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Axioms
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Recent Developments in Numerical Methods, Machine Learning Techniques, and Quantum...
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Recent Developments in Numerical Methods, Machine Learning Techniques, and Quantum Computing for Geoscience Applications

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 2504

Special Issue Editors

Dr. Hongkyu Yoon

E-Mail Website
Guest Editor
Sandia National Laboratories, New Mexico P.O. Box 5800, Albuquerque, NM, USA
Interests: machine learning; multiphysics modeling; geomechanics
Dr. Hamid Nick

E-Mail Website
Guest Editor
Centre for Oil and Gas, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
Interests: coupled processes; uncertainty quantification; subsurface monitoring
Dr. Teeratorn Kadeethum

E-Mail Website
Guest Editor
Sandia National Laboratories, P.O. Box 5800, Albuquerque, NM, USA
Interests: finite element method; machine learning

Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent advancements in numerical methods, machine learning techniques, and quantum computing for geoscience research. 

Topics of interest include, but are not limited to, (1) newly developed finite difference, finite volume, finite element methods, or a combination among these, focusing on locally conservative methods, higher-order approximations, multi-scale multi-dimensional modeling, or dynamics mesh adaptivity; (2) machine learning algorithms and applications for model reduction, optimization, inverse problems, uncertainty quantification, highly parameterized problems (e.g., the parametrization of heterogeneous fields), and efficient dimensionality reduction of nonlinear operators; and (3) quantum computing applications in geoscience research; for instance, seismic inversion with quantum annealing, quantum-computational hydrologic inverse analysis, or quantum optimization.

We hope that this Special Issue will bring together researchers working on both fundamental and applied numerical methods, machine learning, and quantum computing. This would benefit the broader geoscience community.

Dr. Hongkyu Yoon
Dr. Hamid Nick
Dr. Teeratorn Kadeethum
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • geosicence
  • numerical methods
  • machine learning
  • quantum computing

Published Papers (1 paper)

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Research

15 pages, 409 KiB  
Article
An Energy-Based Big Data Framework to Estimate the Young’s Moduli of the Soils Drilled during the Execution of Continuous Flight Auger Piles
by Luan Carlos de Sena Monteiro Ozelim, Darym Júnior Ferrari de Campos, André Luís Brasil Cavalcante and Jose Camapum de Carvalho
Axioms 2023, 12(4), 340; https://doi.org/10.3390/axioms12040340 - 31 Mar 2023
Viewed by 1302
Abstract
Understanding the soil mass and how it behaves is determinant to the quality and reliability of a foundation design. Normally, such behavior is predicted based on laboratory and in situ tests. In the big data era, instead of executing more tests, engineers should [...] Read more.
Understanding the soil mass and how it behaves is determinant to the quality and reliability of a foundation design. Normally, such behavior is predicted based on laboratory and in situ tests. In the big data era, instead of executing more tests, engineers should understand how to take advantage of ordinary execution procedures to obtain the parameters of interest. Sensors are key components in engineering big data frameworks, as they provide a large number of valuable measured data. In particular, the building process (excavation and concreting) of continuous flight auger piles (CFAPs) can be fully monitored by collecting data from sensors in the drilling machine. This makes this type of pile an ideal candidate to utilize a big data methodology to indirectly obtain some constitutive parameters of the soil being drilled. Thus, in the present paper, the drilling process of CFAPs is modeled by a new physical model which predicts the energy spending during the execution of this type of pile. This new model relies on other fundamental properties of the soils drilled, such as unit weight, cohesion and internal friction angle. In order to show the applicability of the big data methodological framework hereby developed, a case study was conducted. A work site in Brasília-DF, Brazil, was studied and the execution of three CFAPs was monitored. Soil surveys were carried out to identify the soil strata in the site and, therefore, to validate the estimates of Young’s moduli provided by the new formulas. The 95% confidence intervals of Young’s moduli obtained for silty clay, clayey silt and silt were, in MPa, [14.56, 19.11], [12.26, 16.88] and [19.65, 26.11], respectively. These intervals are consistent with literature reports for the following materials: stiff to very stiff clays with low-medium plasticity, medium silts with slight plasticity, and stiff to very stiff silts with low plasticity, respectively. These were the types of materials observed during the site surveys; therefore, the results obtained are consistent with literature reports as well as with field surveys. This new framework may be useful to provide real-time estimates of the drilled soil’s parameters, as well as updating CFAPs designs during their execution. This way, sustainable designs can be achieved, where substrata materials are better characterized, avoiding over-designed structures. Full article
(This article belongs to the Special Issue Recent Developments in Numerical Methods, Machine Learning Techniques, and Quantum Computing for Geoscience Applications)
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