, D2, and D3) with different excavation behaviors. Zone D1, characterized by lower sand content, allows deeper vertical cuts, whereas the presence of sandy lenses in Zone D3 restricts excavation depth and requires gentler slopes. The findings provide an updated geotechnical classification framework for fine-grained alluvia, offering practical guidelines for safe excavation design and contributing to the broader understanding of alluvial systems in urban geotechnical engineering.

Integration of empirical and systems engineering frameworks in tunnel support design: a comprehensive study of 38 tunnels in Iran

Mehdi Talkhablou

Reliable assessment methods are required for designing initial support for tunnels in complex geological conditions. This study provides a thorough comparison of the Rock Mass Rating (RMR) and Rock Engineering System (RES) frameworks, examining a substantial dataset comprising 38 tunnels situated in various lithological and tectonic zones across Iran. While the RMR framework offers empirical simplicity, the RES framework provides a systems-based approach that quantifies parameter interdependencies. Analysis of field data, including shotcrete thickness and bolt density, revealed that the RES framework captures hydro-mechanical coupling more effectively, particularly in intermediate rock masses. To reconcile discrepancies between the two systems, we explored an integrated statistical formulation combining normalized RMR ratings with RES stability indices. This approach demonstrated a significantly higher correlation with field performance (R² ≈ 0.99) than the individual methods. The results emphasise the importance of integrating empirical and systems-based approaches to improve the reliability of predictions in tunnel support design and provide a solid foundation for engineering decisions in heterogeneous rock masses.