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International Journal of Mining and Geo-Engineering، جلد ۵۵، شماره ۱، صفحات ۱-۶
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عنوان انگلیسی Application of Artificial Neural Network for Stability Analysis of Undercut Slopes
چکیده انگلیسی مقاله One of the significant tasks in undercut slopes is determining the maximum stable undercut span. According to the arching effect theory, undercut excavations cause the weight of the slope to be transmitted to the adjacent stable regions of the slope, which will increase the stability of the slope. In this research, determining the maximum width of undercut slopes was examined through numerical modeling in the FLAC3D software. For this purpose, a series of undercut slope numerical models, with various slope angles, horizontal acceleration coefficients, and counterweight balance widths was conducted, and the results were validated using the corresponding experimental test results. The effect of each parameter on the maximum stable undercut span was investigated with an artificial neural network, where a multi-layer perceptron (MLP) model was performed. The results showed good accuracy of the proposed MLP model in the prediction of the maximum stable undercut span. In addition, a sensitivity analysis demonstrated that the dip angle and horizontal acceleration coefficient were the most and least effective input variables on the maximum stable undercut span, respectively.
کلیدواژه‌های انگلیسی مقاله Undercut Slope, numerical modelling, Artificial Neural Network, Multi-layer Perceptron Model
نویسندگان مقاله Hassan Sarfaraz |
School of Mining Engineering, College of Engineering, University of Tehran,Tehran, Iran

Mohammad Hossein Khosravi |
School of Mining Engineering, College of Engineering, University of Tehran,Tehran, Iran

Thirapong Pipatpongsa |
Department of Urban Management, Kyoto University, Japan

Hassan Bakhshandeh Amnieh |
School of Mining Engineering, College of Engineering, University of Tehran,Tehran, Iran

نشانی اینترنتی https://ijmge.ut.ac.ir/article_77132_f6f42389bf749219d91471dd465710df.pdf
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