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        Chapter 6 New laboratory database of hydraulic conductivity measurements on fine-grained soils

        Proposal review

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        Author(s)
        Feng, Shuyin
        Vardanega, Paul J.
        Language
        English
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        Abstract
        Databases for Data-Centric Geotechnicsforms a definitive reference and guide to databases in geotechnical and rock engineering, to enhance decision-making in geotechnical practice using data-driven methods. This first volume pertains to site characterization. The opening chapter presents an in-depth analysis of site data attributes, including the establishment of a new taxonomy of site data under “4S” (site generalizations, spatial features, sampling characteristics, and smart data) to provide a novel agenda for data-driven site characterization. Type 3 machine learning methods (disruptive value) are possible as sensors become more pervasive and more intelligent. A comprehensive overview of site characterization information is also presented with a focus on its availability, coverage, value to decision making, and challenges. The remaining 13 chapters cover databases of soil and rock properties and the application of these databases to rock socket behavior, rock classification, settlement on soft marine clays, permeability of fine-grained soils, and liquefaction among others. The databases were compiled from studies undertaken in many countries including Austria, Australia, Brazil, Canada, China, France, Finland, Germany, India, Iran, Japan, Korea, Malaysia, Mexico, New Zealand, Norway, Singapore, Sweden, Thailand, the United Kingdom, and the United States. This volume on site characterization is a companion to the volume on geotechnical structures. 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.
        Book
        Databases for Data-Centric Geotechnics
        URI
        https://library.oapen.org/handle/20.500.12657/104309
        Keywords
        geotechnical risk,ground investigation,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
        10.1201/9781003441946-6
        ISBN
        9781003441946, 9781032578958, 9781032579887
        Publisher
        Taylor & Francis
        Publisher website
        https://taylorandfrancis.com/
        Publication date and place
        2025
        Grantor
        • University of Bristol
        Imprint
        CRC Press
        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
        Pages
        20
        Rights
        https://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        • 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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