IDEAS home Printed from https://ideas.repec.org/a/zib/zbngbr/v4y2020i2p73-77.html
   My bibliography  Save this article

A Brief Review of the Slope Stability Analysis Methods

Author

Listed:
  • Sami Ullah

    (Faculty of Engineering, China University of Geosciences Wuhan 430074 Wuhan PR China)

  • Muhib Ullah Khan

    (School of Geosciences and Info-physics, Central South University, Changsha 410083, China)

  • Gohar Rehman

    (Faculty of Engineering, China University of Geosciences Wuhan 430074 Wuhan PR China)

Abstract

One of the most common problem faced by geotechnical engineers is slope stability assessment. The predictions of slope stability in soil or rock masses is very important for the designing of reservoir dams, roads, tunnels, excavations, open pit mines, and other engineering structures. It is the importance of slope stability problem that has reasoned alternate methods for evaluating the safety of a slope. This study reviews the existing methods used for slope stability analysis. These methods are divided into five different groups which are; (a) Limit equilibrium method, (b) Numerical simulation method, (c) Artificial neural network method, (d) Limit analysis method, and (e) Vector sum method.

Suggested Citation

  • Sami Ullah & Muhib Ullah Khan & Gohar Rehman, 2020. "A Brief Review of the Slope Stability Analysis Methods," Geological Behavior (GBR), Zibeline International Publishing, vol. 4(2), pages 73-77:4, May.
    • Handle: RePEc:zib:zbngbr:v:4:y:2020:i:2:p:73-77
    DOI: 10.26480/gbr.02.2020.73.77
    as

    Download full text from publisher

    File URL: https://geologicalbehavior.com/download/1206/
    Download Restriction: no
    File URL: https://libkey.io/10.26480/gbr.02.2020.73.77?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. P. Lu & M. Rosenbaum, 2003. "Artificial Neural Networks and Grey Systems for the Prediction of Slope Stability," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 30(3), pages 383-398, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bahri Korbi, Fadia & Ben-Slimane, Karim & Triki, Dora, 2021. "How do international joint ventures build resilience to navigate institutional crisis? The case of a Tunisian-French IJV during the Arab-Spring," Journal of Business Research, Elsevier, vol. 129(C), pages 157-168.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arunava Ray & Vikash Kumar & Amit Kumar & Rajesh Rai & Manoj Khandelwal & T. N. Singh, 2020. "Stability prediction of Himalayan residual soil slope using artificial neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 3523-3540, September.
    2. Xiuzhen Li & Jiming Kong & Zhenyu Wang, 2012. "Landslide displacement prediction based on combining method with optimal weight," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 61(2), pages 635-646, March.
    3. Min-Yuan Cheng & Nhat-Duc Hoang, 2015. "Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(3), pages 1961-1978, September.
    4. Liulei Bao & Guangcheng Zhang & Xinli Hu & Shuangshuang Wu & Xiangdong Liu, 2021. "Stage Division of Landslide Deformation and Prediction of Critical Sliding Based on Inverse Logistic Function," Energies, MDPI, vol. 14(4), pages 1-24, February.
    5. Leilei Liu & Guoyan Zhao & Weizhang Liang, 2023. "Slope Stability Prediction Using k -NN-Based Optimum-Path Forest Approach," Mathematics, MDPI, vol. 11(14), pages 1-31, July.
    6. He Jia & Sherong Zhang & Chao Wang & Xiaohua Wang & Zhonggang Ma & Yaosheng Tan, 2023. "MSC-1DCNN-based homogeneous slope stability state prediction method integrated with empirical data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 729-753, August.
    7. Jamil Amanollahi & Shahram Kaboodvandpour & Hiva Majidi, 2017. "Evaluating the accuracy of ANN and LR models to estimate the water quality in Zarivar International Wetland, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(3), pages 1511-1527, February.
    8. Shakti Suman & S. Z. Khan & S. K. Das & S. K. Chand, 2016. "Slope stability analysis using artificial intelligence techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(2), pages 727-748, November.
    9. Daxing Lei & Yaoping Zhang & Zhigang Lu & Hang Lin & Zheyuan Jiang, 2024. "Predicting Factor of Safety of Slope Using an Improved Support Vector Machine Regression Model," Mathematics, MDPI, vol. 12(20), pages 1-17, October.
    10. Yukun Yang & Wei Zhou & Izhar Mithal Jiskani & Xiang Lu & Zhiming Wang & Boyu Luan, 2023. "Slope Stability Prediction Method Based on Intelligent Optimization and Machine Learning Algorithms," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    11. Paraskevas Tsangaratos & Andreas Benardos, 2014. "Estimating landslide susceptibility through a artificial neural network classifier," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 1489-1516, December.
    12. Chong Xu & Xiwei Xu & Fuchu Dai & Zhide Wu & Honglin He & Feng Shi & Xiyan Wu & Suning Xu, 2013. "Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 883-900, September.
    13. Chonghao Zhu & Jianjing Zhang & Yang Liu & Donghua Ma & Mengfang Li & Bo Xiang, 2020. "Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 100(1), pages 173-204, January.
    14. Zaiwu Gong & Caiqin Chen & Xinming Ge, 2014. "Risk prediction of low temperature in Nanjing city based on grey weighted Markov model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(2), pages 1159-1180, March.
    15. Wei-ping Lou & Hai-yan Chen & Xin-fa Qiu & Qi-yi Tang & Feng Zheng, 2012. "Assessment of economic losses from tropical cyclone disasters based on PCA-BP," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 60(3), pages 819-829, February.
    16. Sina Shaffiee Haghshenas & Sami Shaffiee Haghshenas & Zong Woo Geem & Tae-Hyung Kim & Reza Mikaeil & Luigi Pugliese & Antonello Troncone, 2021. "Application of Harmony Search Algorithm to Slope Stability Analysis," Land, MDPI, vol. 10(11), pages 1-12, November.
    17. Arsalan Mahmoodzadeh & Mokhtar Mohammadi & Hunar Farid Hama Ali & Hawkar Hashim Ibrahim & Sazan Nariman Abdulhamid & Hamid Reza Nejati, 2022. "Prediction of safety factors for slope stability: comparison of machine learning techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 1771-1799, March.
    18. Shahram Kaboodvandpour & Jamil Amanollahi & Samira Qhavami & Bakhtiyar Mohammadi, 2015. "Assessing the accuracy of multiple regressions, ANFIS, and ANN models in predicting dust storm occurrences in Sanandaj, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(2), pages 879-893, September.
    19. Zaobao Liu & Jianfu Shao & Weiya Xu & Hongjie Chen & Yu Zhang, 2014. "An extreme learning machine approach for slope stability evaluation and prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(2), pages 787-804, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zib:zbngbr:v:4:y:2020:i:2:p:73-77. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Zibeline International Publishing (email available below). General contact details of provider: https://geologicalbehavior.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.