Articles liés à Machine Learning for Data-centric Geotechnics
Synopsis
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 transformative) 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 broader 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 student.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l'auteur
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 and 2020, and is the Founding Editor of Georisk.
Chong Tang is a Professor of Dalian University of Technology in China. He was awarded ASCE's Norman Medal in 2020.
Zi-Jun Cao is Professor at Southwest Jiaotong University, China. He received the GEOSNet Young Researcher Award in 2022 and ISSMGE Bright Spark Lecture Award in 2019.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Résultats de recherche pour Machine Learning for Data-centric Geotechnics
Image d'archives
Machine Learning for Data-Centric Geotechnics (Hardcover)
Edité par
Taylor & Francis Ltd, London, 2026
ISBN 10 : 1032886544
ISBN 13 : 9781032886541
Neuf
Couverture rigide
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
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. N° de réf. du vendeur 9781032886541
Acheter neuf
EUR 182,05
Livraison gratuite
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : 1 disponible(s)
Image d'archives
Machine Learning for Data-Centric Geotechnics (Challenges in Geotechnical and Rock Engineering)
Vendeur : Books Puddle, Woodside, NY, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. N° de réf. du vendeur 26405233983
Acheter neuf
EUR 192,28
Expédition à EUR 3,52
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : 4 disponible(s)
Image d'archives
Machine Learning for Data-Centric Geotechnics (Challenges in Geotechnical and Rock Engineering)
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. N° de réf. du vendeur 18405233973
Acheter neuf
EUR 196,90
Expédition à EUR 9,95
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 4 disponible(s)
Image d'archives
Machine Learning for Data-Centric Geotechnics (Challenges in Geotechnical and Rock Engineering)
Vendeur : Majestic Books, Hounslow, Royaume-Uni
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. N° de réf. du vendeur 407953120
Acheter neuf
EUR 200,82
Expédition à EUR 7,58
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : 4 disponible(s)
Image d'archives
Machine Learning for Data-Centric Geotechnics (Hardcover)
Edité par
Taylor & Francis Ltd, London, 2026
ISBN 10 : 1032886544
ISBN 13 : 9781032886541
Neuf
Couverture rigide
Vendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
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. N° de réf. du vendeur 9781032886541
Acheter neuf
EUR 180,83
Expédition à EUR 43,16
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image d'archives
Machine Learning for Data-Centric Geotechnics (Hardcover)
Edité par
Taylor & Francis Ltd, London, 2026
ISBN 10 : 1032886544
ISBN 13 : 9781032886541
Neuf
Couverture rigide
Vendeur : AussieBookSeller, Truganina, VIC, Australie
Évaluation du vendeur 5 sur 5 étoiles
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. N° de réf. du vendeur 9781032886541
Acheter neuf
EUR 234,89
Expédition à EUR 32,63
Expédition depuis Australie vers Etats-Unis
Expédition depuis Australie vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image d'archives
Machine Learning for Data-Centric Geotechnics
Edité par
Taylor and Francis Ltd, 2026
ISBN 10 : 1032886544
ISBN 13 : 9781032886541
Neuf
Couverture rigide
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9781032886541
Acheter neuf
EUR 270,30
Expédition à EUR 7,89
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image d'archives
Machine Learning for Data-Centric Geotechnics (Challenges in Geotechnical and Rock Engineering)
Vendeur : California Books, Miami, FL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur I-9781032886541
Acheter neuf
EUR 286,10
Livraison gratuite
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Machine Learning for Data-Centric Geotechnics
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
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 . N° de réf. du vendeur 2731061489
Acheter neuf
EUR 258,33
Expédition à EUR 48,99
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Edition internationale
MACHINE LEARNING FOR DATACENTRIC GEOTECHNICS (HB 2026)
Edité par
TAYLOR & FRANCIS NP EXCLUSIVE(CBS), 2026
ISBN 10 : 1032886544
ISBN 13 : 9781032886541
Neuf
Couverture rigide
Vendeur : UK BOOKS STORE, London, LONDO, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. Brand New ! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability. N° de réf. du vendeur Cvs 9781032886541
Acheter neuf
EUR 341,40
Expédition à EUR 4,37
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : 13 disponible(s)