Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill

Qi, Chongchong; Fourie, Andy; Chen, Qiusong

doi:10.1016/j.conbuildmat.2017.11.006

Cited by 226 publications

(91 citation statements)

References 15 publications

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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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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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“…Figure shows a typical ANN with 2 hidden layers. Artificial neural network has been widely used in geomechanic problems with a large number of influencing variables …”

Section: Individual Ai Techniques and The Classifier Ensemblesmentioning

confidence: 99%

A hybrid ensemble method for improved prediction of slope stability

Tang

2018

Num Anal Meth Geomechanics

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Accurate prediction of slope stability is a significant issue in geomechanics with many artificial intelligence (AI) techniques being utilised. However, the application of AI has not reached its full potential because of the lack of more robust algorithms. In this paper, we proposed a hybrid ensemble method for the improved prediction of slope stability using classifier ensembles and genetic algorithm. Gaussian process classification, quadratic discriminant analysis, support vector machine, artificial neural networks, adaptive boosted decision trees, and k-nearest neighbours were chosen to be individual AI techniques, and the weighted majority voting was used as the combination method. Validation method was chosen to be the 10-fold cross-validation, and performance measures were selected to be the accuracy, the receiver operating characteristic curve, and the area under the receiver operating characteristic curve (AUC). Grid search and genetic algorithm were used for the hyperparameter tuning and weight tuning respectively. The results show that the proposed hybrid ensemble method has great potential in improving the prediction of slope stability. Compared with individual classifiers, the optimum ensemble classifier achieved the highest AUC value (0.943) and the highest accuracy (0.902) on the testing set, denoting that the predictive performance has been improved. The optimum ensemble classifier with the Youden's cutoff was recommended for slope stability prediction with respect to the AUC value, the accuracy, the true positive rate, and the true negative rate. This research indicates that the use of the classifier ensembles, rather than the search for the ideal individual classifiers, might help for the slope stability prediction.

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“…The change in the peak compressive strength of CTB at different loading rates was studied, showing a power function relationship. Qi et al [16][17][18] proposed an intelligent method for predicting the UCS of CPB, constructed a constitutive model and a strength prediction model for CPB, and analyzed the hydration reaction mechanism of cement in CPB. Liu et al [19][20][21] studied the early hydration heat, hydration mechanism, and kinetic parameters of CPB by isothermal calorimetry, focused on the effect of the tailings-cement ratio (TCR) on hydration heat, and established a prediction model for paste composition and rheological properties.…”

Section: Introductionmentioning

confidence: 99%

Preparation of a New Type of Cemented Paste Backfill with an Alkali-Activated Silica Fume and Slag Composite Binder

Sun

2020

Materials

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A new type of cemented paste backfill (CPB) was prepared using sodium hydroxide (NaOH) as the activator, slag and silica fume (SF) as the binder, and tailings as the aggregate. The effects of proportion of replacement of 0%, 5%, 10%, 15%, and 20% silica fume on the properties of CPB were studied. The strength formation mechanism of CPB was explored through a combination of scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), and Fourier transform infrared (FTIR) spectroscopy. The SEM images were analyzed by IMAGE J software, and the porosity of CPB with different silica fume contents was obtained. The results show that as the amount of silica fume increases, the unconfined compressive strength (UCS) increases first and then decreases. When the amount of silica fume was approximately 5%, CPB with a larger UCS can be obtained. When the silica fume content increased from 0% to 5%, because silica fume has good activity and small particles, more calcium silicate hydrate (C–S–H) gels and Mg-Al type layered double hydrotalcites (LDHs) were generated in CPB, which made it denser and improved its strength compared with the non-silica fume group. C–S–H gels were the main source of CPB strength. With a further increase in the amount of silica fume, thaumasite produced inside of CPB, reducing the content of C–S–H gels. Moreover, due to the expansion of thaumasite, it is easy to generate a large number of micro cracks in CPB, which weakens the strength of CPB.

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Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill

Cited by 226 publications

References 15 publications

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

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

A hybrid ensemble method for improved prediction of slope stability

Preparation of a New Type of Cemented Paste Backfill with an Alkali-Activated Silica Fume and Slag Composite Binder

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