Compressive strength prediction models for concrete containing nano materials and exposed to elevated temperatures

Results in Engineering, Год журнала: 2025, Номер unknown, С. 103975 - 103975

Опубликована: Янв. 1, 2025

Язык: Английский

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Machine Learning Approach for Prediction and Reliability Analysis of Failure Strength of U-Shaped Concrete Samples Joined with UHPC and PUC Composites

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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Scaling effect on impact responses of steel beams and its energy threshold

International Journal of Mechanical Sciences, Год журнала: 2025, Номер unknown, С. 109996 - 109996

Опубликована: Янв. 1, 2025

Язык: Английский

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Metaheuristic-based prediction of shear resistance of headed stud connectors embedded in concrete coupled with SHAP explainability

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104445 - 104445

Опубликована: Фев. 1, 2025

Язык: Английский

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Integrated Machine Learning and Response Surface Methodology for Comprehensive Rheological Characterization of low-carbon binders

Case Studies in Construction Materials, Год журнала: 2025, Номер unknown, С. e04475 - e04475

Опубликована: Март 1, 2025

Язык: Английский

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Optimizing bio-hybrid composites for impact resistance using machine learning

Journal of the Brazilian Society of Mechanical Sciences and Engineering, Год журнала: 2025, Номер 47(5)

Опубликована: Апрель 4, 2025

Язык: Английский

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EXPLAINABLE MACHINE LEARNING-BASED PREDICTION OF BLAST LOADS ON STRUCTURAL SURFACES IN TWO-DIMENSIONAL SPATIAL COORDINATES

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104979 - 104979

Опубликована: Апрель 1, 2025

Язык: Английский

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ADVANCED PREDICTIVE MODELING OF SHEAR STRENGTH IN STAINLESS-STEEL COLUMN WEB PANELS USING EXPLAINABLE AI INSIGHTS

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103454 - 103454

Опубликована: Ноя. 1, 2024

Язык: Английский

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Intelligent prediction framework for axial compressive capacity of FRP-RACFST columns

Materials Today Communications, Год журнала: 2024, Номер unknown, С. 110999 - 110999

Опубликована: Ноя. 1, 2024

Язык: Английский

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