Mechanical Strength Prediction of Steel-Polypropylene Fiber-based High-Performance Concrete Using Hybrid Machine Learning Algorithms

TL;DR

This study employs hybrid machine learning models to accurately predict the mechanical properties of steel-polypropylene fiber-reinforced high-performance concrete, aiding in optimized mix design and structural assessment.

Contribution

It introduces and compares three advanced hybrid machine learning models for predicting concrete strength, emphasizing interpretability and robustness with extensive validation.

Findings

ET-XGB achieved highest accuracy with R^2 > 0.99 for compressive strength.

RF-LGBM provided the most stable predictions for flexural strength.

SHAP analysis identified key predictors like fiber aspect ratio and silica fume.

Abstract

This research develops and evaluates machine learning models to predict the mechanical properties of steel-polypropylene fiber-reinforced high-performance concrete (HPC). Three model families were investigated: Extra Trees with XGBoost (ET-XGB), Random Forest with LightGBM (RF-LGBM), and Transformer with XGBoost (Transformer-XGB). The target properties included compressive strength (CS), flexural strength (FS), and tensile strength (TS), based on an extensive dataset compiled from published experimental studies. Model training involved k-fold cross-validation, hyperparameter optimization, Shapley additive explanations (SHAP), and uncertainty analysis to ensure both robustness and interpretability. Among the tested approaches, the ET-XGB model achieved the highest overall accuracy, with testing R^2 values of 0.994 for CS, 0.944 for FS, and 0.978 for TS and exhibited lowest uncertainty for CS and TS (approximately 13-16% and 30.4%, respectively). The RF-LGBM model provided the most stable and reliable predictions for FS (R^2 0.977), yielding the lowest uncertainty for FS (approximately 5-33%). The Transformer-XGB model demonstrated strong predictive capability (R^2 0.978 for TS and 0.967 for FS) but consistently showed the highest uncertainty, indicating reduced generalization reliability. SHAP analysis further indicated that fiber aspect ratios (AR1 and AR2), silica fume (Sfu), and steel fiber content (SF) were the most influential predictors of strength, whereas water content (W) and the water-binder ratio (w/b) consistently had negative effects. The findings confirm that machine learning models can provide accurate, interpretable, and generalizable predictions of HPC mechanical properties. These models offer valuable tools for optimizing concrete mix design and enhancing structural performance evaluation in engineering applications.