| 000 | 03428nam a22006135i 4500 | ||
|---|---|---|---|
| 001 | 978-981-16-1034-9 | ||
| 003 | DE-He213 | ||
| 005 | 20220801220040.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 210313s2021 si | s |||| 0|eng d | ||
| 020 |
_a9789811610349 _9978-981-16-1034-9 |
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| 024 | 7 |
_a10.1007/978-981-16-1034-9 _2doi |
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| 050 | 4 | _aTA703-705.4 | |
| 072 | 7 |
_aTNC _2bicssc |
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_aSCI042000 _2bisacsh |
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| 072 | 7 |
_aTNC _2thema |
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| 082 | 0 | 4 |
_a624.15 _223 |
| 100 | 1 |
_aJahed Armaghani, Danial. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _948040 |
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| 245 | 1 | 0 |
_aApplications of Artificial Intelligence in Tunnelling and Underground Space Technology _h[electronic resource] / _cby Danial Jahed Armaghani, Aydin Azizi. |
| 250 | _a1st ed. 2021. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2021. |
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| 300 |
_aIX, 70 p. 16 illus., 15 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aSpringerBriefs in Applied Sciences and Technology, _x2191-5318 |
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| 505 | 0 | _aChapter 1. An Overview of Field Classifications to Evaluate Tunnel Boring Machine Performance -- Chapter 2. Empirical, Statistical and Intelligent Techniques for TBM Performance Prediction. Chapter 3. Developing Statistical Models for Solving Tunnel Boring Machine Performance Problem -- Chapter 4. A Comparative Study of Artificial Intelligence Techniques to Estimate TBM Performance in Various Weathering Zones. | |
| 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. . | ||
| 650 | 0 |
_aEngineering geology. _94157 |
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| 650 | 0 |
_aStatistical Physics. _93234 |
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| 650 | 0 |
_aGeotechnical engineering. _94958 |
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| 650 | 0 |
_aMathematical statistics. _99597 |
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| 650 | 0 |
_aManufactures. _931642 |
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| 650 | 0 |
_aEngineering mathematics. _93254 |
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| 650 | 1 | 4 |
_aGeoengineering. _931828 |
| 650 | 2 | 4 |
_aStatistical Physics. _93234 |
| 650 | 2 | 4 |
_aGeotechnical Engineering and Applied Earth Sciences. _931829 |
| 650 | 2 | 4 |
_aMathematical Statistics. _99597 |
| 650 | 2 | 4 |
_aMachines, Tools, Processes. _931645 |
| 650 | 2 | 4 |
_aEngineering Mathematics. _93254 |
| 700 | 1 |
_aAzizi, Aydin. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _948041 |
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| 710 | 2 |
_aSpringerLink (Online service) _948042 |
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| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811610332 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811610356 |
| 830 | 0 |
_aSpringerBriefs in Applied Sciences and Technology, _x2191-5318 _948043 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-16-1034-9 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cEBK | ||
| 999 |
_c78146 _d78146 |
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