Machine-Learning-Based Predictive Models for Punching Shear Strength of FRP-Reinforced Concrete Slabs: A Comparative Study DOI Creative Commons
Weidong Xu, Xian‐Ying Shi

Buildings, Год журнала: 2024, Номер 14(8), С. 2492 - 2492

Опубликована: Авг. 12, 2024

This study is focused on the punching strength of fiber-reinforced polymer (FRP) concrete slabs. The mechanical properties reinforced slabs are often constrained by their shear at column connection regions. Researchers have explored use reinforcement as an alternative to traditional steel address this limitation. However, current codes poorly calculate FRP-reinforced aim was create a robust model that can accurately predict its strength, thus improving analysis and design composite structures with In study, 189 sets experimental data were collected, six machine learning models, including linear regression, support vector machine, BP neural network, decision tree, random forest, eXtreme Gradient Boosting, constructed evaluated based goodness fit, standard deviation, root-mean-square error in order select most suitable for study. optimal obtained compared models proposed researchers. Finally, explainability conducted using SHapley Additive exPlanations (SHAP). results showed forests performed best among all outperformed existing suggested effective depth important proportional strength. not only provides guidance but also informs future engineering practice.

Язык: Английский

Advanced and hybrid machine learning techniques for predicting compressive strength in palm oil fuel ash-modified concrete with SHAP analysis DOI Creative Commons

Tariq Ali,

Kennedy C. Onyelowe, Muhammad Sarmad Mahmood

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 10, 2025

The increasing demand for sustainable construction materials has led to the incorporation of Palm Oil Fuel Ash (POFA) into concrete reduce cement consumption and lower CO₂ emissions. However, predicting compressive strength (CS) POFA-based remains challenging due variability input factors. This study addresses this issue by applying advanced machine learning models forecast CS POFA-incorporated concrete. A dataset 407 samples was collected, including six parameters: content, POFA dosage, water-to-binder ratio, aggregate superplasticizer curing age. divided 70% training 30% testing. evaluated include Hybrid XGB-LGBM, ANN, Bagging, LSSVM, GEP, XGB LGBM. performance these assessed using key metrics, coefficient determination (R2), root mean square error (RMSE), normalized means (NRMSE), absolute (MAE) Willmott index (d). XGB-LGBM model achieved maximum R2 0.976 lowest RMSE, demonstrating superior accuracy, followed ANN with an 0.968. SHAP analysis further validated identifying most impactful factors, ratio emerging as influential. These predictive offer industry a reliable framework evaluating concrete, reducing need extensive experimental testing, promoting development more eco-friendly, cost-effective building materials.

Язык: Английский

Процитировано

0
Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation DOI Creative Commons
Adil Khan, Majid Khan, Waseem Akhtar Khan

и другие.

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

Опубликована: Март 25, 2025

Язык: Английский

Процитировано

0
Artificial intelligence approaches in predicting the mechanical properties of natural fiber-reinforced concrete: A comprehensive review DOI
Mohammed Mohammed, Jawad K. Oleiwi,

Aeshah M. Mohammed

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 153, С. 110933 - 110933

Опубликована: Апрель 22, 2025

Язык: Английский

Процитировано

0
Utilizing rice husk for sustainable production of mesoporous titania nanocomposites with highly adsorption and photocatalysis DOI
Tzong‐Horng Liou,

Sheng-Yeh Wang

Biomass and Bioenergy, Год журнала: 2025, Номер 199, С. 107950 - 107950

Опубликована: Май 3, 2025

Язык: Английский

Процитировано

0
Machine-Learning-Based Predictive Models for Punching Shear Strength of FRP-Reinforced Concrete Slabs: A Comparative Study DOI Creative Commons
Weidong Xu, Xian‐Ying Shi

Buildings, Год журнала: 2024, Номер 14(8), С. 2492 - 2492

Опубликована: Авг. 12, 2024

This study is focused on the punching strength of fiber-reinforced polymer (FRP) concrete slabs. The mechanical properties reinforced slabs are often constrained by their shear at column connection regions. Researchers have explored use reinforcement as an alternative to traditional steel address this limitation. However, current codes poorly calculate FRP-reinforced aim was create a robust model that can accurately predict its strength, thus improving analysis and design composite structures with In study, 189 sets experimental data were collected, six machine learning models, including linear regression, support vector machine, BP neural network, decision tree, random forest, eXtreme Gradient Boosting, constructed evaluated based goodness fit, standard deviation, root-mean-square error in order select most suitable for study. optimal obtained compared models proposed researchers. Finally, explainability conducted using SHapley Additive exPlanations (SHAP). results showed forests performed best among all outperformed existing suggested effective depth important proportional strength. not only provides guidance but also informs future engineering practice.

Язык: Английский

Процитировано

3