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Analysis of institutional authors

Fernandez AAuthorSanchidrián JaCorresponding AuthorSegarra PAuthorGomez SAuthorLi EAuthor

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April 17, 2023
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Article

Rock mass structural recognition from drill monitoring technology in underground mining using discontinuity index and machine learning techniques

Publicated to: International Journal of Mining Science and Technology. 33 (5): 555-571 - 2023-05-31 33(5), DOI: 10.1016/j.ijmst.2023.02.004

Authors:

Fernandez, Alberto; Sanchidrian, Jose A; Segarra, Pablo; Gomez, Santiago; Li, Enming; Navarro, Rafael
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Affiliations

Univ Politecn Madrid, ETSI Minas & Energia, Madrid, Spain - Author
Univ Salamanca, GIR Charrock, Salamanca, Spain - Author
Universidad de Salamanca - Author
Universidad Politécnica de Madrid - Author
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Abstract

A procedure to recognize individual discontinuities in rock mass from measurement while drilling (MWD) technology is developed, using the binary pattern of structural rock characteristics obtained from in-hole images for calibration. Data from two underground operations with different drilling technology and different rock mass characteristics are considered, which generalizes the application of the methodology to different sites and ensures the full operational integration of MWD data analysis. Two approaches are followed for site-specific structural model building: a discontinuity index (DI) built from variations in MWD parameters, and a machine learning (ML) classifier as function of the drilling parameters and their variability. The prediction ability of the models is quantitatively assessed as the rate of recognition of discontinuities observed in borehole logs. Differences between the parameters involved in the models for each site, and differences in their weights, highlight the site-dependence of the resulting models. The ML approach offers better performance than the classical DI, with recognition rates in the range 89% to 96%. However, the simpler DI still yields fairly accurate results, with recognition rates 70% to 90%. These results validate the adaptive MWD-based methodology as an engineering solution to predict rock structural condition in underground mining operations.
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Keywords

machine learningmodelrock mass characterizationsimilarity metrics of binary vectorsstructural rock factorunderground miningChargeability assessmentDrill monitoring technologyMachine learningRock mass characterizationSimilarity metrics of binary vectorsStructural rock factorUnderground mining

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal International Journal of Mining Science and Technology due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position 1/32, thus managing to position itself as a Q1 (Primer Cuartil), in the category Mining & Mineral Processing. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 11.19. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 10.43 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-27, the following number of citations:

  • WoS: 45
  • Scopus: 49
  • Google Scholar: 58
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-27:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 64.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 64 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 4.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Additionally, the work has been submitted to a journal classified as Diamond in relation to this type of editorial policy.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/92701/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 48
  • Downloads: 60
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (FERNANDEZ OSORIO, ALBERTO) .

the author responsible for correspondence tasks has been SANCHIDRIAN BLANCO, JOSE ANGEL.

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Project objectives

Los objetivos perseguidos en esta aportación son los siguientes: desarrollar un procedimiento para reconocer discontinuidades individuales en masas rocosas mediante tecnología de medición durante la perforación (MWD); calibrar el reconocimiento utilizando patrones binarios de características estructurales obtenidas de imágenes en el interior del taladro; construir modelos estructurales específicos para cada sitio mediante un índice de discontinuidad (DI) basado en variaciones de parámetros MWD y un clasificador de aprendizaje automático (ML) en función de dichos parámetros; evaluar cuantitativamente la capacidad predictiva de ambos modelos mediante la tasa de reconocimiento de discontinuidades observadas en registros de sondeos; y validar la metodología adaptativa basada en MWD como solución ingenieril para predecir condiciones estructurales en minería subterránea.
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Most relevant results

El estudio presenta un procedimiento novedoso para reconocer discontinuidades en macizos rocosos mediante tecnología de medición durante la perforación (MWD). Los resultados más relevantes son: el índice de discontinuidad (DI) basado en variaciones de parámetros MWD logra tasas de reconocimiento entre 70% y 90%; el clasificador de aprendizaje automático (ML) mejora el rendimiento con tasas entre 89% y 96%; se identifican diferencias significativas en parámetros y pesos entre sitios, evidenciando la dependencia específica del modelo al emplazamiento; la metodología es aplicable a diferentes operaciones subterráneas con distintas tecnologías y características del macizo; y la validación cuantitativa se realiza mediante comparación con registros de perforación.
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