ArticleScientific reports2026
Explainable machine learning-based investigation for influential parameters towards interfacial capacity of spliced glass fiber reinforced polymer bars.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Splicing glass fiber reinforced polymer (GFRP) bars is commonly encountered in practical applications due to limitations associated with rebar length. In turn, this affects the bond stress maintaining the equilibrium between concrete and reinforcement. Also, there is a lack of estimating the bond strength of spliced GFRP-reinforced beams employing machine learning approaches and interpreting the effective parameters. Thus, this paper predicts interfacial capacity of spliced GFRP-reinforced beams using machine learning (ML) models including ordinary least square regression (OLS), artificial neural network (ANN), and extreme gradient boosting algorithm (XGBoost), and compares those models against the established models. A database compromising 120 samples was collected from relevant experimental studies conducted on beam tests. XGBoost model gave the best performance out of the remaining ML models with an R
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