Evaluating hydrothermal degradation of GFRP composites under sustained loading using explainable machine learning

• Hydrothermal performance of GFRP composites for ocean and engineering applications. • Shapley additive explanations to develop polynomial fitted mathematical expressions for mechanical performance on the basis of XGBoost machine learning model. • Sustained loading above 30% shows a negative impact on the mechanical performance of hydrothermally-aged GFRP composites. • Comments on technical specifications by the European Committee for Standardization CEN/TS 19101, for moisture and temperature conversion factors. • Prediction application for the mechanical performance of GFRP composites based on an intelligent model. The understanding of GFRP composites under hydrothermal conditions and sustained loading offers valuable insights into their performance in challenging environments such as coastal areas, deep-sea structures, and environmentally friendly and long-lasting infrastructure solutions. This study examined the mechanical response of GFRP composites subjected to synergic sustained loading and hydrothermal degradation. A prediction application based on the XGBoost machine learning model was developed to estimate the residual mechanical response. The developed model was used to calculate the conversion factor accounting for moisture and temperature-based degradation in FRP composites. The SHAP analysis corroborated the experimental findings such that GFRP-based composites experience an initial rapid decline in mechanical properties when exposed to harsh environments, followed by a slower degradation rate over time. The pultruded vinyl ester-based GFRP composites depict less degradation than polyester-based composites and composites made via vacuum infusion. It was inferred that sustained loading below 30% has no negative impact on the mechanical characteristics of hydrothermal-aged GFRP composites. The degradation became worse for the sustained loading beyond 30% of the ultimate strength of the GFRP composite. Comments are also made on the current recommendations in technical specifications by the European Committee for Standardization CEN/TS 19101 related to moisture and temperature conversion factors. The current work is limited to the mechanical investigation of GFRP composites subjected to hydrothermal degradation. It needs to be extended to other composites such as CFRP and BFRP.

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Publication Details

Journal
Journal of Ocean Engineering and Science
Published
2025-05-30
DOI
https://doi.org/10.1016/j.joes.2025.05.005
Citations
7
Primary Topic
Non-Destructive Testing Techniques
Type
article
Field-Weighted Citation Impact
4.99

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article

Evaluating hydrothermal degradation of GFRP composites under sustained loading using explainable machine learning

Mudassir Iqbal, Xuan Zhao, Pei-Fu Zhang, Hui Li et al.
7 citations
Journal of Ocean Engineering and Science
Non-Destructive Testing Techniques
4.99
article

Evaluating hydrothermal degradation of GFRP composites under sustained loading using explainable machine learning

Mudassir Iqbal, Xuan Zhao, Pei-Fu Zhang, Hui Li, Daxu Zhang, Congshui Yu, Xiao-Ling Zhao
article en
7 citations

Abstract

• Hydrothermal performance of GFRP composites for ocean and engineering applications. • Shapley additive explanations to develop polynomial fitted mathematical expressions for mechanical performance on the basis of XGBoost machine learning model. • Sustained loading above 30% shows a negative impact on the mechanical performance of hydrothermally-aged GFRP composites. • Comments on technical specifications by the European Committee for Standardization CEN/TS 19101, for moisture and temperature conversion factors. • Prediction application for the mechanical performance of GFRP composites based on an intelligent model. The understanding of GFRP composites under hydrothermal conditions and sustained loading offers valuable insights into their performance in challenging environments such as coastal areas, deep-sea structures, and environmentally friendly and long-lasting infrastructure solutions. This study examined the mechanical response of GFRP composites subjected to synergic sustained loading and hydrothermal degradation. A prediction application based on the XGBoost machine learning model was developed to estimate the residual mechanical response. The developed model was used to calculate the conversion factor accounting for moisture and temperature-based degradation in FRP composites. The SHAP analysis corroborated the experimental findings such that GFRP-based composites experience an initial rapid decline in mechanical properties when exposed to harsh environments, followed by a slower degradation rate over time. The pultruded vinyl ester-based GFRP composites depict less degradation than polyester-based composites and composites made via vacuum infusion. It was inferred that sustained loading below 30% has no negative impact on the mechanical characteristics of hydrothermal-aged GFRP composites. The degradation became worse for the sustained loading beyond 30% of the ultimate strength of the GFRP composite. Comments are also made on the current recommendations in technical specifications by the European Committee for Standardization CEN/TS 19101 related to moisture and temperature conversion factors. The current work is limited to the mechanical investigation of GFRP composites subjected to hydrothermal degradation. It needs to be extended to other composites such as CFRP and BFRP.

Journal of Ocean Engineering and ScienceVol. 10(6)
Hong Kong Polytechnic University (HK), Sichuan International Studies University (CN), China Construction Eighth Engineering Division (China) (CN), Shanghai Ocean University (CN), University of Engineering and Technology Peshawar (PK)
Natural Science Foundation of Shanghai, National Natural Science Foundation of China
Openalex Percentile: Top 4%
Non-Destructive Testing Techniques
4.99
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