Towards Intelligent Mining for Backfill: A genetic programming-based method for strength forecasting of cemented paste backfill

Qi, Chongchong; Tang, Xiaolin; Dong, Xiangjian; Chen, Qiusong; Fourie, Andy; Liu, Enyan

doi:10.1016/j.mineng.2019.01.004

Cited by 91 publications

(38 citation statements)

References 32 publications

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“…The cemented paste backfill (CPB) technology is a promising method for the safe disposal of mine tailings. Moreover, benefits like reclaiming water, stabilising the rock mass and thus increasing mining safety, and increasing ore recovery rate promote the broad application of CPB worldwide [2][3][4][5][6][7][8][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Understanding Cement Hydration of Cemented Paste Backfill: DFT Study of Water Adsorption on Tricalcium Silicate (111) Surface

Liü

et al. 2019

Minerals

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Understanding cement hydration is of crucial importance for the application of cementitious materials, including cemented paste backfill. In this work, the adsorption of a single water molecule on an M3-C 3 S (111) surface is investigated using density functional theory (DFT) calculations. The adsorption energies for 14 starting geometries are calculated and the electronic properties of the reaction are analysed. Two adsorption mechanisms, molecular adsorption and dissociative adsorption, are observed and six adsorption configurations are found. The results indicate that spontaneous dissociative adsorption is energetically favored over molecular adsorption. Electrons are transferred from the surface to the water molecule during adsorption. The density of states (DOS) reveals the bonding mechanisms between water and the surface. This study provides an insight into the adsorption mechanism at an atomic level, and can significantly promote the understanding of cement hydration within such systems.

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Section: Introductionmentioning

confidence: 99%

Understanding Cement Hydration of Cemented Paste Backfill: DFT Study of Water Adsorption on Tricalcium Silicate (111) Surface

Liü

et al. 2019

Minerals

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“…Qi and Fourie [11] summarized recent progress in CPB design, with particular emphasis on flocculation and sedimentation, CPB mix design and CPB pipe transport, and envisaged a future in which the CPB design is optimized in an integrated CPB design system, accelerated by artificial intelligence and interpreted using atomic simulation. Qi [12][13][14] proposed a strength prediction model integrating boosted regression trees (BRT) and particle swarm optimization (PSO) and thought that more efficient reuse of waste tailings as CPB can be achieved by reducing the required number of mechanical experiments during engineering applications. At the same time, an intelligent modelling framework for the mechanical properties prediction using machine learning (ML) algorithms and genetic algorithm (GA) was proposed [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Prediction Model of Strength of Paste Backfill Prepared from Waste-Unclassified Tailings

Zhang

2019

Advances in Materials Science and Engineering

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Paste backfilling is an important support for the development of green mines and deep mining. It can effectively reduce a series of risks of underground goaf and surface tailings ponds. Reasonable strength of backfill is an effective guarantee for controlling ground stress and realizing safe mining function. Under the combination of complex materials and local conditions, ensuring the optimal design and effective proportion for paste backfill strength is the bottleneck problem that restricts the safety, economy, and efficiency of filling mining. The strength developing trend of paste backfilling prepared from waste rock and unclassified tailings has been studied. Different levels of cement contents, tailings-waste ratios, and slurry concentrations were investigated through orthogonal design to obtain the relationship between the UCS and the multi-influential factors. Combined with the experimental results and the previous strength prediction models, the waste rock-unclassified tailings paste strength prediction model was proposed. Introducing the water-cement ratio, the cement-tailings ratio, the amount of cement, and the packing density that characterizing the overall gradation of unclassified tailings and waste rock, as well as the curing time, a strength prediction model of multifactors was developed. Moreover, the microscopic structure of the paste prepared from waste-unclassified tailings was analyzed with an Environment Scanning Electron Microscope (ESEM), and the influence mechanism was ascertained. The weight coefficient of strength development is carded in this paper, and the strength model of unclassified tailings-waste paste considering five factors is obtained, which is of great significance to guide the mining engineering.

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“…The obtained results appeared in point form for each instance, with analyses of values of each feature from the grid and corresponding predictions. On the other hand, a PDP is an alternative approach to analyzing the dependence of the predicted output versus input variables [163]. The average of all ICE plots gives the PDP, which represents the influence of the corresponding input to the output for the whole dataset.…”

Section: Sensitivity Analysis Using Ice and Pdp Conceptsmentioning

confidence: 99%

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

et al. 2020

Sustainability

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Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

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Towards Intelligent Mining for Backfill: A genetic programming-based method for strength forecasting of cemented paste backfill

Cited by 91 publications

References 32 publications

Understanding Cement Hydration of Cemented Paste Backfill: DFT Study of Water Adsorption on Tricalcium Silicate (111) Surface

Understanding Cement Hydration of Cemented Paste Backfill: DFT Study of Water Adsorption on Tricalcium Silicate (111) Surface

A Novel Prediction Model of Strength of Paste Backfill Prepared from Waste-Unclassified Tailings

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

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