Predicting and Optimising the Strength of Cemented Paste Fills Through Bayesian Network Model
IR@CIMFR: CSIR-Central Institute of Mining and Fuel Research, Dhanbad
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| Title |
Predicting and Optimising the Strength of Cemented Paste Fills Through Bayesian Network Model
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| Creator |
Mishra, Kanhaiya
Ghosh, C. N. Singh, Prashant Behera, S. K. Mandal , Phanil. K. |
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| Subject |
Mine Stowing and Filling
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| Description |
The techno-economic and social benefits of cemented paste backfill (CPB) resulted in its wide acceptance by the mining industry. The Ordinary Portland Cement (OPC) remains the key binder but to diminish its economical constraints, suitability of alternate binders has been examined worldwide. The present study aimed to investigate the effect of partial replacement of OPC with fly ash on the CPB’s strength and to determine the most optimal mix to achieve the required strength (1 MPa at 28 days of curing) at the most cost-effective way using the Bayesian network (BN). The CPB mixes were prepared at 72 wt.% solid concentration with mill tailings (87–91%), OPC (6–13%), and fly ash (0–4%), and instantly after mixing, fresh (slump, bleeding, density) CPB properties were measured. The strength was tested at 7, 14, 28, and 56 days of curing and initially analysed through traditional model. The traditional models follow the aleatory principle and are considered not appropriate for geotechnical engineering. Hence, the BN model was developed and tested. The reliability of two classifiers in learning model structure was compared which gives Naïve Bayes as the highest reliable tool. The CPB’s strength is most sensitive to the OPC content. The most consistent mix(s) is mill tailings: 87–88%, OPC: 9–11%, fly ash: 1–4%. Adding fly ash at 89–91wt% mill tailings possesses high failure probability of the CPB. The collinearity test indicates that the fines percentage and chemical composition of CPB’s ingredients are highly correlated with its slump, bleeding, and strength development.
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| Publisher |
Springer
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| Date |
2022-07-14
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| Type |
Article
PeerReviewed |
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| Identifier |
Mishra, Kanhaiya and Ghosh, C. N. and Singh, Prashant and Behera, S. K. and Mandal , Phanil. K. (2022) Predicting and Optimising the Strength of Cemented Paste Fills Through Bayesian Network Model. Mining, Metallurgy & Exploration.
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| Relation |
http://cimfr.csircentral.net/2515/
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