Cited by Performance of Sulfate Species on Limestone Powder Concrete under Low Temperature Pulse Current

Efficient prediction of California bearing ratio in solid waste-cement-stabilized soil using improved hybrid extreme gradient boosting model DOI

Yiliang Tu, Qianglong Yao, Shuitao Gu

et al.

Materials Today Communications, Journal Year: 2025, Volume and Issue: 43, P. 111627 - 111627

Published: Jan. 15, 2025

Language: Английский

Citations

Performance optimisation and predictive modelling of rice husk ash recycled concrete under the coupled action of freeze-thaw cycles and chloride erosion: Experimental study and machine learning DOI

Wei Zhang, Zhenhua Duan, Chao Liu

et al.

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 481, P. 141467 - 141467

Published: May 4, 2025

Language: Английский

Citations

Predicting corrosion behaviour of steel reinforcement in eco-friendly coral aggregate concrete based on hybrid machine learning methods DOI

Zhen Sun, Yalin Li, Li Su

et al.

Nondestructive Testing And Evaluation, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 21

Published: April 30, 2024

This study proposes a machine learning-assisted method for assessing steel reinforcement corrosion, utilising data on chloride ion concentration (CIC) and threshold (CCT) from eco-friendly coral aggregate concrete (EFCAC). A total of 2185 points were collected to establish the EFCAC-CIC database. multi-objective slime mould optimised support vector regression (MOSMA-SVR) model was developed. The significance each feature analysed. Subsequently, graphical user interface (GUI) developed based MOSMA-SVR model. Finally, GUI EFCAC-CCT used assess corrosion behaviour reinforcement. Results indicate that provides predictions are closer actual values, with smaller mean errors standard deviations. performance indicators, including coefficient determination, absolute error, percentage square root a20-index, 0.987, 0.0124, 0.0332, 2.91e − 4, 0.0171 0.9991, respectively, they superior those other tested models. water type water–binder ratio identified as two most critical factors. Furthermore, by integrating importance analysis results, salt-resistant EFCAC can be designed. When this is combined EFCAC-CCT, rebar in assessed real time.

Language: Английский

Citations

Study of Entropy Weight-Grey theory-BP Network life prediction Model of unit silica fume concrete lining under the influence of carbonation-sulfate freeze-thaw cycle erosion DOI

Zhi‐Min Chen, Meisheng Yi, Jiqiang Zhang

et al.

Research in Cold and Arid Regions, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

Optimization and Prediction of Colored Pervious Concrete Properties: Enhancing Performance through Augmented Grey Wolf Optimizer and Artificial Neural Networks DOI

Ahmet Tugrul Koc,

Sadık Alper Yıldızel

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112069 - 112069

Published: Feb. 1, 2025

Language: Английский

Citations

Machine learning in concrete durability: challenges and pathways identified by RILEM TC 315-DCS towards enhanced predictive models DOI

Woubishet Zewdu Taffese, Benoît Hilloulin, Yury Villagrán Zaccardi

et al.

Materials and Structures, Journal Year: 2025, Volume and Issue: 58(4)

Published: May 1, 2025

Language: Английский

Citations

Artificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review DOI

Dayou Luo,

Kejin Wang,

Dongming Wang

et al.

npj Materials Sustainability, Journal Year: 2025, Volume and Issue: 3(1)

Published: May 17, 2025

Language: Английский

Citations

Performance of Sulfate Species on Limestone Powder Concrete under Low Temperature Pulse Current DOI

Chenjie Wu,

Meng Li, Dezhi Wang

et al.

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2024, Volume and Issue: unknown, P. 103850 - 103850

Published: Dec. 1, 2024

Language: Английский

Citations