Machine learning-based prediction of compressive energy absorption in shoe soles with different features.

ABSTRACT: This study conducted 537 force-controlled compression tests on shoe soles to measure strain energy. Various features were studied, including midsole and outsole structures, sole geometry, sole hardness, and loading conditions including applied compression force, angle, and loading speed. Strain energies during compression tests were recorded, followed by correlation analysis, analysis of variance (ANOVA), and Tukey tests. Subsequently, different machine learning (ML) algorithms-i.e., simple linear regression (SLR), support vector regression (SVR), random forest (RF), and multi-layer perceptron (MLP)-were applied for curve fitting. The correlation matrix revealed that strain energy exhibits a strong dependence on the applied force (correlation = 0.89), which stands out significantly compare

SUBMITTER: Mohammadi MM 

PROVIDER: S-EPMC12549818 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature