ArticleMaterials (Basel, Switzerland)2022
Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning.
Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 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
- Evaluate the compressive strength of fiber-reinforced geopolymer concrete incorporating ground granulated blast furnace slag with machine learning approach.Scientific reports · 2026Article
- Explainable Ensemble Machine Learning for the Prediction and Optimization of Pozzolanic Concrete Compressive Strength.Polymers · 2026Article
- Modeling mechanical properties of rubberized concrete using gene expression programming (GEP) and random forest: a comparative study.Scientific reports · 2026Article
- Finite element analysis of flexural behavior in ultra-high-strength concrete beams with BFRP and hybrid BFRP-steel reinforcement.Scientific reports · 2025Article
- Experimental and machine learning-based analysis of red mud influence on recycled aggregate concrete properties.Scientific reports · 2025Article
- Predicting the mechanical performance of industrial waste incorporated sustainable concrete using hybrid machine learning modeling and parametric analyses.Scientific reports · 2025Article
- Machine learning approaches for forecasting compressive strength of high-strength concrete.Scientific reports · 2025Article
- AI driven prediction of early age compressive strength in ultra high performance fiber reinforced concrete.Scientific reports · 2025Article
- Unlocking the Potential of Caribbean Coarse Aggregates for High-Strength Concrete Development.Materials (Basel, Switzerland) · 2025Article
- Advanced and hybrid machine learning techniques for predicting compressive strength in palm oil fuel ash-modified concrete with SHAP analysis.Scientific reports · 2025Article
- Explainable Ensemble Learning and Multilayer Perceptron Modeling for Compressive Strength Prediction of Ultra-High-Performance Concrete.Biomimetics (Basel, Switzerland) · 2024Article
- An ensemble learning-based prediction model for the compressive strength degradation of concrete containing superabsorbent polymers (SAP).Scientific reports · 2024Article
- Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis.Materials (Basel, Switzerland) · 2023Article
- Prediction of the shear capacity of ultrahigh-performance concrete beams using neural network and genetic algorithm.Scientific reports · 2023Article
- Mechanical Properties of Ultra-High Performance Concrete with Coal Gasification Coarse Slag as River Sand Replacement.Materials (Basel, Switzerland) · 2022Article
- Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods.Materials (Basel, Switzerland) · 2022Article
- Artificial Intelligence Models for the Mass Loss of Copper-Based Alloys under Cavitation.Materials (Basel, Switzerland) · 2022Article
- New SHapley Additive ExPlanations (SHAP) Approach to Evaluate the Raw Materials Interactions of Steel-Fiber-Reinforced Concrete.Materials (Basel, Switzerland) · 2022Article
- Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms.Polymers · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
In civil engineering, ultra-high-strength concrete (UHSC) is a useful and efficient building material. To save money and time in the construction sector, soft computing approaches have been used to estimate concrete properties. As a result, the current work used sophisticated soft computing techniques to estimate the compressive strength of UHSC. In this study, XGBoost, AdaBoost, and Bagging were the employed soft computing techniques. The variables taken into account included cement content, fly ash, silica fume and silicate content, sand and water content, superplasticizer content, steel fiber, steel fiber aspect ratio, and curing time. The algorithm performance was evaluated using statistical metrics, such as the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R
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