Upgrading the prediction of jet grouting column diameter using deep learning with an emphasis on high energies

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/114409
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Title: Upgrading the prediction of jet grouting column diameter using deep learning with an emphasis on high energies
Authors: Díaz Castañeda, Esteban | Tomás, Roberto
Research Group/s: Ingeniería del Terreno y sus Estructuras (InTerEs)
Center, Department or Service: Universidad de Alicante. Departamento de Ingeniería Civil
Keywords: Column diameter | Deep learning | Ground improvement | Jet grouting | Neural networks
Knowledge Area: Ingeniería del Terreno
Issue Date: May-2021
Publisher: Springer Nature
Citation: Acta Geotechnica. 2021, 16: 1627-1633. https://doi.org/10.1007/s11440-020-01091-8
Abstract: This article proposed a new method to estimate the diameter of jet grouting columns. The method uses the largest data collection of column diameters measured to date and includes a large amount of new data that fills the existing gap of data for high injection energies. The dataset was analysed using a deep neural network that took into account the problem’s key parameters (i.e. type of soil, soil resistance, type of jet and specific energy in the nozzle). As a result, three different neural networks were selected, one for each type of jet, according to the errors and consistency associated with each. Finally, using the trained networks, a number of design charts were developed to determine the diameter of a jet grouting column as a function of the soil properties and the jet system. These charts allow generating an optimal jet grouting design, improving the prediction of the diameter of jet columns especially in the high energy triple fluid.
Sponsor: This work was supported by the Spanish Ministry of Economy and Competitiveness, the State Agency of Research and the European Funds for Regional Development under project TEC2017-85244-C2-1-P.
URI: http://hdl.handle.net/10045/114409
ISSN: 1861-1125 (Print) | 1861-1133 (Online)
DOI: 10.1007/s11440-020-01091-8
Language: eng
Type: info:eu-repo/semantics/article
Rights: © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Peer Review: si
Publisher version: https://doi.org/10.1007/s11440-020-01091-8
Appears in Collections:INV - INTERES - Artículos de Revistas

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