Browsing by Author "Azarafza, Mohammad"

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Citation - WoS: 60
Citation - Scopus: 70
Application of Machine Learning Techniques for the Estimation of the Safety Factor in Slope Stability Analysis
(Mdpi, 2022) Nanehkaran, Yaser Ahangari; Pusatli, Tolga; Jin Chengyong; Chen, Junde; Cemiloglu, Ahmed; Azarafza, Mohammad; Derakhshani, Reza; 51704
Slope stability is the most important stage in the stabilization process for different scale slopes, and it is dictated by the factor of safety (FS). The FS is a relationship between the geotechnical characteristics and the slope behavior under various loading conditions. Thus, the application of an accurate procedure to estimate the FS can lead to a fast and precise decision during the stabilization process. In this regard, using computational models that can be operated accurately is strongly needed. The performance of five different machine learning models to predict the slope safety factors was investigated in this study, which included multilayer perceptron (MLP), support vector machines (SVM), k-nearest neighbors (k-NN), decision tree (DT), and random forest (RF). The main objective of this article is to evaluate and optimize the various machine learning-based predictive models regarding FS calculations, which play a key role in conducting appropriate stabilization methods and stabilizing the slopes. As input to the predictive models, geo-engineering index parameters, such as slope height (H), total slope angle (beta), dry density (gamma(d)), cohesion (c), and internal friction angle (phi), which were estimated for 70 slopes in the South Pars region (southwest of Iran), were considered to predict the FS properly. To prepare the training and testing data sets from the main database, the primary set was randomly divided and applied to all predictive models. The predicted FS results were obtained for testing (30% of the primary data set) and training (70% of the primary data set) for all MLP, SVM, k-NN, DT, and RF models. The models were verified by using a confusion matrix and errors table to conclude the accuracy evaluation indexes (i.e., accuracy, precision, recall, and f1-score), mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE). According to the results of this study, the MLP model had the highest evaluation with a precision of 0.938 and an accuracy of 0.90. In addition, the estimated error rate for the MLP model was MAE = 0.103367, MSE = 0.102566, and RMSE = 0.098470.
Citation - WoS: 10
Citation - Scopus: 9
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
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: 31
Citation - Scopus: 33
Fuzzy-Based Intelligent Model for Rapid Rock Slope Stability Analysis Using Qslope
(Mdpi, 2023) Mao, Yimin; Chen, Liang; Nanehkaran, Yaser A.; Azarafza, Mohammad; Derakhshani, Reza
Artificial intelligence (AI) applications have introduced transformative possibilities within geohazard analysis, particularly concerning the assessment of rock slope instabilities. This study delves into the amalgamation of AI and empirical techniques to attain highly precise outcomes in the evaluation of slope stability. Specifically, our primary objective is to propose innovative and efficient methods by investigating the integration of AI within the well-regarded Q(slope) system, renowned for its efficacy in analyzing rock slope stability. Given the complexities inherent in rock characteristics, particularly in coastal regions, the Q(slope) system necessitates adjustments and harmonization with other geomechanical methodologies. Uncertainties prevalent in rock engineering, compounded by water-related factors, warrant meticulous consideration during all calculations. To address these complexities, we present a novel approach through the infusion of fuzzy set theory into the Q(slope) classification, leveraging fuzziness to effectively quantify and accommodate uncertainties. Our approach employs a sophisticated fuzzy algorithm encompassing six inputs, three outputs, and 756 fuzzy rules, thereby enabling a robust assessment of rock slope stability in coastal regions. The implementation of this method capitalizes on the high-level programming language Python, enhancing computational efficiency. To validate the potency of our AI-based approach, we conducted preliminary tests on slope instabilities within coastal zones, indicating a promising initial direction. The results underwent thorough evaluation, affirming the precision and dependability of the proposed method. However, it is crucial to emphasize that this work represents a first attempt to apply AI to the evaluation of rock slope stability. Our findings underscore a high degree of concurrence and expeditious stability assessment, vital for timely and effective hazard mitigation. Nonetheless, we acknowledge that the reliability of this innovative method must be established through broader applications across diverse scenarios. The proposed AI-based approach's effectiveness is validated through a preliminary survey on a slope instability case within a coastal region, and its potential merits must be substantiated through broader validation efforts.
Citation - WoS: 0
Citation - Scopus: 0
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.
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.