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- MakaleYalnız künye2026Scopus SJRQ1Machine learning-based multi-class classification of rock mass permeability using integrated geotechnical and geophysical parameters
Sari, Mahmut; Alemdag, Selcuk; Melek, Mesut; Seren, Aysel
In engineering applications, machine learning (ML) provides a promising approach when direct characterization of subsurface properties is challenging, time-consuming, and costly. This study developed a multi-class ML framework for classifying rock mass permeability based on Lugeon values using 86 observations from drilling and geophysical investigations at the Kırklartepe dam site in Bayburt, Türkiye. Seven predictors were considered: depth, Rock Quality Designation (RQD), joint frequency, Schmidt hammer hardness, P-wave velocity, S-wave velocity, and electrical resistivity. The observations were assigned to four permeability classes: very low, low, moderate, and high. Six classifiers were evaluated: Polynomial Kernel Support Vector Machine (P-SVM), Linear Discriminant Analysis, k-Nearest Neighbors (k-NN), AdaBoostM2, Bagged Trees, and Radial Basis Function SVM. Sequential Forward Feature Selection and Leave-One-Out Cross-Validation were used for feature selection and model evaluation, respectively. Robustness was further assessed using bootstrap confidence intervals, class-imbalance treatments, feature-selection stability, and learning curves. Among the baseline models, P-SVM using all seven predictors achieved the highest accuracy (79.07%), with a macro-F1 of 0.774 and the lowest Golden Distance (GD = 0.4372). When balanced class-wise performance was prioritized, k-NN with in-fold SMOTE achieved the lowest overall GD (0.4126) and a macro-F1 of 0.777. S-wave velocity and electrical resistivity were the most influential predictors. Overall, the findings demonstrate that the proposed framework effectively integrates geotechnical and geophysical data for rock mass permeability classification, providing a robust, data-driven decision-support approach that supports subsurface characterization and complements conventional permeability assessment in engineering applications.
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İncele - MakaleAçık Erişim2024Scopus SJRQ2Superplasticizer Dosage Effect on Strength, Microstructure and Permeability Enhancement of Cementitious Paste Fills
Cavusoglu, Ibrahim
A cementitious paste fill (CPF) ensures the long-age stability of underground cavities. Recently, superplasticizers, specifically ones that are polycarboxylate ether-based, have been incorporated to enhance CPF performance, yet their long-term effects on permeability, mechanical properties, and microstructures remain unclear. This study investigates these effects with CPF samples containing varying superplasticizer dosages (0%, 1%, 3%, 5%) that have been cured for up to 150 days. Rheological assessments (slump cone, vane shear tests), unconfined compressive strength testing, microstructural characterization (MIP, SEM), and hydraulic conductivity measurements were performed alongside XRD and thermal analyses (TAns) on high w/c (2) cement paste samples. The results showed that superplasticizer addition reduced CPF water content by 23% and yield stress by six times, aiding slurry transport. Long-term strength was enhanced by up to 2.4 times compared to the control samples, indicating improved underground stability. Superplasticizers altered the CPF samples stress–strain responses, increasing their load-bearing capacity. TG/DTG and XRD analyses revealed that hydration product development increased and porosity decreased in the presence of a superplasticizer. Hydraulic conductivity and permeability also decreased significantly. SEM and MIP analyses showed that the superplasticizer enhanced denser microstructures with fewer pores and fractures. These findings offer promising implications for designing CPFs with improved strength, durability, and environmental sustainability.
AtıfScopus14WoS11OpenAlex12İncele - MakaleYalnız künye2015Scopus SJRQ2Assessment of bearing capacity and permeability of foundation rocks at the Gumustas Waste Dam Site (NE Turkey) using empirical and numerical analysis
Alemdag, Selcuk
In this study, the bearing capacity and permeability of andesite and andesitic breccia rock masses, forming the basement for Gumustas waste dam, planned as a rock fill housing, were evaluated using various empirical equations and numerical analysis. The total stress constituting over the basement caused through ore waste building up in the body of dam and reservoir was determined by a finite element method. Then, this stress was compared with bearing capacities calculated for the rock masses. The permeability of rock masses in dam site and the lake were appraised with pressure water test experiments (Lugeon method), performed within opened bore holes and finite element method (seepage analysis). Since chemicals seeping from waste dams into the ground water are of vital importance for human health and the environment, the dam body must be made completely impervious using upstream slope and reservoir geosynthetics.
AtıfScopus30WoS24OpenAlex39İncele - MakaleAçık Erişim2012Scopus SJRQ1Assessment of permeability and injection depth at the Atasu dam site (Turkey) based on experimental and numerical analyses
Gurocak, Zulfu; Alemdag, Selcuk
This study was performed in order to evaluate the permeability of the basalts and pyroclastics and the maximum depth of grout injection at the Atasu dam site, Turkey, using the equations of Kiraly and Hoek and Bray based on values obtained from Lugeon tests. In order to evaluate maximum discharge values and depth of injection, seepage analyses were performed using the finite element technique for each 10 m up to 100 m. The results indicate that to establish an impermeable zone at the dam site, the depth of injection should be taken as 50 m for the left and right slopes and 40 m for the river bed.
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