利用有效爆破设计参数预测地面振动的先进工具

IF 1.1 4区综合性期刊 Q3 MULTIDISCIPLINARY SCIENCES Current Science Pub Date : 2022-10-10 DOI:10.18520/cs/v123/i7/887-894

J. Jain, Anurag Agrawal, B. Choudhary

{"title":"利用有效爆破设计参数预测地面振动的先进工具","authors":"J. Jain, Anurag Agrawal, B. Choudhary","doi":"10.18520/cs/v123/i7/887-894","DOIUrl":null,"url":null,"abstract":"The blasting technique is mainly used for breaking the rock mass. It is also required to control blast-induced ground vibrations for the safety of nearby habitats. This study was conducted in two different mines and 56 blast vibration data were collected from overburden benches. During trial blasts, it was confirmed that the study benches had similar geology. Analysis of blasts data was done using advanced data analysis software such as MATLAB-based artificial neural network (ANN) and Waikato Environment for Knowledge analysis (WEKA) and compared with the empirical equations. The ANN prediction model gave a significantly high R 2 = 0.92 with a low root mean square error (RMSE, 0.67), while WEKA gave a comparatively low R 2 = 0.86 with a high RMSE (1.11).","PeriodicalId":11194,"journal":{"name":"Current Science","volume":"36 1","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An advance tool to predict ground vibration using effective blast design parameters\",\"authors\":\"J. Jain, Anurag Agrawal, B. Choudhary\",\"doi\":\"10.18520/cs/v123/i7/887-894\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The blasting technique is mainly used for breaking the rock mass. It is also required to control blast-induced ground vibrations for the safety of nearby habitats. This study was conducted in two different mines and 56 blast vibration data were collected from overburden benches. During trial blasts, it was confirmed that the study benches had similar geology. Analysis of blasts data was done using advanced data analysis software such as MATLAB-based artificial neural network (ANN) and Waikato Environment for Knowledge analysis (WEKA) and compared with the empirical equations. The ANN prediction model gave a significantly high R 2 = 0.92 with a low root mean square error (RMSE, 0.67), while WEKA gave a comparatively low R 2 = 0.86 with a high RMSE (1.11).\",\"PeriodicalId\":11194,\"journal\":{\"name\":\"Current Science\",\"volume\":\"36 1\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2022-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Science\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.18520/cs/v123/i7/887-894\",\"RegionNum\":4,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Science","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.18520/cs/v123/i7/887-894","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 1

摘要

爆破技术主要用于破坏岩体。它还需要控制爆炸引起的地面振动，以确保附近栖息地的安全。该研究在两个不同的矿山进行，收集了56个覆盖层台阶的爆破振动数据。在试验爆炸中，证实了研究工作台具有相似的地质结构。利用基于matlab的人工神经网络(ANN)和Waikato Environment for Knowledge Analysis (WEKA)等先进的数据分析软件对爆炸数据进行分析，并与经验方程进行对比。ANN预测模型的r2 = 0.92，均方根误差(RMSE, 0.67)较低，而WEKA预测模型的r2 = 0.86较低，RMSE较高(1.11)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

An advance tool to predict ground vibration using effective blast design parameters

The blasting technique is mainly used for breaking the rock mass. It is also required to control blast-induced ground vibrations for the safety of nearby habitats. This study was conducted in two different mines and 56 blast vibration data were collected from overburden benches. During trial blasts, it was confirmed that the study benches had similar geology. Analysis of blasts data was done using advanced data analysis software such as MATLAB-based artificial neural network (ANN) and Waikato Environment for Knowledge analysis (WEKA) and compared with the empirical equations. The ANN prediction model gave a significantly high R 2 = 0.92 with a low root mean square error (RMSE, 0.67), while WEKA gave a comparatively low R 2 = 0.86 with a high RMSE (1.11).

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Current Science 综合性期刊-综合性期刊

CiteScore

1.50

自引率

10.00%

发文量

审稿时长

3 months

期刊介绍： Current Science, published every fortnight by the Association, in collaboration with the Indian Academy of Sciences, is the leading interdisciplinary science journal from India. It was started in 1932 by the then stalwarts of Indian science such as CV Raman, Birbal Sahni, Meghnad Saha, Martin Foster and S.S. Bhatnagar. In 2011, the journal completed one hundred volumes. The journal is intended as a medium for communication and discussion of important issues that concern science and scientific activities. Besides full length research articles and shorter research communications, the journal publishes review articles, scientific correspondence and commentaries, news and views, comments on recently published research papers, opinions on scientific activity, articles on universities, Indian laboratories and institutions, interviews with scientists, personal information, book reviews, etc. It is also a forum to discuss issues and problems faced by science and scientists and an effective medium of interaction among scientists in the country and abroad. Current Science is read by a large community of scientists and the circulation has been continuously going up. Current Science publishes special sections on diverse and topical themes of interest and this has served as a platform for the scientific fraternity to get their work acknowledged and highlighted. Some of the special sections that have been well received in the recent past include remote sensing, waves and symmetry, seismology in India, nanomaterials, AIDS, Alzheimer''s disease, molecular biology of ageing, cancer, cardiovascular diseases, Indian monsoon, water, transport, and mountain weather forecasting in India, to name a few. Contributions to these special issues ‘which receive widespread attention’ are from leading scientists in India and abroad.