- Title
- Intelligent techniques for prediction of drilling rate for percussive drills in topically weathered limestone
- Creator
- Bhatawdekar, Ramesh; Roy, Bishwajit; Changtham, Saksarid; Khandelwal, Manoj; Armaghani, Danial; Mohamad, Edy; Pathak, Pranjal; Mondal, Subhrojit; Kumar, Radhikesh; Azlan, Mohd
- Date
- 2022
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/189033
- Identifier
- vital:17360
- Identifier
-
https://doi.org/10.1007/978-981-16-9770-8_29
- Identifier
- ISBN:2366-2557 (ISSN); 9789811697692 (ISBN)
- Abstract
- Physico-mechanical properties of rocks have a direct correlation with the drilling rate of percussive drill. The prediction of drilling rate is important for the deployment of drills during the planning stage. In tropical climatic regions, limestone is classified as blocky, very blocky, blocky/ seamy and disintegrated based on the degree of weathering. Weathering of limestone takes place very rapidly in tropical (wet) climatic regions. Previous researchers have correlated different individual rock mass properties with rate of drilling. However, single property of limestone is not adequate to correlate with the drilling rate. In this study, sensitivity analysis of different properties of weathered limestone was carried out with respect to drilling rate. Rock density, rock quality designation (RQD), geological strength index (GSI), point load index (PLI) and Schmidt hammer rebound number (SHRN) were identified as crucial input parameters. 113 data sets were collected with the foregoing five input parameters and the output parameter as drilling rate of percussive drills. Data was analysed with multi variable regression analysis (MVRA) which showed R2 value as 0.54. Artificial neural network (ANN) has been widely used for solving various engineering problems. On the other hand, optimization problems are solved by the Biogeography Based Optimization (BBO) model. Further this data was analysed with a hybrid intelligent model namely BBO- ANN. The R2 values for training data set and testing data set 0.638 and 0.761 respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- International Conference on Geotechnical challenges in Mining, Tunneling and Underground structures, ICGMTU 2021, Virtual, online, 20-21 December 2021, Lecture Notes in Civil Engineering Vol. 228, p. 457-471
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022, The Author(s)
- Subject
- Artificial neural network (ANN); Black box optimisation (BBO); Geological strength index (GSI); Multivariable regression analysis (MVRA); Point load index (PLI); Rock quality designation (RQD); Schmidt hammer rebound number (SHRN)
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