Machine learning data centric geotechnics (10 résultats)

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  • Langue : anglais

    Edité par CRC Press, 2026

    1032886544 / 9781032886541

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    Etat : New.

  • Langue : anglais

    Edité par CRC Press, 2026

    1032886544 / 9781032886541

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    Vendeur : Majestic Books, Hounslow, Royaume-UniMajestic Books

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    EUR 198,57

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    Etat : New.

  • Langue : anglais

    Edité par CRC Press, 2026

    1032886544 / 9781032886541

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    Vendeur : Biblios, frankfurt am main, HESSE, AllemagneBiblios

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    EUR 196,90

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  • Langue : anglais

    Edité par Taylor and Francis Ltd, 2026

    1032886544 / 9781032886541

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    Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-UniPBShop.store UK

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    Etat: Neuf

    EUR 270,30

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    HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.

  • Langue : anglais

    Edité par CRC Press, 2026

    1032886544 / 9781032886541

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    Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books

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    Etat: Neuf

    EUR 286,10

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    Etat : New.

  • Langue : anglais

    Edité par CRC Press, 2026

    1032886544 / 9781032886541

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    Vendeur : moluna, Greven, Allemagnemoluna

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    Etat : New. Kok-Kwang Phoon is President designate of Singapore University of Technology and Design. He has edited or written several books with CRC Press, including Model Uncertainties in Foundation Design. He was awarded the ASCE Norman Medal twice in 2005 .

  • Langue : anglais

    Edité par CRC Press Aug 2026, 2026

    1032886544 / 9781032886541

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    Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH

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    EUR 362,30

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    Buch. Etat : Neu. Neuware - Machine learning and other digital technologies fed with large datasets offer a major set of tools for practical geotechnical design. Large language models and other generative AIs can perform cognitive tasks currently undertaken by humans -- and might even predict the next event based on some time series. This depends on a balance of data centricity, fit-for (and transform) practice, and geotechnical context, and can be achieved by the integration of information, data, techniques, tools, perspectives, concepts, theories, along with experience from both geotechnical engineering and machine learning in computer science. And yet good engineering and research outcomes are still dependent on how practice (which includes the workforce) is improved or even transformed in the longer term to better serve end-users. This collection of focused chapters from a group of specialists presents principles and broad up to date practice of machine learning, along with a number of example areas of site characterization, design and construction in geotechnics.This book is essential for sophisticated practitioners as well as graduate students.…

  • Langue : anglais

    Edité par Taylor & Francis Ltd, London, 2026

    1032886544 / 9781032886541

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    Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-UnisGrand Eagle Retail

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    Hardcover. Etat : new. Hardcover. Machine learning and other digital technologies fed with large datasets offer a major set of tools for practical geotechnical design. Large language models and other generative AIs can perform cognitive tasks currently undertaken by humans -- and might even predict the next event based on some time series. This depends on a balance of data centricity, fit-for (and transform) practice, and geotechnical context, and can be achieved by the integration of information, data, techniques, tools, perspectives, concepts, theories, along with experience from both geotechnical engineering and machine learning in computer science. And yet good engineering and research outcomes are still dependent on how practice (which includes the workforce) is improved or even transformed in the longer term to better serve end-users. This collection of focused chapters from a group of specialists presents principles and broad up to date practice of machine learning, along with a number of example areas of site characterization, design and construction in geotechnics.This book is essential for sophisticated practitioners as well as graduate students. This collection of chapters from specialists presents principles and practices of machine learning, along with a number of example areas of site characterization, design and construction in geotechnics.This book is essential for sophisticated practitioners as well as graduate student. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

  • Langue : anglais

    Edité par Taylor & Francis Ltd, London, 2026

    1032886544 / 9781032886541

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    Vendeur : CitiRetail, Stevenage, Royaume-UniCitiRetail

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    Etat: Neuf

    EUR 180,83

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    Hardcover. Etat : new. Hardcover. Machine learning and other digital technologies fed with large datasets offer a major set of tools for practical geotechnical design. Large language models and other generative AIs can perform cognitive tasks currently undertaken by humans -- and might even predict the next event based on some time series. This depends on a balance of data centricity, fit-for (and transform) practice, and geotechnical context, and can be achieved by the integration of information, data, techniques, tools, perspectives, concepts, theories, along with experience from both geotechnical engineering and machine learning in computer science. And yet good engineering and research outcomes are still dependent on how practice (which includes the workforce) is improved or even transformed in the longer term to better serve end-users. This collection of focused chapters from a group of specialists presents principles and broad up to date practice of machine learning, along with a number of example areas of site characterization, design and construction in geotechnics.This book is essential for sophisticated practitioners as well as graduate students. This collection of chapters from specialists presents principles and practices of machine learning, along with a number of example areas of site characterization, design and construction in geotechnics.This book is essential for sophisticated practitioners as well as graduate student. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

  • Langue : anglais

    Edité par Taylor & Francis Ltd, London, 2026

    1032886544 / 9781032886541

    • Couverture rigide
    • impression à la demande

    Vendeur : AussieBookSeller, Truganina, VIC, AustralieAussieBookSeller

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    Etat: Neuf

    EUR 234,89

    EUR 32,63 expédition 
    Expédition depuis Australie vers Etats-Unis

    Quantité disponible : 1 disponible

    Hardcover. Etat : new. Hardcover. Machine learning and other digital technologies fed with large datasets offer a major set of tools for practical geotechnical design. Large language models and other generative AIs can perform cognitive tasks currently undertaken by humans -- and might even predict the next event based on some time series. This depends on a balance of data centricity, fit-for (and transform) practice, and geotechnical context, and can be achieved by the integration of information, data, techniques, tools, perspectives, concepts, theories, along with experience from both geotechnical engineering and machine learning in computer science. And yet good engineering and research outcomes are still dependent on how practice (which includes the workforce) is improved or even transformed in the longer term to better serve end-users. This collection of focused chapters from a group of specialists presents principles and broad up to date practice of machine learning, along with a number of example areas of site characterization, design and construction in geotechnics.This book is essential for sophisticated practitioners as well as graduate students. This collection of chapters from specialists presents principles and practices of machine learning, along with a number of example areas of site characterization, design and construction in geotechnics.This book is essential for sophisticated practitioners as well as graduate student. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…