ArticleScientific reports2025
Estimating the surface chloride concentration of marine concrete utilizing advanced hybrid machine learning models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Explainable AI-based optimization framework for sustainable fly ash concrete design.Scientific reports · 2026Article
- An experimental and computational intelligence simulation for predicting the strength accuracy of self-compacting concrete using Linz-Donawitz Slag as industrial waste derivatives.Scientific reports · 2026Article
- Hybrid machine learning for SCC strength prediction using metaheuristic optimization.Scientific reports · 2026Article
- Prediction of Chloride Penetration Depth in Concrete Using a Combined Ensemble-Neural Network Architecture: Facing Data Saturation.Materials (Basel, Switzerland) · 2026Article
- A hybrid flower pollination algorithm-deep learning approach for strength prediction of sustainable recycled fine aggregate concrete with GUI-based implementation.Scientific reports · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hybrid machine learning (ML) models exhibit enhanced accuracy relative to both ensemble and individual models. In this study, advanced hybrid ML approaches were employed to create a reliable model for estimating surface chloride concentration (CC) in marine concrete, eliminating the necessity for labour-intensive and expensive physical experiments. The study combined artificial neural networks (ANN) and support vector regression (SVR) with metaheuristic optimization algorithms, specifically grey wolf optimization (GWO) and gorilla troops optimization (GTO), resulting in the development of four innovative hybrid ML models. Additionally, the integration of partial dependence plots and SHapley Additive exPlanations (SHAP) values offered profound insights into the critical variables influencing surface chloride concentration, advancing both the interpretability and precision of the models. These data sets were sourced from a wide range of literature. All the models exhibited strong performance, with SVR-GWO proving to be the optimal selection. Notably, SVR-GWO demonstrated the highest coefficient of determination (R
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