Predicting different components of blast-induced ground vibration using earthworm optimisation-based adaptive neuro-fuzzy inference system

Blasting mud displacement technology has always played a crucial role in reclamation projects. To further analyze the effectiveness and application of blasting dike construction technology in complex thick silt soft soil layers, this study first utilized the excellent linear classification and prediction capabilities of Support Vector Machines (SVM) to construct a prediction model for dike slope displacement. Additionally, the traditional blasting mud displacement process was optimized by proposing a full lateral blasting mud displacement technique aimed at achieving mud-rock displacement in thick silt soft soil layers. The full lateral blasting mud displacement technique eliminates the need for end blasting, instead using lateral fixed-point and dike widening blasting methods to accomplish the mud displacement task. In the experimental results analysis section, the performance of SVM prediction models with different kernel functions was tested. The results indicated that the SVM model with the RBF kernel had the best prediction performance, with mean squared error values for different measurement points not exceeding 0.35. By adjusting the blasting parameters at different blasting sites and comparing the settlement values at four blasting sites using the full lateral blasting mud displacement technique, it was found that the construction plan is feasible, with all settlement values remaining within reasonable ranges. This study provides a new approach for mud-rock displacement in thick silt layers using blasting mud displacement technology in dike construction.

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Chaos Game Optimization-Hybridized Artificial Neural Network for Predicting Blast-Induced Ground Vibration

Zhao,

Wang,

Cao

2024

Applied Sciences

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In this study, we introduced the chaos game optimization-artificial neural network (CGO-ANN) model as a novel approach for predicting peak particle velocity (PPV) induced by mine blasting. The CGO-ANN model is compared with other established methods, including the particle swarm optimization-artificial neural network (PSO-ANN), the genetic algorithm-artificial neural network (GA-ANN), single ANN, and the USBM empirical model. The aim is to demonstrate the superiority of the CGO-ANN model for PPV prediction. Utilizing a dataset comprising 180 blasting events from the Tonglushan Copper Mine in China, we investigated the performance of each model. The results showed that the CGO-ANN model outperforms other models in terms of prediction accuracy and robustness. This study highlights the effectiveness of the CGO-ANN model as a promising tool for PPV prediction in mining operations, contributing to safer and more efficient blasting practices.

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Predicting different components of blast-induced ground vibration using earthworm optimisation-based adaptive neuro-fuzzy inference system

Cited by 3 publications

References 44 publications

Hybrid Metaheuristic Optimization Algorithms with Least-Squares Support Vector Machine and Boosted Regression Tree Models for Prediction of Air-Blast Due to Mine Blasting

Hybrid Metaheuristic Optimization Algorithms with Least-Squares Support Vector Machine and Boosted Regression Tree Models for Prediction of Air-Blast Due to Mine Blasting

Study on the technology of blasting to squeeze silt and build embankment in deep muddy soft soil under complex environment

Chaos Game Optimization-Hybridized Artificial Neural Network for Predicting Blast-Induced Ground Vibration

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