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Titlebook: Environmental Issues of Blasting; Applications of Arti Ramesh M. Bhatawdekar,Danial Jahed Armaghani,Aydin Book 2021 The Author(s), under ex

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发表于 2025-3-21 19:11:36 | 显示全部楼层 |阅读模式
书目名称Environmental Issues of Blasting
副标题Applications of Arti
编辑Ramesh M. Bhatawdekar,Danial Jahed Armaghani,Aydin
视频video
概述Reviews the available AI and ML models in solving blasting environmental issues.Presents the successful use of certain AI and ML models in minimizing and controlling blasting environmental issues.Is a
丛书名称SpringerBriefs in Applied Sciences and Technology
图书封面Titlebook: Environmental Issues of Blasting; Applications of Arti Ramesh M. Bhatawdekar,Danial Jahed Armaghani,Aydin Book 2021 The Author(s), under ex
描述.This book gives a rigorous and up-to-date study of the various AI and machine learning algorithms for resolving environmental challenges associated with blasting. Blasting is a critical activity in any mining or civil engineering project for breaking down hard rock masses. A small amount of explosive energy is only used during blasting to fracture rock in order to achieve the appropriate fragmentation, throw, and development of muck pile. The surplus energy is transformed into unfavourable environmental effects such as back-break, flyrock, air overpressure, and ground vibration. The advancement of artificial intelligence and machine learning techniques has increased the accuracy of predicting these environmental impacts of blasting. This book discusses the effective application of these strategies in forecasting, mitigating, and regulating the aforementioned blasting environmental hazards..
出版日期Book 2021
关键词Blasting Operations; Environmental Issues of Blasting; Rock Fragmentations; Flyrock Distance in Blastin
版次1
doihttps://doi.org/10.1007/978-981-16-8237-7
isbn_softcover978-981-16-8236-0
isbn_ebook978-981-16-8237-7Series ISSN 2191-530X Series E-ISSN 2191-5318
issn_series 2191-530X
copyrightThe Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
The information of publication is updating

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发表于 2025-3-21 22:54:31 | 显示全部楼层
Review of Empirical and Intelligent Techniques for Evaluating Rock Fragmentation Induced by Blastinring initial era, various empirical equations were developed for predicting fragment size based on blast design parameters. During the last decade, various machine learning (ML) models such as artificial neural network and support vector machine have been proposed for prediction of rock fragmentatio
发表于 2025-3-22 01:38:58 | 显示全部楼层
Applications of AI and ML Techniques to Predict Backbreak and Flyrock Distance Resulting from Blastrameters for prediction of flyrock and backbreak. Statistical models as well as empirical equations do not have required accuracy for prediction of flyrock and backbreak. Various artificial intelligence (AI) techniques for prediction of flyrock and backbreak developed during last decade were reviewe
发表于 2025-3-22 04:46:42 | 显示全部楼层
Blast-Induced Air and Ground Vibrations: A Review of Soft Computing Techniques,based on maximum charge per delay and distance. Empirical equations or statistical methods were not accurate for prediction of these environmental issues. During last decade, various artificial intelligence and machine learning techniques such as artificial neural network, neuro-fuzzy, fuzzy logic,
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Types of Translation: An Overview,ring initial era, various empirical equations were developed for predicting fragment size based on blast design parameters. During the last decade, various machine learning (ML) models such as artificial neural network and support vector machine have been proposed for prediction of rock fragmentatio
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An Overview of Blasting Operations and Possible Techniques to Solve Environmental Issues of Blastins found the most popular and economical technique for breaking rock mass. Even though well-fragmented rock is favorable outcome of blasting, the negative environmental effects such as air over pressure (AOp), ground vibration, and flyrock have remained the matter of concern. AOp is caused by shock w
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