Towards Intelligent Mining for Backfill: A genetic programming-based method for strength forecasting of cemented paste backfill
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- @Article{QI:2019:ME,
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author = "Chongchong Qi and Xiaolin Tang and Xiangjian Dong and
Qiusong Chen and Andy Fourie and Enyan Liu",
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title = "Towards Intelligent Mining for Backfill: A genetic
programming-based method for strength forecasting of
cemented paste backfill",
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journal = "Minerals Engineering",
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volume = "133",
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pages = "69--79",
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year = "2019",
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keywords = "genetic algorithms, genetic programming, Cemented
paste backfill, Uniaxial compressive strength,
Forecasting, Intelligent Mining for Backfill",
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ISSN = "0892-6875",
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DOI = "doi:10.1016/j.mineng.2019.01.004",
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URL = "http://www.sciencedirect.com/science/article/pii/S0892687519300056",
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abstract = "As cemented paste backfill (CPB) plays an increasingly
important role in minerals engineering, forecasting its
mechanical properties becomes a necessity for efficient
CPB design. Machine learning (ML) techniques have
previously demonstrated remarkable successes in such
task by providing black-box predictions. To express the
non-linear relationship in an explicit and precise way,
we employed genetic programming (GP) for the uniaxial
compressive strength (UCS) prediction of CPB. The
influence of sampling method, training set size and
maximum tree depth on the GP performance was
investigated. A detailed analysis was conducted on a
representative GP model and the relative variable
importance was investigated using the relative variable
frequency, partial dependence plots and relative
importance scores. The statistical parameters show that
a satisfactory performance was obtained by the GP
modelling (R2a >a 0.80 on the testing set). Results of
this study indicate that cement-tailings ratio, solids
content and curing time were the most three important
variables for the UCS prediction. The predictive
performance of GP modelling was comparable to
well-recognised ML techniques, and the trained GP model
can be generalised to entirely new tailings with
satisfactory performance. This study indicates that the
GP-based method is capable of providing explicit and
precise forecasting of UCS, which can serve as a
reliable tool for quick, inexpensive and effective
assessment of UCS in the absence of adequate
experimental data",
- }
Genetic Programming entries for
Chongchong Qi
Xiaolin Tang
Xiangjian Dong
Qiusong Chen
Andy Fourie
Enyan Liu
Citations