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        Databases for Data-Centric Geotechnics

        Proposal review

        Geotechnical Structures

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        Contributor(s)
        Tang, Chong (editor)
        Phoon, Kok-Kwang (editor)
        Language
        English
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        Abstract
        Databases 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.
        URI
        https://library.oapen.org/handle/20.500.12657/100668
        Keywords
        geotechnical 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 Observations
        DOI
        10.1201/9781003441960
        ISBN
        9781003441960, 9781032579108, 9781032579917
        Publisher
        Taylor & Francis
        Publisher website
        https://taylorandfrancis.com/
        Publication date and place
        2025
        Imprint
        CRC Press
        Series
        Challenges in Geotechnical and Rock Engineering,
        Classification
        Mathematical theory of computation
        E-book readers, tablets and other portable devices: consumer / user guides
        Civil engineering, surveying and building
        Soil and rock mechanics
        Chapters in this book
        • Chapter 10 The DINGO database of axial pile–load tests for the United Kingdom
        • Chapter 6 New laboratory database of hydraulic conductivity measurements on fine-grained soils
        Rights
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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