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001435170 001__ 1435170
001435170 003__ OCoLC
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001435170 019__ $$a1242026223$$a1244630239$$a1249944377
001435170 020__ $$a9789811610349$$q(electronic bk.)
001435170 020__ $$a9811610347$$q(electronic bk.)
001435170 020__ $$z9789811610332
001435170 020__ $$z9811610339
001435170 0247_ $$a10.1007/978-981-16-1034-9$$2doi
001435170 035__ $$aSP(OCoLC)1243540934
001435170 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dGW5XE$$dYDX$$dOCLCO$$dOCLCF$$dLEATE$$dOCLCQ$$dOCLCO$$dCOM$$dOCLCQ
001435170 049__ $$aISEA
001435170 050_4 $$aTA815
001435170 08204 $$a624.1/93$$223
001435170 1001_ $$aArmaghani, Danial Jahed,$$eauthor.
001435170 24510 $$aApplications of artificial intelligence in tunnelling and underground space technology /$$cDanial Jahed Armaghani, Aydin Azizi.
001435170 264_1 $$aSingapore :$$bSpringer,$$c[2021]
001435170 300__ $$a1 online resource (78 pages)
001435170 336__ $$atext$$btxt$$2rdacontent
001435170 337__ $$acomputer$$bc$$2rdamedia
001435170 338__ $$aonline resource$$bcr$$2rdacarrier
001435170 4901_ $$aSpringerBriefs in applied sciences and technology
001435170 504__ $$aIncludes bibliographical references.
001435170 5050_ $$aIntro -- About This Book -- Contents -- About the Authors -- 1 An Overview of Field Classifications to Evaluate Tunnel Boring Machine Performance -- 1.1 Introduction -- 1.2 Tunnel Boring Machine -- 1.2.1 Brief History of TBM -- 1.2.2 Types and Basic Principles of TBM -- 1.2.3 TBM Performance Parameters -- 1.2.4 Factors Influencing TBM Performance -- 1.3 TBM Prediction Field Classifications -- 1.4 TBM Performance Prediction Using Field Approach -- 1.5 RMCs Used in TBM Performance Prediction -- 1.6 Discussion and Conclusion -- References
001435170 5058_ $$a2 Empirical, Statistical, and Intelligent Techniques for TBM Performance Prediction -- 2.1 Introduction -- 2.2 Theoretical Models -- 2.2.1 Cutter Load Approach -- 2.2.2 Specific Energy Approach -- 2.3 Empirical Models -- 2.4 Statistical Approach -- 2.5 Computational-Based Techniques -- 2.6 Discussion and Conclusion -- References -- 3 Developing Statistical Models for Solving Tunnel Boring Machine Performance Problem -- 3.1 Introduction -- 3.2 Regression-Based Models -- 3.2.1 Linear Multiple Regression (LMR) -- 3.2.2 Non-linear Multiple Regression (NLMR) -- 3.3 Case Study
001435170 5058_ $$a3.4 Data Measurement and Input Variables -- 3.4.1 Rock Material Properties -- 3.4.2 Rock Mass Properties -- 3.4.3 Machine Characteristics -- 3.4.4 Input Variables -- 3.5 Regression-Based Models -- 3.5.1 Simple Regression -- 3.5.2 Multiple Regression -- 3.6 Discussion and Conclusion -- References -- 4 A Comparative Study of Artificial Intelligence Techniques to Estimate TBM Performance in Various Weathering Zones -- 4.1 Introduction -- 4.2 Methodology -- 4.2.1 Artificial Neural Network (ANN) -- 4.2.2 Group Method of Data Handling (GMDH) -- 4.3 Tunnel Site and Data Collection
001435170 5058_ $$a4.4 GMDH Model Development -- 4.5 Model Assessment and Discussion -- 4.6 Conclusions -- References
001435170 506__ $$aAccess limited to authorized users.
001435170 520__ $$aThis book covers the tunnel boring machine (TBM) performance classifications, empirical models, statistical and intelligent-based techniques which have been applied and introduced by the researchers in this field. In addition, a critical review of the available TBM performance predictive models will be discussed in details. Then, this book introduces several predictive models i.e., statistical and intelligent techniques which are applicable, powerful and easy to implement, in estimating TBM performance parameters. The introduced models are accurate enough and they can be used for prediction of TBM performance in practice before designing TBMs.
001435170 588__ $$aDescription based on print version record.
001435170 650_0 $$aTunneling$$xEquipment and supplies.
001435170 650_0 $$aArtificial intelligence$$xEngineering applications.
001435170 650_6 $$aTunnels$$xConception et construction$$xAppareils et matériel.
001435170 650_6 $$aIntelligence artificielle$$xApplications en ingénierie.
001435170 655_0 $$aElectronic books.
001435170 7001_ $$aAzizi, Aydin,$$eauthor.
001435170 77608 $$iPrint version:$$aJahed Armaghani, Danial.$$tApplications of Artificial Intelligence in Tunnelling and Underground Space Technology.$$dSingapore : Springer Singapore Pte. Limited, ©2021$$z9789811610332
001435170 830_0 $$aSpringerBriefs in applied sciences and technology.
001435170 852__ $$bebk
001435170 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-1034-9$$zOnline Access$$91397441.1
001435170 909CO $$ooai:library.usi.edu:1435170$$pGLOBAL_SET
001435170 980__ $$aBIB
001435170 980__ $$aEBOOK
001435170 982__ $$aEbook
001435170 983__ $$aOnline
001435170 994__ $$a92$$bISE