A predictive model for the freeze-thaw concrete durability index utilizing the deeplabv3+ model with machine learning

Construction and Building Materials, Год журнала: 2025, Номер 459, С. 139788 - 139788

Опубликована: Янв. 1, 2025

Язык: Английский

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Study on mechanical and bonding properties of nano-SiO2 reinforced recycled concrete: Macro test and micro analysis

Journal of Building Engineering, Год журнала: 2024, Номер 94, С. 109877 - 109877

Опубликована: Июнь 17, 2024

Язык: Английский

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Parametric study on global estimation models for compressive strength adopting various machine learning algorithms in concrete

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 136, С. 108888 - 108888

Опубликована: Июль 4, 2024

Язык: Английский

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Classification and prediction of deformed steel and concrete bond-slip failure modes based on SSA-ELM model

Structures, Год журнала: 2023, Номер 57, С. 105131 - 105131

Опубликована: Сен. 1, 2023

Язык: Английский

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Fast grid search: A grid search-inspired algorithm for optimizing hyperparameters of support vector regression

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, Год журнала: 2024, Номер 32(1), С. 68 - 92

Опубликована: Фев. 7, 2024

This study presents a fast hyperparameter optimization algorithm based on the benefits and shortcomings of standard grid search (GS) for support vector regression (SVR). presented GS-inspired algorithm, called (FGS), was tested benchmark datasets, impact FGS prediction accuracy primarily compared with GS which it is based. To validate efficacy proposed conduct comprehensive comparison, two additional techniques, namely particle swarm Bayesian optimization, were also employed in development models given datasets. The evaluation models' predictive performance conducted by assessing root mean square error, absolute percentage error. In addition to these metrics, number evaluated submodels time required used as determinative measures models. Experimental results proved that FGS-optimized SVR yield precise performance, supporting reliability, validity, applicability algorithm. As result, can be offered faster alternative optimizing hyperparameters terms execution time.

Язык: Английский

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Multi-objective optimization of tribological properties of diesel engine camshaft bearings

Structural and Multidisciplinary Optimization, Год журнала: 2025, Номер 68(1)

Опубликована: Янв. 1, 2025

Язык: Английский

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Machine learning based optimization for mix design of manufactured sand concrete

Construction and Building Materials, Год журнала: 2025, Номер 467, С. 140256 - 140256

Опубликована: Фев. 12, 2025

Язык: Английский

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Subgrade cumulative deformation probabilistic prediction method based on machine learning

Soil Dynamics and Earthquake Engineering, Год журнала: 2025, Номер 191, С. 109233 - 109233

Опубликована: Янв. 22, 2025

Язык: Английский

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Prediction of landfill gases concentration based on Grey Wolf Optimization – Support Vector Regression during landfill excavation process

Waste Management, Год журнала: 2025, Номер 198, С. 128 - 136

Опубликована: Март 4, 2025

Язык: Английский

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The improved mountain gazelle optimizer for spatiotemporal support vector regression: a novel method for railway subgrade settlement prediction integrating multi-source information

Applied Intelligence, Год журнала: 2025, Номер 55(6)

Опубликована: Март 4, 2025

Язык: Английский

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