Machine learning–assisted performance prediction of carbon fiber Mortise-Tenon connections in civil structures

Abstract

This paper proposes a performance prediction and design method for carbon fiber mortise-tenon structures, integrating finite element simulation, experiment, and machine learning, to address the issues of experiment dependency and low design efficiency of such structures in composite materials. Through parametric finite element analysis, a dataset of 120 samples containing six key geometric parameters—tenon angle (a), tenon height (h), tenon root length (m), tenon neck width (n), specimen width (w), and thickness (T)—was established, and the prediction performance of nine machine learning regression models was systematically compared. The GradientBoostingRegressor model, after multi-stage hyperparameter optimization, achieved the best prediction performance, with a nested cross-validation R 2 of 0.76 ± 0.11 and MAPE of 4.10% ± 0.54%. To verify model reliability, seven groups of T300 carbon fiber mortise-tenon specimens with different geometric configurations were fabricated for uniaxial tensile testing, and full-field strain was obtained using Digital Image Correlation (DIC) technology. The results indicate that the mean absolute percentage error between machine learning predictions and experimental measurements is 9.99%, while the error between finite element simulation and experiments is 4.72%, demonstrating acceptable engineering accuracy. SHAP (SHapley Additive exPlanations) analysis reveals that tenon angle (a) is the dominant feature (mean |SHAP| = 1.296), followed by neck width (n, 0.539) and specimen width (w, 0.406), providing physically interpretable insights into the data-driven predictions. DIC strain contours confirm that high-strain bands concentrate at the tenon-neck/mortise-shoulder transition zone, consistent with the failure mechanisms captured by the model. This study confirms the effectiveness of machine learning in rapid performance prediction and key parameter identification for carbon fiber mortise-tenon structures, providing a new approach for the intelligent design of composite material connections.