ArticleBiomimetics (Basel, Switzerland)2024
Explainable Ensemble Learning and Multilayer Perceptron Modeling for Compressive Strength Prediction of Ultra-High-Performance Concrete.
Article in Biomimetics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A hybrid experimental and machine learning framework for designing and predicting compressive strength of ultra-high-performance concrete.Scientific reports · 2026Article
- Synergistic Effects of HPMC-HEMC Blending on the Rheological Properties of Composite Cement Paste Based on Explainable Machine Learning.Materials (Basel, Switzerland) · 2026Article
- Dataset on the reproducibility of UHPC mechanical properties under a fixed recipe with controlled production variability.Scientific data · 2026Article
- A Machine Learning Framework for the Reconstruction of Composite Fatigue and Fracture Properties: A Synthetic Data Study.Materials (Basel, Switzerland) · 2026Article
- Identification and validation of an explainable machine learning model for early postoperative pulmonary complications after esophagectomy in patients with esophageal cancer.Journal of gastrointestinal oncology · 2026Article
- Predicting mechanical properties of CFRP composites using data-driven models with comparative analysis.PloS one · 2025Article
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Authors and funding
4 authors.
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Abstract
The performance of ultra-high-performance concrete (UHPC) allows for the design and creation of thinner elements with superior overall durability. The compressive strength of UHPC is a value that can be reached after a certain period of time through a series of tests and cures. However, this value can be estimated by machine-learning methods. In this study, multilayer perceptron (MLP) and Stacking Regressor, an ensemble machine-learning models, is used to predict the compressive strength of high-performance concrete. Then, the ML model's performance is explained with a feature importance analysis and Shapley additive explanations (SHAPs), and the developed models are interpreted. The effect of using different random splits for the training and test sets has been investigated. It was observed that the stacking regressor, which combined the outputs of Extreme Gradient Boosting (XGBoost), Category Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extra Trees regressors using random forest as the final estimator, performed significantly better than the MLP regressor. It was shown that the compressive strength was predicted by the stacking regressor with an average R
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