Multivariate Adaptive Regression Splines (MARS) approach to blast-induced ground vibration prediction

被引：53

作者：

Arthur, Clement Kweku ^{[1
]}

Temeng, Victor Amoako ^{[1
]}

Ziggah, Yao Yevenyo ^{[2
]}

机构：

[1] Univ Mines & Technol, Fac Mineral Resources Technol, Dept Min Engn, Tarkwa, Ghana

[2] Univ Mines & Technol, Fac Mineral Resources Technol, Dept Geomat Engn, Tarkwa, Ghana

来源：

INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT | 2020年 / 34卷 / 03期

关键词：

Multivariate Adaptive Regression Splines; artificial neural network; blast-induced ground vibration; peak particle velocity; ARTIFICIAL NEURAL-NETWORK; PEAK PARTICLE-VELOCITY; INTELLIGENT APPROACH; LOGISTIC-REGRESSION; MODEL; MINE; AIR; FEASIBILITY; FREQUENCY;

D O I：

10.1080/17480930.2019.1577940

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Modelling and prediction of blast-induced ground vibration is a significant aspect of mining and civil engineering operations, as ground vibration has dire consequences on both the environment, mine production and successful implementation of engineering projects. This study proposes the Multivariate Adaptive Regression Splines (MARS) as a novel alternative technique to model and predict blast-induced ground vibration. The MARS approach was compared with three artificial neural network methods and four conventional ground vibration predictors. The statistical analyses revealed that the MARS produced the best performance and can successfully be used for the prediction of blast-induced ground vibration.

引用

页码：198 / 222

页数：25

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