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Predicting and Optimising the Strength of Cemented Paste Fills Through Bayesian Network Model
Mining, Metallurgy & Exploration ( IF 1.5 ) Pub Date : 2022-07-14 , DOI: 10.1007/s42461-022-00650-9
Kanhaiya Mishra , P. S. Paul , C. N. Ghosh , Prashant Singh , S. K. Behera , Phanil. K. Mandal

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.

Graphical abstract



中文翻译:

通过贝叶斯网络模型预测和优化水泥膏体填充强度

胶结膏体回填(CPB)的技术经济和社会效益使其被采矿业广泛接受。普通硅酸盐水泥 (OPC) 仍然是主要粘合剂,但为了减少其经济限制,替代粘合剂的适用性已在全球范围内进行了研究。本研究旨在研究用粉煤灰部分替代 OPC 对 CPB 强度的影响,并确定以最具成本效益的方式达到所需强度(固化 28 天时 1 MPa)的最佳混合贝叶斯网络 (BN)。CPB 混合物以 72 wt.% 的固体浓度与磨尾矿 (87-91%)、OPC (6-13%) 和粉煤灰 (0-4%) 混合,并在混合后立即新鲜(坍落度、渗色, 密度) 测量 CPB 性能。强度在7、14、28、和56天的固化,并通过传统模型进行初步分析。传统模型遵循偶然原则,被认为不适合岩土工程。因此,BN 模型被开发和测试。比较了学习模型结构中两个分类器的可靠性,得出朴素贝叶斯是最可靠的工具。CPB 的强度对 OPC 内容最为敏感。最一致的混合物是磨尾矿:87–88%,OPC:9–11%,飞灰:1–4%。在 89-91wt% 的磨机尾矿中添加粉煤灰具有较高的 CPB 失效概率。共线性检验表明,CPB成分的细粉百分比和化学成分与其坍落度、渗色和强度发展高度相关。传统模型遵循偶然原则,被认为不适合岩土工程。因此,BN 模型被开发和测试。比较了学习模型结构中两个分类器的可靠性,得出朴素贝叶斯是最可靠的工具。CPB 的强度对 OPC 内容最为敏感。最一致的混合物是磨尾矿:87–88%,OPC:9–11%,飞灰:1–4%。在 89-91wt% 的磨机尾矿中添加粉煤灰具有较高的 CPB 失效概率。共线性检验表明,CPB成分的细粉百分比和化学成分与其坍落度、渗色和强度发展高度相关。传统模型遵循偶然原则,被认为不适合岩土工程。因此,BN 模型被开发和测试。比较了学习模型结构中两个分类器的可靠性,得出朴素贝叶斯是最可靠的工具。CPB 的强度对 OPC 内容最为敏感。最一致的混合物是磨尾矿:87–88%,OPC:9–11%,飞灰:1–4%。在 89-91wt% 的磨机尾矿中添加粉煤灰具有较高的 CPB 失效概率。共线性检验表明,CPB成分的细粉百分比和化学成分与其坍落度、渗色和强度发展高度相关。比较了学习模型结构中两个分类器的可靠性,得出朴素贝叶斯是最可靠的工具。CPB 的强度对 OPC 内容最为敏感。最一致的混合物是磨尾矿:87–88%,OPC:9–11%,飞灰:1–4%。在 89-91wt% 的磨机尾矿中添加粉煤灰具有较高的 CPB 失效概率。共线性检验表明,CPB成分的细粉百分比和化学成分与其坍落度、渗色和强度发展高度相关。比较了学习模型结构中两个分类器的可靠性,得出朴素贝叶斯是最可靠的工具。CPB 的强度对 OPC 内容最为敏感。最一致的混合物是磨尾矿:87–88%,OPC:9–11%,飞灰:1–4%。在 89-91wt% 的磨机尾矿中添加粉煤灰具有较高的 CPB 失效概率。共线性检验表明,CPB成分的细粉百分比和化学成分与其坍落度、渗色和强度发展高度相关。

图形概要

更新日期:2022-07-15
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