Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Different Nature-Inspired Optimization Algorithms and Deep Neural Network     

Nguyen  Hoang  Bui  Xuan-Nam  Tran  Quang-Hieu  Nguyen  Dinh-An  Hoa  Le Thi Thu  Le  Qui-Thao  Giang  Le Thi Huong 《Natural Resources Research》2021,30(6):4695-4717

Natural Resources Research - Blast-induced ground vibration (GV) is a hazardous phenomenon in open-pit mines, and it has unquestionable effects, such as slope instability, deformation of...  相似文献   

Predicting Blast-induced Ground Vibration in Quarries Using Adaptive Fuzzy Inference Neural Network and Moth–Flame Optimization     

Bui  Xuan-Nam  Nguyen  Hoang  Tran  Quang-Hieu  Nguyen  Dinh-An  Bui  Hoang-Bac 《Natural Resources Research》2021,30(6):4719-4734

Natural Resources Research - Blasting is a first preparatory stage that plays a fundamental role in the subsequent operations of an open pit mine. However, its adverse effects can seriously affect...  相似文献   

Estimation of Blast-Induced Air Overpressure in Quarry Mines Using Cubist-Based Genetic Algorithm     

Fang  Qiancheng  Nguyen  Hoang  Bui  Xuan-Nam  Tran  Quang-Hieu 《Natural Resources Research》2020,29(2):593-607

In the present work, blast-induced air overpressure is estimated by an innovative intelligence system based on the cubist algorithm (CA) and genetic algorithm (GA) with high accuracy, called GA–CA model. Herein, CA initialization model was developed first and the hyper-parameters of the CA model were selected randomly. Subsequently, the GA procedure was applied to perform a global search for the optimized values of the hyper-factors of the CA model. Root-mean-square error (RMSE) is utilized as a compatibility function to determine the optimal CA model with the lowest RMSE. Gaussian process (GP), conditional inference tree (CIT), principal component analysis (PCA), hybrid neural fuzzy inference system (HYFIS) and k-nearest neighbor (k-NN) models are also developed as the benchmark models in order to compare and analyze the quality of the proposed GA–CA algorithm; 164 blasting works were investigated at a quarry mine of Vietnam for this aim. The results revealed that GA significantly improved the performance of the CA model. Based on the statistical indices used for model assessment, the proposed GA–CA model was confirmed as the most superior model as compared to the other models (i.e., GP, CIT, HYFIS, PCA, k-NN). It can be applied as a robust soft computing tool for estimating blast-induced air overpressure.

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Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost     

Zhang  Xiliang  Nguyen  Hoang  Bui  Xuan-Nam  Tran  Quang-Hieu  Nguyen  Dinh-An  Bui  Dieu Tien  Moayedi  Hossein 《Natural Resources Research》2020,29(2):711-721

Natural Resources Research - Blasting is a useful technique for rocks fragmentation in open-pit mines, underground mines, as well as for civil engineering work. However, the negative impacts of...  相似文献   

Prediction of Blast-Induced Ground Vibration Intensity in Open-Pit Mines Using Unmanned Aerial Vehicle and a Novel Intelligence System     

Bui  Xuan-Nam  Choi  Yosoon  Atrushkevich  Victor  Nguyen  Hoang  Tran  Quang-Hieu  Long  Nguyen Quoc  Hoang  Hung-Thang 《Natural Resources Research》2020,29(2):771-790

Natural Resources Research - Predicting and reducing blast-induced ground vibrations is a common concern among engineers and mining enterprises. Dealing with these vibrations is a challenging issue...  相似文献   

A Novel Artificial Intelligence Approach to Predict Blast-Induced Ground Vibration in Open-Pit Mines Based on the Firefly Algorithm and Artificial Neural Network     

Shang  Yonghui  Nguyen  Hoang  Bui  Xuan-Nam  Tran  Quang-Hieu  Moayedi  Hossein 《Natural Resources Research》2020,29(2):723-737

Natural Resources Research - The primary purpose of this study was to develop a novel hybrid artificial intelligence model, with a robust performance, to predict ground vibration induced by bench...  相似文献