The Optimization of Cemented Hydraulic Backfill Mixture Design Parameters for Different Strength Conditions Using Artificial Intelligence Algorithms

Sadrossadat, Ehsan; Başarır, Hakan; Karrech, Ali; Durham, Richard; Fourie, Andy; Han, Bin

doi:10.1007/978-3-030-33954-8_28

Cited by 6 publications

(2 citation statements)

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“…In contrast, the MOPSO can handle multiple functions. Therefore, MPSO can obtain a set of optimal solutions, i.e., Pareto solutions [35,47]. MOPSO can quickly converge to the Pareto front for its practical searchability.…”

Section: Multi-objective Pso Optimizationmentioning

confidence: 99%

“…Additionally, metaheuristic algorithms are also widely applied in mine blasting optimization, e.g., the grasshopper optimization algorithm (GOA) is employed to identify the blasting solution that minimizes dust generation [34]. Meanwhile, multi-objective particle swarm optimization (MOPSO), developed from PSO, is capable of handling multiple metrics in mining applications [35].…”

Section: Introductionmentioning

confidence: 99%

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Prediction and Optimization of Blasting-Induced Ground Vibration in Open-Pit Mines Using Intelligent Algorithms

Guo

Zhao

Ping-feng³

2023

Applied Sciences

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Prediction and parameter optimization are effective methods for mine personnel to control blast-induced ground vibration. However, the challenge of effective prediction and optimization lies in the multi-factor and multi-effect nature of open-pit blasting. This study proposes a hybrid intelligent model to predict ground vibrations using a least-squares support vector machine (LSSVM) optimized by a particle swarm algorithm (PSO). Meanwhile, multi-objective particle swarm optimization (MOPSO) was used to optimize the blast design parameters by considering the vibration of particular areas and the bulk rate of blast fragmentation. To compare the prediction performance of PSO-LSSVM, a genetic-algorithm-optimized BP neural network (GA-BP), unoptimized LSSVM, and BP were used, by applying the same database. In addition, the root-mean-squared error (RMSE), the mean absolute error (MAE), and the correlation coefficient (r) were regarded as the evaluation indicators. Furthermore, the optimization results of the blasting parameters were obtained by quoting the established vibration prediction model and bulk rate proxy model in MOPSO and verified by field tests. The results indicated that the PSO-LSSVM model provided the highest efficiency in predicting vibrations with an RMSE of 1.954, MAE of 1.717, and r of 0.965. Furthermore, the blasting vibration can be controlled by using the two-objective optimization model to obtain the best blasting parameters. Consequently, this study can provide more specific recommendations for vibration hazard control.

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Section: Multi-objective Pso Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%