Explainable Tuned Machine Learning Models for Assessing the Impact of Corrosion on Bond Strength in Concrete DOI Creative Commons

Maryam Bypour,

Alireza Mahmoudian,

Mohammad Yekrangnia

и другие.

Cleaner Engineering and Technology, Год журнала: 2024, Номер 23, С. 100834 - 100834

Опубликована: Ноя. 12, 2024

Язык: Английский

Machine Learning-Driven Optimization for Predicting Compressive Strength in Fly Ash Geopolymer Concrete DOI Creative Commons

Maryam Bypour,

Mohammad Yekrangnia, Mahdi Kioumarsi

и другие.

Cleaner Engineering and Technology, Год журнала: 2025, Номер unknown, С. 100899 - 100899

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2
A novel machine learning-based approach to determine the reduction factor for punching shear strength capacity of voided concrete slabs DOI Creative Commons

Alireza Mahmoudian,

Mussa Mahmoudi, Mohammad Yekrangnia

и другие.

Deleted Journal, Год журнала: 2025, Номер 2(1)

Опубликована: Фев. 27, 2025

Язык: Английский

Процитировано

1
Explainable Boosting Machine Learning for Predicting Bond Strength of FRP Rebars in Ultra High-Performance Concrete DOI Creative Commons

Alireza Mahmoudian,

Maryam Bypour,

Mahdi Kioumarsi

и другие.

Computation, Год журнала: 2024, Номер 12(10), С. 202 - 202

Опубликована: Окт. 9, 2024

Aiming at evaluating the bond strength of fiber-reinforced polymer (FRP) rebars in ultra-high-performance concrete (UHPC), boosting machine learning (ML) models have been developed using datasets collected from previous experiments. The considered variables this study are rebar type and diameter, elastic modulus tensile rebars, compressive cover, embedment length, test method. dataset contains two methods: pullout tests beam tests. Four types rebar, including carbon (CFRP), glass (GFRP), basalt, steel were considered. ML applied include AdaBoost, CatBoost, Gradient Boosting, XGBoost, Hist Boosting. After hyperparameter tuning, these demonstrated significant improvements predictive accuracy, with XGBoost achieving highest R2 score 0.95 lowest Root Mean Square Error (RMSE) 2.21. Shapley values analysis revealed that strength, modulus, length most critical factors influencing strength. findings offer valuable insights for applying predicting FRP-reinforced UHPC, providing a practical tool structural engineering.

Язык: Английский

Процитировано

5
Interpretable machine learning models for predicting flexural bond strength between FRP/steel bars and concrete DOI Creative Commons
Mohsen Ebrahimzadeh,

Alireza Mahmoudian,

Nima Tajik

и другие.

Structures, Год журнала: 2025, Номер 74, С. 108587 - 108587

Опубликована: Март 4, 2025

Язык: Английский

Процитировано

0
Explainable Tuned Machine Learning Models for Assessing the Impact of Corrosion on Bond Strength in Concrete DOI Creative Commons

Maryam Bypour,

Alireza Mahmoudian,

Mohammad Yekrangnia

и другие.

Cleaner Engineering and Technology, Год журнала: 2024, Номер 23, С. 100834 - 100834

Опубликована: Ноя. 12, 2024

Язык: Английский

Процитировано

3