Data-Driven Prediction of Rebar Corrosion Parameters in Mortar and Simulated Pore Solution Using Optimised Extreme Gradient Boosting Models

Cakiroglu C., BEKDAŞ G., Pillala S., Geem Z. W.

Coatings, cilt.16, sa.4, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 4
  • Basım Tarihi: 2026
  • Doi Numarası: 10.3390/coatings16040456
  • Dergi Adı: Coatings
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex
  • Anahtar Kelimeler: Bayesian optimisation, corrosion current density, corrosion potential, SHAP explainability, XGBoost
  • İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet

Özet

This study presents two independently optimised Extreme Gradient Boosting (XGBoost) regression models, one for predicting corrosion current density ((Formula presented.)) and one for predicting corrosion potential ((Formula presented.)) parameters of carbon steel rebar embedded in mortar and immersed in simulated pore solution. An experimental dataset consisting of 216 measurements was curated from a systematic potentiodynamic scan study covering six chloride contamination levels, two carbonation states (non-carbonated and carbonated), four moisture conditions for mortar (65%, 85%, 95% relative humidity, and submerged), and three conditioning durations for simulated pore solution (36 h, 72 h and 20 days). Hyperparameters of the XGBoost models were optimised using a Bayesian optimisation framework with the Tree-structured Parzen Estimator (TPE) sampler over 300 trials. Model performance was assessed using 5-fold cross-validation and a random 80:20 train–test split. The optimised models achieved cross-validation (Formula presented.) scores of 0.936 and 0.953 for (Formula presented.) and (Formula presented.), respectively. On the hold-out test set, (Formula presented.) values of 0.933 and 0.945 were obtained with test RMSE values of 0.2 log10(µA/cm2) and 41.9 mV, respectively. The contribution of each input feature to model predictions was quantified and visualised using the SHapley Additive exPlanations (SHAP) methodology. SHAP analysis reveals that chloride content has the highest impact on (Formula presented.), followed by carbonation state and the low-humidity condition, while for (Formula presented.), chloride content and the Submerged condition have the greatest impact. An interactive web application was developed using Streamlit, enabling researchers and practitioners to obtain corrosion parameter predictions. The findings provide data-driven insights into the relative importance of environmental factors governing rebar corrosion, with direct implications for the development of accurate corrosion prediction models for reinforced concrete service life assessment.