001441565 000__ 03477cam\a2200565Ii\4500
001441565 001__ 1441565
001441565 003__ OCoLC
001441565 005__ 20230309004746.0
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001441565 007__ cr\un\nnnunnun
001441565 008__ 220106s2021\\\\si\a\\\\ob\\\\000\0\eng\d
001441565 019__ $$a1291268748$$a1291288774$$a1291311673$$a1291314614
001441565 020__ $$a9789811682377$$q(electronic bk.)
001441565 020__ $$a9811682372$$q(electronic bk.)
001441565 020__ $$z9789811682360
001441565 020__ $$z9811682364
001441565 0247_ $$a10.1007/978-981-16-8237-7$$2doi
001441565 035__ $$aSP(OCoLC)1291229638
001441565 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCO$$dOCLCF$$dOCLCO$$dOCLCQ
001441565 049__ $$aISEA
001441565 050_4 $$aTA748$$b.B53 2021
001441565 08204 $$a624.1/526$$223
001441565 1001_ $$aBhatawdekar, Ramesh M.,$$eauthor.
001441565 24510 $$aEnvironmental issues of blasting :$$bapplications of artificial intelligence techniques /$$cRamesh M. Bhatawdekar, Danial Jahed Armaghani, Aydin Azizi.
001441565 264_1 $$aSingapore :$$bSpringer,$$c[2021]
001441565 264_4 $$c©2021
001441565 300__ $$a1 online resource :$$billustrations (chiefly color).
001441565 336__ $$atext$$btxt$$2rdacontent
001441565 337__ $$acomputer$$bc$$2rdamedia
001441565 338__ $$aonline resource$$bcr$$2rdacarrier
001441565 4901_ $$aSpringerBriefs in applied sciences and technology
001441565 504__ $$aIncludes bibliographical references.
001441565 5050_ $$a1. An Overview of Blasting Operations and Possible Techniques to Solve Environmental Issues of Blasting -- 2. Review of Empirical and Intelligent Techniques for Evaluating Rock Fragmentation Induced by Blasting -- 3. Applications of AI and ML Techniques to Predict Back-Break and Flyrock Distance Resulting from Blasting -- 4. Blast-Induced Air and Ground Vibrations: A Review of Soft Computing Techniques.
001441565 506__ $$aAccess limited to authorized users.
001441565 520__ $$aThis 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.
001441565 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 18, 2022).
001441565 650_0 $$aBlasting$$xEnvironmental aspects.
001441565 650_0 $$aArtificial intelligence$$xEngineering applications.
001441565 650_6 $$aIntelligence artificielle$$xApplications en ingénierie.
001441565 655_0 $$aElectronic books.
001441565 7001_ $$aArmaghani, Danial Jahed,$$eauthor.
001441565 7001_ $$aAzizi, Aydin,$$eauthor.
001441565 77608 $$iPrint version: $$z9811682364$$z9789811682360$$w(OCoLC)1280392313
001441565 830_0 $$aSpringerBriefs in applied sciences and technology.
001441565 852__ $$bebk
001441565 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-8237-7$$zOnline Access$$91397441.1
001441565 909CO $$ooai:library.usi.edu:1441565$$pGLOBAL_SET
001441565 980__ $$aBIB
001441565 980__ $$aEBOOK
001441565 982__ $$aEbook
001441565 983__ $$aOnline
001441565 994__ $$a92$$bISE