A Predictive Model for the Freeze-Thaw Concrete Durability Index Utilizing the Deeplabv3+ Model with Machine Learning DOI
Daming Luo,

Xudong Qiao,

Ditao Niu

et al.

Published: Jan. 1, 2024

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Language: Английский

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

Xudong Qiao,

Ditao Niu

et al.

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 459, P. 139788 - 139788

Published: Jan. 1, 2025

Language: Английский

Citations

2
Study on mechanical and bonding properties of nano-SiO2 reinforced recycled concrete: Macro test and micro analysis DOI
Congcong Fan,

Yuanxun Zheng,

Jingbo Zhuo

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 94, P. 109877 - 109877

Published: June 17, 2024

Language: Английский

Citations

7
Parametric study on global estimation models for compressive strength adopting various machine learning algorithms in concrete DOI
Woldeamanuel Minwuye Mesfin, Hyeong-Ki Kim

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 136, P. 108888 - 108888

Published: July 4, 2024

Language: Английский

Citations

6
Classification and prediction of deformed steel and concrete bond-slip failure modes based on SSA-ELM model DOI
Congcong Fan,

Yuanxun Zheng,

Yongchao Wen

et al.

Structures, Journal Year: 2023, Volume and Issue: 57, P. 105131 - 105131

Published: Sept. 1, 2023

Language: Английский

Citations

12
Fast grid search: A grid search-inspired algorithm for optimizing hyperparameters of support vector regression DOI Creative Commons
Mustafa Açıkkar

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, Journal Year: 2024, Volume and Issue: 32(1), P. 68 - 92

Published: Feb. 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.

Language: Английский

Citations

4
Multi-objective optimization of tribological properties of diesel engine camshaft bearings DOI
Jingjing Zhao, Yuan Li, Yan Li

et al.

Structural and Multidisciplinary Optimization, Journal Year: 2025, Volume and Issue: 68(1)

Published: Jan. 1, 2025

Language: Английский

Citations

0
Machine learning based optimization for mix design of manufactured sand concrete DOI

Yuan Zhong-xia,

Wei Zheng, Hongxia Qiao

et al.

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 467, P. 140256 - 140256

Published: Feb. 12, 2025

Language: Английский

Citations

0
Subgrade cumulative deformation probabilistic prediction method based on machine learning DOI
Zhixing Deng,

Linrong Xu,

Yongwei Li

et al.

Soil Dynamics and Earthquake Engineering, Journal Year: 2025, Volume and Issue: 191, P. 109233 - 109233

Published: Jan. 22, 2025

Language: Английский

Citations

0
Prediction of landfill gases concentration based on Grey Wolf Optimization – Support Vector Regression during landfill excavation process DOI
Zhimin Liu,

Zehua Zhang,

Qingwen Zhang

et al.

Waste Management, Journal Year: 2025, Volume and Issue: 198, P. 128 - 136

Published: March 4, 2025

Language: Английский

Citations

0
The improved mountain gazelle optimizer for spatiotemporal support vector regression: a novel method for railway subgrade settlement prediction integrating multi-source information DOI
Chen Guangwu, Shilin Zhao, Peng Li

et al.

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(6)

Published: March 4, 2025

Language: Английский

Citations

0