Application of Artificial Neural Network for Stability Analysis of Undercut Slopes
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. I...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Tehran
2021-06-01
|
| Series: | International Journal of Mining and Geo-Engineering |
| Subjects: | |
| Online Access: | https://ijmge.ut.ac.ir/article_77132_f6f42389bf749219d91471dd465710df.pdf |
| _version_ | 1818236067398025216 |
|---|---|
| author | Hassan Sarfaraz Mohammad Hossein Khosravi Thirapong Pipatpongsa Hassan Bakhshandeh Amnieh |
| author_facet | Hassan Sarfaraz Mohammad Hossein Khosravi Thirapong Pipatpongsa Hassan Bakhshandeh Amnieh |
| author_sort | Hassan Sarfaraz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T12:03:58Z |
| format | Article |
| id | doaj.art-79c14c45881f4fa3b242275b264e46f2 |
| institution | Directory Open Access Journal |
| issn | 2345-6949 |
| language | English |
| last_indexed | 2024-12-12T12:03:58Z |
| publishDate | 2021-06-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | International Journal of Mining and Geo-Engineering |
| spelling | doaj.art-79c14c45881f4fa3b242275b264e46f22022-12-22T00:25:03ZengUniversity of TehranInternational Journal of Mining and Geo-Engineering2345-69492021-06-015511610.22059/ijmge.2020.292606.59483277132Application of Artificial Neural Network for Stability Analysis of Undercut SlopesHassan Sarfaraz0Mohammad Hossein Khosravi1Thirapong Pipatpongsa2Hassan Bakhshandeh Amnieh3School of Mining Engineering, College of Engineering, University of Tehran,Tehran, IranSchool of Mining Engineering, College of Engineering, University of Tehran,Tehran, IranDepartment of Urban Management, Kyoto University, JapanSchool of Mining Engineering, College of Engineering, University of Tehran,Tehran, IranOne 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.https://ijmge.ut.ac.ir/article_77132_f6f42389bf749219d91471dd465710df.pdfundercut slopenumerical modellingartificial neural networkmulti-layer perceptron model |
| spellingShingle | Hassan Sarfaraz Mohammad Hossein Khosravi Thirapong Pipatpongsa Hassan Bakhshandeh Amnieh Application of Artificial Neural Network for Stability Analysis of Undercut Slopes International Journal of Mining and Geo-Engineering undercut slope numerical modelling artificial neural network multi-layer perceptron model |
| title | Application of Artificial Neural Network for Stability Analysis of Undercut Slopes |
| title_full | Application of Artificial Neural Network for Stability Analysis of Undercut Slopes |
| title_fullStr | Application of Artificial Neural Network for Stability Analysis of Undercut Slopes |
| title_full_unstemmed | Application of Artificial Neural Network for Stability Analysis of Undercut Slopes |
| title_short | Application of Artificial Neural Network for Stability Analysis of Undercut Slopes |
| title_sort | application of artificial neural network for stability analysis of undercut slopes |
| topic | undercut slope numerical modelling artificial neural network multi-layer perceptron model |
| url | https://ijmge.ut.ac.ir/article_77132_f6f42389bf749219d91471dd465710df.pdf |
| work_keys_str_mv | AT hassansarfaraz applicationofartificialneuralnetworkforstabilityanalysisofundercutslopes AT mohammadhosseinkhosravi applicationofartificialneuralnetworkforstabilityanalysisofundercutslopes AT thirapongpipatpongsa applicationofartificialneuralnetworkforstabilityanalysisofundercutslopes AT hassanbakhshandehamnieh applicationofartificialneuralnetworkforstabilityanalysisofundercutslopes |