Hybrid Machine Learning Model for Predicting the Fatigue Life of Plain Concrete Under Cyclic Compression

Abstract

Accurately predicting the fatigue life of concrete is crucial for ensuring the safety and durability of structural elements subjected to cyclic loading. Traditional empirical models often struggle to capture the complex interactions between mechanical properties and loading conditions, particularly the influence of frequency. This study introduces a hybrid machine learning model based on the stacking ensemble strategy, integrating Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANNs) to enhance prediction accuracy. A dataset of 891 experimental results from the literature was utilized, incorporating four key input variables: compressive strength, stress ratio, maximum stress-to-strength ratio, and loading frequency. The hybrid model demonstrated superior performance (R2 = 0.965, RMSE = 0.19), outperforming individual models and established predictive equations. SHAP analysis validated the model’s interpretability and emphasized the necessity of accounting for loading frequency. This study contributes a robust and generalizable tool for fatigue life prediction within the defined input domain, offering valuable insights for engineering design and structural assessment.