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Application of Machine Learning in Slope Stability Assessment

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Abstract

The failure of slopes has major consequences worldwide, including extensive damage to infrastructure and related economic losses and, in the worst case, loss of life. The ability to monitor and forecast failure is a major concern for risk management, and it is generally hindered by lack of data. Recent technological advances enable the use of multiple sources of information, such as earth observation, imagery analysis, real-time monitoring, which can be combined with theoretical deduction, numerical analysis, and machine learning techniques for monitoring, as well as forecasting the safety of engineering slopes. This chapter focuses on review of the slope stability analysis methods including the theoretical solutions, numerical simulations, physical experimentations, the in-situ monitoring methods as well as the machine learning approaches.

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Authors and Affiliations

  1. School of Civil Engineering, Chongqing University, Chongqing, China

    Zhang Wengang

  2. Chongqing University, Chongqing, China

    Liu Hanlong & Zhang Yanmei

  3. Bei**g Normal University, Zhuhai, China

    Wang Lin

  4. Chengdu University of Technology, Chengdu, China

    Zhu **ng

Authors
  1. Zhang Wengang
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  2. Liu Hanlong
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  3. Wang Lin
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  4. Zhu **ng
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  5. Zhang Yanmei
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Correspondence to Zhang Wengang .

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Wengang, Z., Hanlong, L., Lin, W., **ng, Z., Yanmei, Z. (2023). Overview. In: Application of Machine Learning in Slope Stability Assessment. Springer, Singapore. https://doi.org/10.1007/978-981-99-2756-2_1

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  • DOI: https://doi.org/10.1007/978-981-99-2756-2_1

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