Artificial intelligence model for studying unconfined compressive performance of fiber-reinforced cemented paste backfill

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

“…In addition, the important parameter spread factor of RBF and GRNN is set to 5. The above parameter settings are based on relevant researches and modeling experience [20] , [36] , [37] , [38] , [39] .…”

“…CPB forms a filling body with strength and can effectively support the mined-out area. Uniaxial compressive strength (UCS) is the essential strength metric for CPB, which is affected by slurry concentration, binder content, curing time and particle size for tailings [15] , [16] , [17] , [18] , [19] , [20] . Many scholars have conducted mechanistic modeling of UCS based on different affecting factors and have achieved instructive results [21] , [22] , [23] , [24] , [25] .…”

“…In the age of artificial intelligence, many scholars have done prospective and innovative studies on predicting UCS in recent years, as detailed in Table 1 [1] , [20] , [26] , [27] , [28] , [29] , [30] , [31] . Artificial neural networks (ANN), a popular branch of machine learning technology, have been widely used in various engineering practices and modeling.…”

“… Authors Publish time Main methods Input Output Sivakugan et al [29] 2005 ANN Cement content, solids content, curing time and grain size distribution Strength Orejarena et al [31] 2010 ANN Water-cement ratios, binder composition and binder content 150d UCS Qi et al [28] 2018 PSO, ANN Tailings type, cement-tailings ratio, solids content, and curing time 3d, 7d, 28d UCS Qi et al [26] 2018 PSO Physical and chemical characteristics of tailings, cement-tailings ratio, solids content, and curing time. 7d, 28d UCS Qi et al [27] 2018 GA Physical and chemical characteristics of tailings, the cement-tailings ratio, the solids content, and the curing time 28d UCS Xiao et al [30] 2020 ANN Proportion of OPC, CG, CFA, and YS, solids content, and curing time 1d, 3d, 28d UCS Yu et al [20] 2021 ELM, SSA Cement-to-tailing mass ratio, solid mass concentration, fiber content, fiber length, and curing time 3d. 7d, 28d, and 56d UCS Li et al [1] 2021 GA, PSO, SSA Fiber properties, cement type, curing time, cement–tailings ratio and concentration 3d, 7d, 28d UCS …”

“…Besides, force majeure factors such as uneven slurry mixing and impurity mixing during the experimental process can also affect UCS. This can lead to outliers in the UCS obtained from laboratory experiments [15] , [16] , [17] , [18] , [19] , [20] . In the conditions of above problems, the weakness of traditional ANNs without data storage and memory mechanisms is exposed, which makes the accuracy of the constructed UCS prediction models also suffer.…”