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Author: search.filters.author.Ram Chandar, K.R.Subject: search.filters.subject.Numerical modeling

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    Accurate Estimation for Stability of Slope and Partition Over Old Underground Coal Workings Using Regression-Based Algorithms
    (Springer Science and Business Media Deutschland GmbH, 2022) Dorthi, K.; Kumar, A.; Ram Chandar, K.R.
    Numerical modeling simulation has found to be best solution for predicting slope and partition stability over old underground coal workings. But it has taken huge time to complete a single simulation model. In this regard, machine learning-based framework is used to predict the stability of old galleries. A case study is taken up in opencast mine and simulation is carried out using numerical model and machine learning-based framework. Framework has shown an overall accuracy of 94–95% for different slope and partition stability. Framework shows a speedup of 2366 × against numerical simulator. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Slope stability monitoring in opencast coal mine based on wireless data acquisition system-a case study
    (Science Publishing Corporation Inc ijet@sciencepubco.com, 2018) Dorthi, K.; Ram Chandar, K.R.
    Real time monitoring of slope failures is necessary to maintain the stability of slopes in open cast mines. A 3-level structure of Wireless Data Acquisition System (WDAQ) is developed for real time monitoring of slope deformation and analysis of data. In this paper, a case study is presented in a large opencast coal mine. Deformation in the slopes with slope angles of 49°, 53°, 58°, 64°, 68° and 70° is monitored over old underground workings. The deformation caused due to the external load like movement Heavy Earth Moving Machinery (HEMM). Maximum deformation was 1.57mm for slope angle of 70°. It can be observed that the deformation increased with increased slope angle. This paper also describes that the validation of data based on WDAQ with the conventional method of monitoring as well as numerical modeling. The data obtained using WDAQ is in close to other two methods. The variation is around 11%. © 2018 Authors.