Prediction of Ground Vibration at Surface for Ring Blasting in Sublevel Stoping Through Empirical Approach, k-Nearest Neighbor, and Random Forest Model

IF 1.5 4区 工程技术 Q3 METALLURGY & METALLURGICAL ENGINEERING
Ashish Kumar Vishwakarma, Vivek Kumar Himanshu, Kaushik Dey
{"title":"Prediction of Ground Vibration at Surface for Ring Blasting in Sublevel Stoping Through Empirical Approach, k-Nearest Neighbor, and Random Forest Model","authors":"Ashish Kumar Vishwakarma, Vivek Kumar Himanshu, Kaushik Dey","doi":"10.1007/s42461-024-00976-6","DOIUrl":null,"url":null,"abstract":"<p>The accurate prediction of blast-induced ground vibration due to underground ring blasting is a prominent need for ensuring the safety of structures. Different site-specific empirical equations are available for the prediction of ground vibration. These empirical equations are best suited when the monitoring and blasting locations are present in the same medium. The change in the medium alters the behavior of wave propagation. Hence, existing empirical equations have limitations in peak particle velocity (PPV) prediction when the blasting location is an underground hard rock mine and the monitoring location is ground surface. This is because the underground metal mine comprises different levels having void in the form of excavated stope or paste-filled stope. It is very difficult to predict the magnitude of PPV on the surface in such instances. Therefore, this study has been carried out to predict the PPV at surface due to underground blasting. In this paper, PPV data was recorded at surface for 207-ring blasts. Furthermore, the PPV has also been measured at different underground locations for 47-ring blasts. Different empirical equations along with <i>k</i>-nearest neighbor (KNN) and random forest (RF) model of machine learning technique were developed for the prediction of PPV. Most of the empirical models have higher accuracy in the prediction of PPV at an underground location. This shows that scaled distance-based empirical predictors are best suited when the monitoring and blasting media are the same. However, the empirical models do not predict PPV accurately when the monitoring location is ground surface and the blast is conducted underground. The machine learning models are better suited for PPV prediction in such cases. Based on the analysis performed for the case study site, RF model predicts PPV at surface with the highest accuracy. The coefficient of determination and root mean square error for RF model used for predicting PPV at ground surface are 0.94 and 0.438 mm/s respectively. The RF-based model is also the best suited among all the models for predicting PPV at underground locations as well.</p>","PeriodicalId":18588,"journal":{"name":"Mining, Metallurgy & Exploration","volume":"216 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mining, Metallurgy & Exploration","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s42461-024-00976-6","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METALLURGY & METALLURGICAL ENGINEERING","Score":null,"Total":0}
引用次数: 0

Abstract

The accurate prediction of blast-induced ground vibration due to underground ring blasting is a prominent need for ensuring the safety of structures. Different site-specific empirical equations are available for the prediction of ground vibration. These empirical equations are best suited when the monitoring and blasting locations are present in the same medium. The change in the medium alters the behavior of wave propagation. Hence, existing empirical equations have limitations in peak particle velocity (PPV) prediction when the blasting location is an underground hard rock mine and the monitoring location is ground surface. This is because the underground metal mine comprises different levels having void in the form of excavated stope or paste-filled stope. It is very difficult to predict the magnitude of PPV on the surface in such instances. Therefore, this study has been carried out to predict the PPV at surface due to underground blasting. In this paper, PPV data was recorded at surface for 207-ring blasts. Furthermore, the PPV has also been measured at different underground locations for 47-ring blasts. Different empirical equations along with k-nearest neighbor (KNN) and random forest (RF) model of machine learning technique were developed for the prediction of PPV. Most of the empirical models have higher accuracy in the prediction of PPV at an underground location. This shows that scaled distance-based empirical predictors are best suited when the monitoring and blasting media are the same. However, the empirical models do not predict PPV accurately when the monitoring location is ground surface and the blast is conducted underground. The machine learning models are better suited for PPV prediction in such cases. Based on the analysis performed for the case study site, RF model predicts PPV at surface with the highest accuracy. The coefficient of determination and root mean square error for RF model used for predicting PPV at ground surface are 0.94 and 0.438 mm/s respectively. The RF-based model is also the best suited among all the models for predicting PPV at underground locations as well.

Abstract Image

通过经验法、k-近邻法和随机森林模型预测分层停层环形爆破的地表地面振动
准确预测地下环形爆破引起的地面振动是确保结构安全的迫切需要。目前有不同的用于预测地面振动的现场经验方程。当监测点和爆破点处于同一介质中时,这些经验方程最为适用。介质的变化会改变波的传播行为。因此,当爆破地点为地下硬岩矿井,而监测地点为地表时,现有的经验方程在预测峰值颗粒速度 (PPV) 方面存在局限性。这是因为地下金属矿由不同层面组成,其空隙形式为挖掘的斜坡或糊状填充的斜坡。在这种情况下,很难预测地表 PPV 的大小。因此,本研究对地下爆破造成的地表 PPV 进行了预测。本文记录了 207 环爆破的地表 PPV 数据。此外,还在地下不同位置测量了 47 次环形爆破的 PPV。为预测 PPV,开发了不同的经验公式以及机器学习技术的 k-nearest neighbor(KNN)和随机森林(RF)模型。大多数经验模型在地下位置预测 PPV 的准确度较高。这表明,当监测介质和爆破介质相同时,基于比例距离的经验预测模型最合适。然而,当监测地点为地面而爆破在地下进行时,经验模型无法准确预测 PPV。在这种情况下,机器学习模型更适合预测 PPV。根据对案例研究地点进行的分析,射频模型预测地表 PPV 的准确度最高。用于预测地表 PPV 的射频模型的判定系数和均方根误差分别为 0.94 和 0.438 毫米/秒。基于射频的模型也是所有模型中最适合预测地下位置 PPV 的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Mining, Metallurgy & Exploration
Mining, Metallurgy & Exploration Materials Science-Materials Chemistry
CiteScore
3.50
自引率
10.50%
发文量
177
期刊介绍: The aim of this international peer-reviewed journal of the Society for Mining, Metallurgy & Exploration (SME) is to provide a broad-based forum for the exchange of real-world and theoretical knowledge from academia, government and industry that is pertinent to mining, mineral/metallurgical processing, exploration and other fields served by the Society. The journal publishes high-quality original research publications, in-depth special review articles, reviews of state-of-the-art and innovative technologies and industry methodologies, communications of work of topical and emerging interest, and other works that enhance understanding on both the fundamental and practical levels.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信