Browsing by Author "Bonab, Masoud Hajialilue"

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Citation - WoS: 13
Citation - Scopus: 12
Deep Learning Method for Compressive Strength Prediction for Lightweight Concrete
(Techno-press, 2023) Nanehkaran, Yaser A.; Azarafza, Mohammad; Pusatli, Tolga; Bonab, Masoud Hajialilue; Irani, Arash Esmatkhah; Kouhdarag, Mehdi; Derakhshani, Reza; 51704; 03.07. Yönetim Bilişim Sistemleri; 03. İktisadi ve İdari Birimler Fakültesi; 01. Çankaya Üniversitesi
Concrete is the most widely used building material, with various types including high-and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.
Citation - WoS: 12
Citation - Scopus: 11
Innovative Stability Analysis of Complex Secondary Toppling Failures in Rock Slopes Using the Block Theory
(Springer Heidelberg, 2025) Mao, Yimin; Azarafza, Mohammad; Bonab, Masoud Hajialilue; Pusatli, Tolga; Nanehkaran, Yaser A.; 03.07. Yönetim Bilişim Sistemleri; 03. İktisadi ve İdari Birimler Fakültesi; 01. Çankaya Üniversitesi
We present the block theory-based secondary toppling stability analysis method (BTSTSA), an advanced and novel method specifically designed to assess secondary toppling failures in slopes. This innovative method comprehensively accounts for various failure mechanisms and computes the factor of safety (F.S) for rock slopes. Grounded in Block theory principles, particularly the key-block method, and supplemented by limit equilibrium techniques, BTSTSA offers a practical and reliable analytical framework. Our investigation focused on five discontinuous rock slopes in the South Pars region, southwest Iran, which are affected by composite toppling failure mechanisms. The stability analysis results were meticulously verified using the Aydan-Kawamoto method, a recognized benchmark in the field. Comparative analysis consistently demonstrated that the BTSTSA approach generates more conservative estimates of the F.S compared to the Aydan-Kawamoto method. This conservatism underscores the robustness and reliability of the BTSTSA framework and highlights its implications for practical engineering applications. The integration of this innovative analytical method with data from these investigations offers crucial insights for geotechnical engineers, equipping them to manage the complexities of secondary toppling failures in discontinuous rock slopes. These findings emphasize the importance of considering conservatism in engineering applications and provide a more accurate and reliable assessment of slope stability, particularly concerning secondary toppling failures, thereby benefiting geotechnical engineering practices.