Application of digital image correlation to derive Paris' law constants in granite specimens - Karimi Aghsaghali, Javad (Theoretical and Applied Fracture Mechanics, 2023)
This study investigates the effectiveness of Digital Image Correlation (DIC) as a powerful tool for deriving Paris' law constants in black granite specimens subjected to cyclic loading. A series of monotonic and cyclic loading tests on rectangular granite specimens yields valuable insights into geological material fatigue behavior, establishing monotonic tests as foundational references for subsequent cyclic loading experiments. Innovative DIC software application enables precise tracking and analysis of Crack Mouth Opening Displacement (CMOD) values, revealing distinct, observable patterns in crack growth during cyclic loading. A notable contribution lies in the implementation of a compliance calibration technique integrating Finite Element Method (FEM), facilitating the direct linkage between CMOD values and effective crack lengths, resulting in highly accurate Paris' law constants. With an average of 1.04 E −7 for C and 8.95 for m, these constants are essential for predicting fatigue crack propagation in granite specimens. This research underscores DIC's efficacy as a pivotal fatigue analysis tool, enhancing predictive capabilities for structural safety and durability assessments in geological materials, while emphasizing the crucial role of advanced imaging and numerical techniques in material science and structural engineering. It lays a robust foundation for future geological material studies and comprehensive structural integrity assessment

Estimating the compressive strength of plastic concrete samples using machine learning algorithms - Karimi Aghsaghali, Javad (Asian Journal of Civil Engineering, 2023)
Determining the mechanical properties of plastic concrete samples through experimental investigation is costly and time-consuming. This research used supervised machine learning (ML) techniques, including Decision Tree (DT), Random Forest (RF), Gradient Boost (GB), Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), and K-Nearest Neighborhood (KNN) for predicting the compressive strength of the plastic concrete samples considering different values of cement, water, water-to-cement ratio, bentonite, temperature, and sand. The models' performances are compared and evaluated using the correlation of coefficient (R2) score, Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). According to the results, the DT model was more effective in predicting with R2 = 0.87. In addition, a sensitivity analysis was carried out to determine each parameter's contribution level in implementing models using the RF algorithm. Consequently, it was shown that ML techniques are valuable tools for predicting the mechanical properties of plastic concrete in a more time and cost-effective way compared to laboratory tests.

Experimental and numerical failure mechanism evaluation of anisotropic rocks using extended finite element method - Karimi Aghsaghali, Javad (Theoretical and Applied Fracture Mechanics, 2024)
Rocks often show an anisotropic behavior due to microcracks, mineral-based structure, and unequal in-situ stresses. The crack tip's stress intensity factors (SIFs) that control rock failure heavily depend on anisotropy. Given the significance of extensive research to identify the effects of anisotropy on the mechanical behavior and strength of rocks, numerical modeling is considered an essential method in designing structures and analyzing their stability. This study experimentally and numerically evaluated the effects of the bedding angle, initial crack angle with the loading axis, crack length, and anisotropy ratio on the stress intensity factor of transversely isotropic rocks via the extended finite element method (XFEM). Cracked chevron-notched Brazilian disks (CCNBDs) were fabricated from phyllite under the International Society for Rock Mechanics (ISRM) standards and tested at different initial crack lengths and angles for validation. The XFEM approach was then used to predict toughness and crack propagation path in the tested and new specimens with varying ratios of anisotropy and bedding angles (ψ = 0, 30, 45, 60, and 90°). The results demonstrated that with the increase of the anisotropy ratio, in equal crack length and initial angle, the absolute value of the SIFs have larger values. In addition, with the increase of crack length, the first and second modes of SIF increased, which indicates the failure of the specimen at lower loads. Also, bedding angle is effective on the stress intensity factors and this effect becomes more intense as the crack length increases. Finally, the anisotropy properties not only affect the values of SIFs, but also can change the angle at which the specimen experiences pure mode I or mode I

Investigating the fatigue performance of Nano-Silica-modified concrete with various admixtures: An experimental study - Karimi Aghsaghali, Javad (Results in Engineering, 2025)
Understanding the fatigue behavior of concrete structures under cyclic loading is crucial for enhancing their durability and performance in demanding applications. In this study, four types of concrete mixtures containing varying percentages of Nano-Silica (NS) (0.0 %, 0.5 %, 1.0 %, and 1.5 %) were prepared to evaluate their mechanical properties and fatigue performance. The investigation focused on assessing the monotonic tensile and compressive behavior, as well as the tensile fatigue life, to establish the relationship between NS content, mechanical properties, and fatigue resistance. Key findings indicate that the optimal NS content for enhancing uniaxial compressive strength (UCS), tensile strength (σt), and elastic modulus (E) was 1.0 %. However, fatigue life under cyclic tensile loading decreased with increasing NS content due to the increased brittleness (B) introduced by higher strength mixtures. Secondary strain rate analysis and brittleness index (BI) calculations further highlighted the balance between mechanical enhancement and deformation capacity. These results provide valuable insights into the design of NS-modified concrete for applications requiring a balance between strength and fatigue durability under extreme loading conditions.