Explainable CatBoost–TabPFN Hybrid for Mode-Resolved Interlaminar Fracture Toughness Prediction in Fiber-Reinforced Polymer Composites
5 affiliations
- 1Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
- 2Department of Chemical Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
- 3Department of Computer Engineering, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
- 4Chemical Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
- 5Department of Civil Engineering, Faculty of Engineering, Islamic University of Madinah, Al-Madinah Al-Munawara, Prince Naif Ibn Abdulaziz, Al Jamiah, Medina 42351, Saudi Arabia
Abstract
Fiber-reinforced polymer (FRP) composites offer high specific strength, high stiffness, and good corrosion resistance, which makes them suitable for aerospace, automotive, marine, and wind-energy structures. Their interlaminar fracture behavior is harder to pin down because delamination testing is slow and costly and the response shifts with loading mode. This study presents an explainable hybrid machine learning framework for interlaminar fracture toughness under Mode-I, Mode-II, and Mixed-Mode loading. A weighted ensemble pairs Categorical Boosting (CatBoost), a gradient-boosting method, with the Tabular Prior-Data Fitted Network (TabPFN), a pretrained transformer built for tabular data. A Tree-structured Parzen Estimator (TPE) tunes the CatBoost hyperparameters and the ensemble weight against specimen-grouped cross-validation. The work draws on the public D-STANDART experimental dataset and treats DCB, ELS, and MMB as three separate prediction problems for GIC, GIIC, and GC rather than pooling them into one model. The analyzed D-STANDART specimens comprise pultruded carbon-fiber-reinforced polymer (CFRP) material based on Zoltek PX35 carbon fiber. The data are partitioned at the specimen level, so no specimen contributes records to more than one subset, and feature selection uses the training data alone. SHapley Additive exPlanations (SHAP) then measure how each input shifts the predicted toughness in each mode. The framework reached an R2 of 0.9280 for DCB, 0.9870 for ELS, and 0.9627 for MMB, with RMSE values of 5.1759, 1.5041, and 2.8809 and MAE values of 1.6502, 0.5372, and 0.7000, respectively. Bringing a tabular foundation model into delamination analysis, and keeping the three loading modes apart, lets the framework report mode-specific feature behavior instead of one averaged picture.
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BibTeX
@article{arifuzzaman2026explainable,
title = {{Explainable CatBoost–TabPFN Hybrid for Mode-Resolved Interlaminar Fracture Toughness Prediction in Fiber-Reinforced Polymer Composites}},
author = {Md Arifuzzaman and Sayeed Rushd and Muhammad Ali Martuza and Mohammod Hafizur Rahman and Md. Kamrul Islam and Ayed Eid Alluqmani},
journal = {Polymers},
year = {2026},
month = oct,
doi = {10.3390/polym18212621},
url = {https://www.mdpi.com/2073-4360/18/21/2621},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Explainable CatBoost–TabPFN Hybrid for Mode-Resolved Interlaminar Fracture Toughness Prediction in Fiber-Reinforced Polymer Composites
AU - Md Arifuzzaman
AU - Sayeed Rushd
AU - Muhammad Ali Martuza
AU - Mohammod Hafizur Rahman
AU - Md. Kamrul Islam
AU - Ayed Eid Alluqmani
PY - 2026
DA - 2026/10/23
JO - Polymers
DO - 10.3390/polym18212621
UR - https://www.mdpi.com/2073-4360/18/21/2621
AB - Fiber-reinforced polymer (FRP) composites offer high specific strength, high stiffness, and good corrosion resistance, which makes them suitable for aerospace, automotive, marine, and wind-energy structures. Their interlaminar fracture behavior is harder to pin down because delamination testing is slow and costly and the response shifts with loading mode. This study presents an explainable hybrid machine learning framework for interlaminar fracture toughness under Mode-I, Mode-II, and Mixed-Mode loading. A weighted ensemble pairs Categorical Boosting (CatBoost), a gradient-boosting method, with the Tabular Prior-Data Fitted Network (TabPFN), a pretrained transformer built for tabular data. A Tree-structured Parzen Estimator (TPE) tunes the CatBoost hyperparameters and the ensemble weight against specimen-grouped cross-validation. The work draws on the public D-STANDART experimental dataset and treats DCB, ELS, and MMB as three separate prediction problems for GIC, GIIC, and GC rather than pooling them into one model. The analyzed D-STANDART specimens comprise pultruded carbon-fiber-reinforced polymer (CFRP) material based on Zoltek PX35 carbon fiber. The data are partitioned at the specimen level, so no specimen contributes records to more than one subset, and feature selection uses the training data alone. SHapley Additive exPlanations (SHAP) then measure how each input shifts the predicted toughness in each mode. The framework reached an R2 of 0.9280 for DCB, 0.9870 for ELS, and 0.9627 for MMB, with RMSE values of 5.1759, 1.5041, and 2.8809 and MAE values of 1.6502, 0.5372, and 0.7000, respectively. Bringing a tabular foundation model into delamination analysis, and keeping the three loading modes apart, lets the framework report mode-specific feature behavior instead of one averaged picture.
SN - 2073-4360
ER -