An Explainable AI Framework for Crack Width Behavior Analysis in Prestressed Concrete Beams

Abstract

Accurate prediction of crack width is essential for serviceability design and durability assessments of prestressed concrete structures. This study presents an explainable machine learning framework for predicting the maximum crack width of prestressed concrete beams based on experimental data. A comprehensive database of 404 specimens, including the bending moment, prestress index, load ratio, effective depth, and section stiffness, was constructed and enhanced through mechanically informed feature engineering. Random forest and XGBoost regression models were developed and systematically tuned using cross-validated hyperparameter optimization. Among the evaluated models, XGBoost achieved the highest predictive accuracy, with a coefficient of determination of 0.6507 and a root mean square error of 0.184 mm. Model interpretability was investigated using feature importance measures and SHapley Additive exPlanations, which identified the bending moment, load ratio, concrete compressive strength, and prestress index as the dominant factors influencing crack width. The observed relationships are consistent with the established flexural cracking theory, confirming that the proposed model captures physically meaningful behavior. The results demonstrate that combining explainable artificial intelligence with structural mechanics provides a robust and transparent tool for crack width prediction, offering valuable support for the performance-based evaluation and design of prestressed concrete members.

Keywords

prestressed concrete / crack width prediction / explainable artificial intelligence / random forest / XGBoost / structural assessment / flexural behavior / data-driven modeling

Supplementary Materials

Supporting information can be accessed online. The refined experimental dataset, full preprocessing scripts, trained machine-learning models scripts, hyperparameter-tuning workflows, and SHAP explainability scripts used in this study are publicly available at: https://github.com/BehradEmadi

Author Contributions

Conceptualization, S.Emadi; methodology, S.Emadi; validation, S.Emadi, and P.Lotfabadi; formal analysis, A.Farmanfarma; investigation, A.Farmanfarma; data curation, S.Emadi; writing—original draft preparation, S.Emadi; writing—review and editing, A.Farmanfarma; visualization, A.Farmanfarma; supervision, S.Emadi; project administration, S.Emadi. All authors have read and agreed to the published version of the manuscript.

Conflict of interest

The authors disclosed no relevant relationships.

Data availability statement

The data that support the findings of this study are available from the corresponding author, Ma, upon reasonable request.

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