IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i8p6486-d1121061.html
   My bibliography  Save this article

Classification and Zoning of Improved Materials of Weathered Redbed Soil in China Based on the Integrity of Mud Skin

Author

Listed:
  • Yi Gao

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

  • Zhen Liu

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

  • Cuiying Zhou

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

Abstract

Natural redbeds are widely distributed throughout China. Ecological restoration entails implementing measures according to the local conditions and obtaining local materials, ensuring ecological environment conservation and restoration in a scientific manner. The mud skin of weathered redbed soil is often used to control soil desertification and repair slope, and its integrity is important to measure the repairing effect. However, most of the materials used for the improvement of weathered redbed soil contain harmful chemicals, bear high costs, and aggravate environmental pollution. At present, the knowledge about different kinds of improvement materials for Chinese different zones is unclear. To solve this problem, we considered naturally weathered redbed soil as the raw material, selected 77 kinds of improved materials, and tested the integrity of the natural redbed weathered soil after adding the improved material; then, we compared it with the natural redbed weathered soil with no added materials. Then, we put forth a classification standard for the materials, discussed the driving environmental factors, formulated the suitable zoning of the materials, and defined the improvement effects of different materials on the weathered redbed soil in different regions of China. The results suggested that, for weathered redbed soil, nano water-based adhesives were most suitable for the south-western, north-western, south-eastern, north-eastern, and northern regions of China and can be widely used in other regions as well. Starch was the least suitable material for the north-western, north-eastern, and northern regions of China. The most unsuitable material for South-West China was larch tannin extract; wormwood straw was the most unsuitable for South-East China. The modified material that was not suitable for use in most zones was starch. Thus, our study provides a concrete scientific basis regarding the effectiveness of different materials in addressing natural hazards caused by weathered redbed soil in China.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6486-:d:1121061
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/8/6486/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/8/6486/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.

    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:15:y:2023:i:8:p:6486-:d:1121061. 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.