IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i6p3367-d770293.html
   My bibliography  Save this article

Early Risk Warning of Highway Soft Rock Slope Group Using Fuzzy-Based Machine Learning

Author

Listed:
  • Cuiying Zhou

    (School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
    Guangdong Engineering Research Centre for Major Infrastructure Safety, Guangzhou 510275, China)

  • Jinwu Ouyang

    (School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
    Guangdong Engineering Research Centre for Major Infrastructure Safety, Guangzhou 510275, China)

  • Zhen Liu

    (School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
    Guangdong Engineering Research Centre for Major Infrastructure Safety, Guangzhou 510275, China)

  • Lihai Zhang

    (Department of Infrastructure Engineering, The University of Melbourne, Melbourne 3010, Australia)

Abstract

Maintaining the stability of highway soft rock slopes is of critical importance for ensuring the safety of road networks. Although much research has been carried out to assess the stability of individual soft rock slope, the goal of efficient and effective risk management focusing on multiple highway soft rock slopes has not been fully achieved due to the many complex factors involved and the interactions among these factors. In the present study, a machine learning algorithm based on a fuzzy neural network (FNN) and a comprehensive evaluation method based on the FNN is developed, in order to identify and issue early warnings regarding the risks induced by soft rock slopes along highways, in an efficient and effective way. Using a large amount of collected soft rock slope information as training and validation data, an FNN-based risk identification model is first developed to identify the risk level of individual soft rock slope based on the meteorological conditions, topographical and geomorphological factors, geotechnical properties, and the measured horizontal displacement. An FNN-based comprehensive evaluation method is then developed, in order to quantify the risk level of a soft rock slope group according to the slope, road and external factors. The results show that the risk level identification accuracy obtained based on validation of the FNN model was higher than 90%, and the model showed a good training effect. On this basis, we further made early warnings of the risks of soft rock slope groups. The proposed early-warning model can quickly and accurately evaluate the risk posed by multiple soft rock slopes to a highway, thereby ensuring the safety of the highway.

Suggested Citation

  • Cuiying Zhou & Jinwu Ouyang & Zhen Liu & Lihai Zhang, 2022. "Early Risk Warning of Highway Soft Rock Slope Group Using Fuzzy-Based Machine Learning," Sustainability, MDPI, vol. 14(6), pages 1-28, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3367-:d:770293
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/6/3367/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/6/3367/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dunwen Liu & Chao Liu & Yu Tang & Chun Gong, 2022. "A GA-BP Neural Network Regression Model for Predicting Soil Moisture in Slope Ecological Protection," Sustainability, MDPI, vol. 14(3), pages 1-14, January.
    2. Jules Maurice Habumugisha & Ningsheng Chen & Mahfuzur Rahman & Md Monirul Islam & Hilal Ahmad & Ahmed Elbeltagi & Gitika Sharma & Sharmina Naznin Liza & Ashraf Dewan, 2022. "Landslide Susceptibility Mapping with Deep Learning Algorithms," Sustainability, MDPI, vol. 14(3), pages 1-22, February.
    3. Chia Yu Huat & Seyed Mohammad Hossein Moosavi & Ahmed Salih Mohammed & Danial Jahed Armaghani & Dmitrii Vladimirovich Ulrikh & Masoud Monjezi & Sai Hin Lai, 2021. "Factors Influencing Pile Friction Bearing Capacity: Proposing a Novel Procedure Based on Gradient Boosted Tree Technique," Sustainability, MDPI, vol. 13(21), pages 1-23, October.
    4. Martin Bednarik & Işık Yilmaz & Marian Marschalko, 2012. "Landslide hazard and risk assessment: a case study from the Hlohovec–Sered’ landslide area in south-west Slovakia," 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. 64(1), pages 547-575, October.
    5. George Gaprindashvili & Cees Westen, 2016. "Generation of a national landslide hazard and risk map for the country of Georgia," 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. 80(1), pages 69-101, January.
    6. Reza Nassirzadeh Goorchi & Mehdi Amini & Hossein Memarian, 2018. "A new rating system approach for risk analysis of rock slopes," 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. 91(1), pages 75-102, April.
    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. Gongfa Chen & Wei Deng & Mansheng Lin & Jianbin Lv, 2023. "Slope stability analysis based on convolutional neural network and digital twin," 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(2), pages 1427-1443, September.
    2. Yi Gao & Zhen Liu & Cuiying Zhou, 2023. "Classification and Zoning of Improved Materials of Weathered Redbed Soil in China Based on the Integrity of Mud Skin," Sustainability, MDPI, vol. 15(8), pages 1-18, April.

    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. Nisar Ali Shah & Muhammad Shafique & Muhammad Ishfaq & Kamil Faisal & Mark Van der Meijde, 2023. "Integrated Approach for Landslide Risk Assessment Using Geoinformation Tools and Field Data in Hindukush Mountain Ranges, Northern Pakistan," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    2. Netra Bhandary & Ranjan Dahal & Manita Timilsina & Ryuichi Yatabe, 2013. "Rainfall event-based landslide susceptibility zonation mapping," 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. 69(1), pages 365-388, October.
    3. Zohre Hoseinzade & Asal Zavarei & Kourosh Shirani, 2021. "Application of prediction–area plot in the assessment of MCDM methods through VIKOR, PROMETHEE II, and permutation," 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. 109(3), pages 2489-2507, December.
    4. Jae-Hyeon Park & Seong-Gyun Park & Hyun Kim, 2022. "Applicability Evaluation of Landslide Vulnerability Criteria for Decision on Landcreep-Vulnerable Areas in South Korea," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    5. Kamila Hodasová & Martin Bednarik, 2021. "Effect of using various weighting methods in a process of landslide susceptibility assessment," 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. 105(1), pages 481-499, January.
    6. Esteban Bravo-López & Tomás Fernández Del Castillo & Chester Sellers & Jorge Delgado-García, 2023. "Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods," Land, MDPI, vol. 12(6), pages 1-28, May.
    7. Haoyuan Hong & Himan Shahabi & Ataollah Shirzadi & Wei Chen & Kamran Chapi & Baharin Bin Ahmad & Majid Shadman Roodposhti & Arastoo Yari Hesar & Yingying Tian & Dieu Tien Bui, 2019. "Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods," 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. 96(1), pages 173-212, March.
    8. Juraj Holec & Martin Bednarik & Marián Šabo & Jozef Minár & Isik Yilmaz & Marián Marschalko, 2013. "A small-scale landslide susceptibility assessment for the territory of Western Carpathians," 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. 69(1), pages 1081-1107, October.
    9. Lingfan Ju & Huan Yu & Qing Xiang & Wenkai Hu & Xiaoyu Xu, 2023. "Spatial Coupling Pattern and Driving Forces of Rural Settlements and Arable Land in Alpine Canyon Region of the Maoxian County, China," IJERPH, MDPI, vol. 20(5), pages 1-18, February.
    10. G. Sakkas & I. Misailidis & N. Sakellariou & V. Kouskouna & G. Kaviris, 2016. "Modeling landslide susceptibility in Greece: a weighted linear combination approach using analytic hierarchical process, validated with spatial and statistical analysis," 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(3), pages 1873-1904, December.
    11. Mohamad Ghozali Hassan* & Che AzlanTaib & Muslim Akanmu & Afif Ahmarofi, 2018. "A Theoretical Review on the Preventive Measures to Landslide Disaster Occurrences in Penang State, Malaysia," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 753-759:6.
    12. Krishna Devkota & Amar Regmi & Hamid Pourghasemi & Kohki Yoshida & Biswajeet Pradhan & In Ryu & Megh Dhital & Omar Althuwaynee, 2013. "Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya," 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. 65(1), pages 135-165, January.
    13. Aihua Wei & Kaining Yu & Fenggang Dai & Fuji Gu & Wanxi Zhang & Yu Liu, 2022. "Application of Tree-Based Ensemble Models to Landslide Susceptibility Mapping: A Comparative Study," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    14. Guangyun Gao & Shaofeng Yao & Yujun Cui & Qingsheng Chen & Xianlin Zhang & Kewen Wang, 2018. "Zoning of confined aquifers inrush and quicksand in Shanghai region," 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. 91(3), pages 1341-1363, April.
    15. Han Zhang & Chao Yin & Shaoping Wang & Bing Guo, 2023. "Landslide susceptibility mapping based on landslide classification and improved convolutional neural networks," 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. 116(2), pages 1931-1971, March.
    16. Hubert Szczepaniuk & Edyta Karolina Szczepaniuk, 2022. "Applications of Artificial Intelligence Algorithms in the Energy Sector," Energies, MDPI, vol. 16(1), pages 1-24, December.
    17. Xinyue Ke & Ni Wang & Long Yu & Zihan Guo & Tianming He, 2023. "Spatial Distribution of Water Risk Based on Atlas Compilation in the Shaanxi Section of the Qinling Mountains, China," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    18. Xiaohua Ding & Mehdi Jamei & Mahdi Hasanipanah & Rini Asnida Abdullah & Binh Nguyen Le, 2023. "Optimized Data-Driven Models for Prediction of Flyrock due to Blasting in Surface Mines," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    19. Cahio Guimarães Seabra Eiras & Juliana Ribeiro Gonçalves de Souza & Renata Delicio Andrade de Freitas & César Falcão Barella & Tiago Martins Pereira, 2021. "Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition 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. 107(2), pages 1427-1442, June.
    20. Aditi Singh & Shilpa Pal & D. P. Kanungo, 2021. "An integrated approach for landslide susceptibility–vulnerability–risk assessment of building infrastructures in hilly regions of India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5058-5095, April.

    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:gam:jsusta:v:14:y:2022:i:6:p:3367-:d:770293. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.