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Analysis of institutional authors

Alcala-Gonzalez, DanielAuthorMateo, Luis FAuthorQuijano, M AngelesAuthorMas-Lopez, M IsabelAuthorGarcia-Del-Toro, Eva MCorresponding Author

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December 25, 2025
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Compressive Strength-Based Classification of Eco-Friendly Concretes Using Machine Learning Models

Publicated to: MATERIALS. 18 (23): 5344- - 2025-11-27 18(23), DOI: 10.3390/ma18235344

Authors:

Alcala-Gonzalez, Daniel; Mateo, Luis F; Quijano, M Angeles; Mas-Lopez, M Isabel; Garcia-del-Toro, Eva M
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Affiliations

Univ Politecn Madrid UPM, ETSI Caminos Canales & Puertos, Ctr I D I Infraestruct Civiles Inteligentes & Sost, Edificio Retiro,Alfonso XII 3, 28014 Madrid, Spain - Author
Univ Politecn Madrid, Dept Ingn Civil Hidraul Energia & Medio Ambiente, ETSI Caminos Canales & Puertos, Edificio Retiro,Alfonso XII 3, Madrid 28014, Spain - Author
Univ Politecn Madrid, Dept Matemat Informat Aplicadas Ingn Civil & Naval, ETSI Caminos Canales & Puertos, Edificio Retiro,Alfonso XII 3, Madrid 28014, Spain - Author
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Abstract

Accurate prediction of compressive strength in eco-friendly concretes, where part of the cement is replaced with recycled glass powder, remains a fundamental challenge for sustainable construction. This study evaluates and compares the performance of five machine learning models-Na & iuml;ve Bayes, Random Forest, Decision Tree, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN)-for classifying the compressive strength of concretes with different mix designs and curing ages. The dataset includes 846 experimental samples produced at the School of Civil Engineering of UPM between 2004 and 2019. The results showed that Na & iuml;ve Bayes and Random Forest achieved the highest accuracy and generalizability, confirming that the incorporation of glass powder does not introduce significant data instability and can serve as a viable and sustainable substitute of cement. The Decision Tree model provided the greatest interpretability, enabling insight into the influence of mixture parameters, while SVM and k-NN were primarily effective in extreme strength categories. Overall, the findings demonstrated that probabilistic and ensemble learning methods outperform deterministic and proximity-based algorithms in classifying materials with high compositional variability. This work reinforces the potential of artificial intelligence as a non-destructive, reliable, and scalable tool for optimizing the performance of low carbon concretes and promoting sustainable materials engineering.
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Keywords

Barium compoundsClassification (of information)ClassifiersCompressive strengthConcrete mixturesDecision treesEco-friendlyEco-friendly concreteEco-friendly concrete,compressive strength,machine learning,na & iumlEnergy-consumptionEnvironmental protectionGlassGlass powderLearning systemsMachine learningMachine learning modelsMachine-learningNaïve bayesNearest neighbor searchNon destructive evaluationNon-destructive evaluationNondestructive examinationPerformancePredictionRandom forestRandom forestsSupport vector machinesSustainable constructionSustainable developmentVe bayes,random forest,glass powder,sustainable construction,non-destructive evaluation

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal MATERIALS 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, 2025, it was in position 25/97, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Metallurgy & Metallurgical Engineering. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Condensed Matter Physics.

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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 2026-04-25:

  • 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: 9 (PlumX).

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

    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.
    • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/92792/

    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: 19
    • Downloads: 8
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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 (ALCALA GONZALEZ, DANIEL) and Last Author (GARCIA DEL TORO, EVA MARIA).

    the author responsible for correspondence tasks has been GARCIA DEL TORO, EVA MARIA.

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