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http://dx.doi.org/10.7474/TUS.2021.31.6.508

Prediction of Blast Vibration in Quarry Using Machine Learning Models  

Jung, Dahee (Department of Energy Resources Engineering, Pukyong National University)
Choi, Yosoon (Department of Energy Resources Engineering, Pukyong National University)
Publication Information
Tunnel and Underground Space / v.31, no.6, 2021 , pp. 508-519 More about this Journal
Abstract
In this study, a model was developed to predict the peak particle velocity (PPV) that affects people and the surrounding environment during blasting. Four machine learning models using the k-nearest neighbors (kNN), classification and regression tree (CART), support vector regression (SVR), and particle swarm optimization (PSO)-SVR algorithms were developed and compared with each other to predict the PPV. Mt. Yogmang located in Changwon-si, Gyeongsangnam-do was selected as a study area, and 1048 blasting data were acquired to train the machine learning models. The blasting data consisted of hole length, burden, spacing, maximum charge per delay, powder factor, number of holes, ratio of emulsion, monitoring distance and PPV. To evaluate the performance of the trained models, the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were used. The PSO-SVR model showed superior performance with MAE, MSE and RMSE of 0.0348, 0.0021 and 0.0458, respectively. Finally, a method was proposed to predict the degree of influence on the surrounding environment using the developed machine learning models.
Keywords
Blasting; Peak particle velocity; Ground vibration; Machine learning;
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