ArticleMaterials (Basel, Switzerland)2025
Prediction of Ultra-High-Performance Concrete (UHPC) Compressive Strength Based on Convolutional Neural Networks.
Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Dataset on the reproducibility of UHPC mechanical properties under a fixed recipe with controlled production variability.Scientific data · 2026Article
- Advanced Experimental Technology, Theory and Numerical Methods in Geomaterials and Concrete Materials.Materials (Basel, Switzerland) · 2026Article
- Comparative machine learning models for predicting the compressive strength of ultra-high-performance concrete.PloS one · 2026Article
- Effect of sodium hydroxide dosage on strength development in cement-fly ash mortars: Experimental and ANN-based prediction.PloS one · 2026Article
- Machine-Learning-Based Probabilistic Model and Design-Oriented Formula of Shear Strength Capacity of UHPC Beams.Materials (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
This paper investigates the potential of deep learning in predicting the compressive strength of ultra-high-performance concrete (UHPC) by developing a convolutional neural network (CNN) model with two convolutional layers. The proposed CNN architecture demonstrates capability in accurately predicting UHPC compressive strength from tabular data encompassing various material compositions. Ten input variables were selected, including the cement content, water content, silica fume content, silica powder content, silica sand content, superplasticizer content, and curing parameters. The model was trained and tested on a dataset comprising 219 samples. Experimental results indicated excellent predictive performance, with the CNN achieving a coefficient of determination (
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Registered trials
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