Prediction of compressive strength of high-volume fly ash self-compacting concrete with silica fume using machine learning techniques

The quality and composition of the components in Self-Compacting Concrete (SCC) determine its compressive strength; however, determining these complex relationships through traditional statistical methods is difficult. This study uses five state-of-the-art machine learning techniques, namely, Random Forest (RF), Adaboost, Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN), and Bidirectional Long Short-Term Memory (BI-LSTM), to simulate the strength properties of high-volume fly ash self-compacting concrete (HVFA-SCC) with silica fume. A database with 240 SCC compressive strength tests that included a range of material proportions was put together in order to train these models. The primary constituents that were selected and taken into consideration as input variables were cement, fly ash, super-plasticizer, silica fume, coarse and fine aggregate, and age. Various statistical metrics, regression error characteristics curve, SHAP analysis, and uncertainty analysis were used to validate the models' effectiveness in predicting the compressive strength of HVFA-SCC with varying material compositions. The recommended RF model demonstrated the highest predictive accuracy of all the models that were analysed. While machine learning can predict compressive strengths, its opaque 'black-box' nature limits its use for SCC mix engineers. This study introduces an open-source Random Forest-based GUI to close this gap. This interface helps engineers make mix proportion decisions by accurately estimating SCC compressive strength under various test conditions. •Flowability, compressive strength and microstructural behaviours are investigated on binary and ternary blended HVFA-SCC.•Intelligent prediction models of compressive strength are developed based on machine learning techniques.•Cement, FA, super-plasticizer, SF, age, coarse and fine aggregates are considered as effective influenced factors.•RF model demonstrated the highest predictive accuracy of all the models that were analysed.•Using precise estimates of SCC compressive strength , developed GUI aids engineers in choosing appropriate mix proportion.