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Analysis of institutional authors

Li, EnmingAuthor

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December 11, 2023
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Intelligent prediction of engineered cementitious composites with limestone calcined clay cement (LC3-ECC) compressive strength based on novel machine learning techniques

Publicated to:Computers And Concrete. 32 (6): 577-594 - 2023-01-01 32(6), DOI: 10.12989/cac.2023.32.6.577

Authors: Li E; Zhang N; Xi B; Tam VWY; Wang J; Zhou J

Affiliations

Central South University - Author
Leibniz Institute of Ecological Urban and Regional Development - Author
Politecnico di Milano - Author
The University of Hong Kong - Author
Universidad Politécnica de Madrid - Author
Western Sydney University - Author
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Abstract

Engineered cementitious composites with calcined clay limestone cement (LC3-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC3-ECC compressive strength makes the prediction of LC3-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC3-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC3-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC3-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R2) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC3-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

Keywords

Engineered cementitious composites (ecc)Green concreteLimestone calcined clay cement (lc ) 3Metaheuristic optimizationSupport vector regression

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Computers And Concrete due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Computational Mechanics.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-07-10:

  • Scopus: 3

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 2025-07-10:

  • 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: 27 (PlumX).

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Australia; China; Germany; Hong Kong; Italy.

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 (LI, ENMING) .