期刊
APPLIED SCIENCES-BASEL
卷 10, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/app10030869
关键词
ground vibration; prediction; machine learning; feature selection; environmental issue of blasting
类别
资金
- National Natural Science Foundation Project of China [41807259]
- State Key Laboratory of Safety and Health for Metal Mines [2017-JSKSSYS-04]
- Project of Changsha Science and Technology Project [kc1809012]
- Project for the Hunan Social Science Results Review Committee [XSP18YBC343]
- Innovation-Driven Project of Central South University [2020CX040]
- Research Foundation of Education Bureau of Hunan Province, China [18A381, 18A295]
In mining and civil engineering applications, a reliable and proper analysis of ground vibration due to quarry blasting is an extremely important task. While advances in machine learning led to numerous powerful regression models, the usefulness of these models for modeling the peak particle velocity (PPV) remains largely unexplored. Using an extensive database comprising quarry site datasets enriched with vibration variables, this article compares the predictive performance of five selected machine learning classifiers, including classification and regression trees (CART), chi-squared automatic interaction detection (CHAID), random forest (RF), artificial neural network (ANN), and support vector machine (SVM) for PPV analysis. Before conducting these model developments, feature selection was applied in order to select the most important input parameters for PPV. The results of this study show that RF performed substantially better than any of the other investigated regression models, including the frequently used SVM and ANN models. The results and process analysis of this study can be utilized by other researchers/designers in similar fields.
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