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深部开采充填体稳定性及与岩体智能匹配研究.[D].周士霖.中南大学.2012, 02
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基于聚类的煤岩分界图像识别技术研究.[D].黄韶杰.中国矿业大学(北京).2016, 07
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Integrated and intelligent design framework for cemented paste backfill: A combination of robust machine learning modelling and multi-objective optimization.[J].Chongchong Qi;Qiusong Chen;S. Sonny Kim.Minerals Engineering.2020,
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Flocculation-dewatering prediction of fine mineral tailings using a hybrid machine learning approach.[J].Chongchong Qi;Hai-Bang Ly;Qiusong Chen;Tien-Thinh Le;Vuong Minh Le;Binh Thai Pham.Chemosphere.2020, C
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Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill.[J].Enming Li;Jian Zhou;Xiuzhi Shi;Danial Jahed Armaghani;Zhi Yu;Xin Chen;Peisheng Huang.Engineering with Computers.2020, 4
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Pressure drops of fresh cemented paste backfills through coupled test loop experiments and machine learning techniques.[J].Chongchong Qi;Qiusong Chen;Xiangjian Dong;Qinli Zhang;Zaher Mundher Yaseen.Powder Technology.2020,
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A Paste Slurry Mass Prediction Numerical Model for Backfilling Coal Mining.[J].W. Lv;X. Du;K. Guo.Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería.2020,
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A Hybrid Artificial Intelligence Model for Predicting the Strength of Foam-Cemented Paste Backfill.[J].Jingping Qiu;Zhenbang Guo;Long Li;Shiyu Zhang;Yingliang Zhao;Zhengyu Ma.IEEE Access.2020,