ArticleScientific reports2026
An experimental and computational intelligence simulation for predicting the strength accuracy of self-compacting concrete using Linz-Donawitz Slag as industrial waste derivatives.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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4 authors.
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Abstract
This study investigates the mechanical performance of self-compacting concrete (SCC) incorporating Linz-Donawitz Slag (LDS) as a partial cement replacement, with an emphasis on developing an accurate predictive framework for compressive strength (CS). A total of 147 experimental data points were generated from seven SCC mix designs with varying LDS contents. To model the complex nonlinear relationships between input variables and CS test values, several machine learning (ML) models, including Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model were developed to predict CS of LDS based-SCC. Among these, the hybrid CNN-LSTM model demonstrated superior predictive performance, achieving a coefficient of determination (R
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