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Stability analysis of road slope cut in cemented conglomerate based on DEM, PSO, and GIM

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Abstract

Landslides in conglomerate deposits are a common geological hazard in road engineering. Accurately obtaining the mechanical parameters of conglomerates and scientifically analyzing slope stability are important to ensure the safe construction and operation of road projects. To this end, this study conducted large-scale horizontal push-shear tests on loess, weakly cemented conglomerates, and cemented conglomerates based on specific projects. The particle swarm optimization algorithm was introduced using the granular discrete element method to intelligently invert the model parameters of the stratum. The gravity increase method was used to analyze slope stability. The calculation results show that the instability of the excavated multilevel slope occurs mainly in the weakly cemented conglomerate layer; its stability factor is 0.78, which is consistent with the field survey results. The slope stability is improved through adjustments in the excavation rate of the weakly cemented conglomerate slope, thus effectively guiding engineering practice. This study provides a reference for the acquisition of the mechanical parameters of coarse-grained rock and soil and the analyses of slope stability.

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Funding

This work is supported by the National Natural Science Foundation of China (nos. 42107201 and 41972300), the China Postdoctoral Science Foundation (2020M680625), the Fundamental Research Funds for the Central Universities of China (no. 292019072), and the China Scholarship Council.

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Authors and Affiliations

  1. School of Engineering and Technology, China University of Geosciences (Beijing), Beijing, 100083, China

    Hanxun Wang, Bin Zhang, Yadong Zhou, Jin Zhou & Hu Xing

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  1. Hanxun Wang
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  2. Bin Zhang
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  3. Yadong Zhou
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  4. Jin Zhou
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  5. Hu Xing
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Correspondence to Hanxun Wang.

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Wang, H., Zhang, B., Zhou, Y. et al. Stability analysis of road slope cut in cemented conglomerate based on DEM, PSO, and GIM. Bull Eng Geol Environ 81, 387 (2022). https://doi.org/10.1007/s10064-022-02888-5

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  • DOI: https://doi.org/10.1007/s10064-022-02888-5

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