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Stability analysis of rock slope based on an abstraction ant colony clustering algorithm

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Abstract

The study of the stability of rock slope is very important because its instability can cause large disasters. Because the main influence factor of the stability of rock slope is the geological environment, the engineering experience analogy method is a practical and extensively applied method. The main feature of the engineering analogy method is the cluster. Based on the analysis of a dataset of rock slope samples and using the engineering analogy method in relation to the abstraction ant colony clustering algorithm, a new method for rock slope stability analysis is proposed. Using this method, rock slopes can be automatically clustered to obtain the stability status of rock slopes in one class. Therefore, the class rating can represent the stability status of rock slopes. Some real engineering examples are used to verify the computing effect of the new algorithm. Engineering applications prove that this new algorithm can automatically estimate the stability of rock slope with high validity. Its robustness surpasses the robustness of traditional algorithms, and its application is more convenient than that of traditional algorithms. However, as a heuristic algorithm, the selection of algorithm parameters is sometimes challenging, and the computation effect for a highly complex problem is not satisfactory. Therefore, this practical method of slope stability analysis should be popularized.

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Acknowledgments

The financial supports from The Fundamental Research Funds for the Central Universities under Grant No. 2014B17814 and 2014B07014 are gratefully acknowledged.

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Authors and Affiliations

  1. Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, College of Civil and Transportation Engineering, Hohai University, 1 Xikang Road, Nanjing, 210098, People’s Republic of China

    Wei Gao

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  1. Wei Gao
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Correspondence to Wei Gao.

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Gao, W. Stability analysis of rock slope based on an abstraction ant colony clustering algorithm. Environ Earth Sci 73, 7969–7982 (2015). https://doi.org/10.1007/s12665-014-3956-4

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  • DOI: https://doi.org/10.1007/s12665-014-3956-4

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