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dc.contributor.editorTang, Chong
dc.contributor.editorPhoon, Kok-Kwang
dc.date.accessioned2025-04-08T12:47:20Z
dc.date.available2025-04-08T12:47:20Z
dc.date.issued2025
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/100668
dc.description.abstractDatabases for Data-Centric Geotechnics forms a definitive reference and guide to databases in geotechnical and rock engineering, to enhance decision-making in geotechnical practice using data-driven methods. This second volume pertains to geotechnical structures. The opening chapter presents a substantial survey of performance databases and the effectiveness of our prediction models in matching the field measurements in these databases, based on (1) full-scale field tests, (2) 39 prediction exercises organized as a part of international conferences, and (3) comparison between numerical analyses and in-situ or field measurements conducted by the French LCPC. The focus is on the evaluation of the statistical degree of confidence in predicting various of quantities of interest such as capacity and deformation. The following 18 chapters then present databases on the performance of shallow foundations, spudcan foundations, deep foundations, anchors and pipelines, retaining systems and excavations, and landslides. The databases were compiled from studies undertaken in many countries such as Australia, Belgium, Bolivia, Brazil, Canada, China, Egypt, France, Germany, Hungary, Iran, Ireland, Japan, Kenya, Malaysia, Netherlands, Norway, Poland, Portugal, South Africa, the United Kingdom and the United States. This volume on geotechnical structures is a companion to the volume on site characterization. Databases for Data-Centric Geotechnics represents the most diverse and comprehensive assembly of database research in a single publication (consisting of two volumes) to date. It follows from Model Uncertainties for Foundation Design, also published by CRC Press, and suits specialist geotechnical engineers, researchers and graduate students.en_US
dc.languageEnglishen_US
dc.relation.ispartofseriesChallenges in Geotechnical and Rock Engineeringen_US
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computationen_US
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UD Digital Lifestyle and online world: consumer and user guides::UDH E-book readers, tablets and other portable devices: consumer / user guidesen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TN Civil engineering, surveying and buildingen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TN Civil engineering, surveying and building::TNC Structural engineering::TNCC Soil and rock mechanicsen_US
dc.subject.othergeotechnical risk,georisk,artificial neural networks,numerical modelling in geotechnics,numerical modelling of soils,ISSMGE TC 304 CPT,machine learning,VSPDB,Shear-Wave Velocity,Next Generation Liquefaction,Soil Profile Database,Deep Foundation Load Test Database,DFLTD,micropile and helical pile load,Databases to Interrogate Geotechnical Observationsen_US
dc.titleDatabases for Data-Centric Geotechnicsen_US
dc.title.alternativeGeotechnical Structuresen_US
dc.typebook
oapen.identifier.doi10.1201/9781003441960en_US
oapen.relation.isPublishedBy7b3c7b10-5b1e-40b3-860e-c6dd5197f0bben_US
oapen.relation.hasChaptere6bc6dbd-2125-412f-b6de-cc78fffcb835
oapen.relation.isbn9781003441960en_US
oapen.relation.isbn9781032579108en_US
oapen.relation.isbn9781032579917en_US
oapen.imprintCRC Pressen_US


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