Dump slope stability analysis using artificial intelligence

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Authors

  • Gagan Gupta
     ,IN
  • Sanjay K. Sharma
     ,IN
  • GSP Singh
     ,IN

DOI:

https://doi.org/10.18311/jmmf/2022/30445

Keywords:

Dump slope stability, industry 4.0, numerical modelling, artificial intelligence

Abstract

The fourth industrial revolution has introduced several rapidly progressive technologies like big data analytics, the internet of things, simulation, autonomous robots, cloud computing and artificial intelligence. It has reduced the effort and processing time in the manufacturing/production industry through new emerging technologies. In this study, artificial intelligence has been adopted to forecast the stability of multi bench dump slope structure with ease and minimum interval. The supervised machine learning methodbased Decision Tree, Gradient Boosting, Multi-variate Nonlinear, Random Forest and Support Vector Machine soft computing models are deployed to assess the dump slope stability. Numerical modelling has been used to generate error-free datasets for the training and testing of models. Hyperparameter tuning has been done to optimize the performance of the machine learning models. The performance of the models has been analyzed based on the Coefficient of Determination and the Root Mean Square error. The study outcomes reveal that the Multivariate Nonlinear regression model predicts the stability of dump slope structure with better accuracy for the considered datasets. It yields a coefficient of determination of 95.4%, while the root mean square error is only 4.6%.

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Published

2022-06-10

How to Cite

Gupta, G., Sharma, S. K., & Singh, G. (2022). Dump slope stability analysis using artificial intelligence. Journal of Mines, Metals and Fuels, 70(3), 129–135. https://doi.org/10.18311/jmmf/2022/30445

Issue

Volume 70, Issue 3, March 2022

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC
Received 2022-06-10
Accepted 2022-06-10
Published 2022-06-10

 

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