Machine Learning Approach for Prediction and Reliability Analysis of Failure Strength of U-Shaped Concrete Samples Joined with UHPC and PUC Composites DOI Open Access
Sadi Ibrahim Haruna, Yasser E. Ibrahim, Ibrahim Khalil Umar

и другие.

Journal of Composites Science, Год журнала: 2025, Номер 9(1), С. 23 - 23

Опубликована: Янв. 6, 2025

To meet the increasing demand for resilient infrastructure in seismic and high-impact areas, accurate prediction reliability analysis of performance composite structures under impact loads is essential. Conventional techniques, including experimental testing high-quality finite element simulation, require considerable time resources. address these issues, this study investigated individual hybrid models support vector regression (SVR), Gaussian process (GPR), improved eliminate particle swamp optimization hybridized artificial neural network (IEPANN) predicting failure strength concrete developed by combining normal (NC) with ultra-high (UHPC) polyurethane-based polymer (PUC), considering different surface treatments subjected to various static loads. An dataset was utilized train ML perform on dataset. Key parameters included compressive (Cfc), flexural load U-shaped specimens (P), density (ρ), first crack (N1), splitting tensile (ft). Results revealed that all had high accuracy, achieving NSE values above acceptable thresholds greater than 90% across datasets. Statistical errors such as RMSE, MAE, PBIAS were calculated fall within limits. Hybrid IEPANN appeared be most effective model, demonstrating highest value 0.999 lowest PBIAS, MAE 0.0013, 0.0018, 0.001, respectively. The times (N1 N2) reduced survival probability increased.

Язык: Английский

Machine Learning Approach for Prediction and Reliability Analysis of Failure Strength of U-Shaped Concrete Samples Joined with UHPC and PUC Composites DOI Open Access
Sadi Ibrahim Haruna, Yasser E. Ibrahim, Ibrahim Khalil Umar

и другие.

Journal of Composites Science, Год журнала: 2025, Номер 9(1), С. 23 - 23

Опубликована: Янв. 6, 2025

To meet the increasing demand for resilient infrastructure in seismic and high-impact areas, accurate prediction reliability analysis of performance composite structures under impact loads is essential. Conventional techniques, including experimental testing high-quality finite element simulation, require considerable time resources. address these issues, this study investigated individual hybrid models support vector regression (SVR), Gaussian process (GPR), improved eliminate particle swamp optimization hybridized artificial neural network (IEPANN) predicting failure strength concrete developed by combining normal (NC) with ultra-high (UHPC) polyurethane-based polymer (PUC), considering different surface treatments subjected to various static loads. An dataset was utilized train ML perform on dataset. Key parameters included compressive (Cfc), flexural load U-shaped specimens (P), density (ρ), first crack (N1), splitting tensile (ft). Results revealed that all had high accuracy, achieving NSE values above acceptable thresholds greater than 90% across datasets. Statistical errors such as RMSE, MAE, PBIAS were calculated fall within limits. Hybrid IEPANN appeared be most effective model, demonstrating highest value 0.999 lowest PBIAS, MAE 0.0013, 0.0018, 0.001, respectively. The times (N1 N2) reduced survival probability increased.

Язык: Английский

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