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
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
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
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) .