A New Approach to Machine Learning Model Development for Prediction of Concrete Fatigue Life under Uniaxial Compression

Abstract

The goal of this work is to show how machine learning models, such as the random forest, neural network, gradient boosting, and AdaBoost models, can be used to forecast the fatigue life (N) of plain concrete under uniaxial compression. Here, we developed our final machine learning model by generating the following three data files from the original data used in the work of Zhang et al.: (a) grouped data with the same input variable value and different output variable logN value, (b) data excluding outliers selected by three or more outlier detection methods; (c) average data excluding outliers, created by averaging the grouped data after excluding outliers from among the grouped data. Excluding the sustained strength of the concrete variable, originally treated as the seventh input variable in the work of Zhang et al., resulted in improving the determination coefficient (R-2) values. Moreover, the gradient boosting model showed a high R-2 value at 0.753, indicating a high accuracy in predicting outcomes. Further analysis using data excluding outliers shows that the R-2 value increased to 0.803. Moreover, the average data excluding outliers provided the best R-2 value at 0.915. Finally, a permutation feature importance (PFI) analysis was carried out to determine the strength of the relationship between the feature and the target value for the gradient boosting model. The analysis results showed that the maximum stress level (S-max) and loading frequency (f) were the most significant input variables, followed by compressive strength (f '(c)) and maximum to minimum stress ratio (R). Shape and height to width ratio (h/w) were the features with a non-significant influence on the model. This trend was previously confirmed by a Pearson and Spearman correlation analysis.