Građevinar, Vol. 78 No. 01., 2026.
Original scientific paper
https://doi.org/10.14256/JCE.4344.2025
Flexural capacity prediction of partially encased composite beams using machine learning
Hongxin Liu
Ping Yang
Yaming Li
Shuizhong Jia
Xiaomeng Xie
Abstract
To address traditional limitations, this study investigated the flexural performance of large-section PEC beams with varied web openings using experiments and machine learning (ML). Four-point bending tests on specimens with different sections and openings demonstrated excellent ductility (coefficient >4.0), although openings slightly reduced yield strength without significantly affecting overall performance. A database of 15 variables was used to train and validate four ML models (RF, CatBoost, KNN, LightGBM). The RF model achieved the highest accuracy (~2.6% MAE). Shapley analysis improved interpretability by identifying key parameters. Integrating explainable ML substantially enhances the prediction accuracy and interpretability of PEC flexural capacity, offering a promising approach for intelligent structural design and assessent.
Keywords
PEC beams; failure mode; flexural capacity; ductility; machine learning
Hrčak ID:
345451
URI
Publication date:
2.3.2026.
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