Predicting the crack repair rate of self-healing concrete using soft-computing tools

Lou, Yuanfeng; Wang, Huiling; Amin, Muhammad Nasir; Arifeen, Siyab Ul; Dodo, Yakubu; Althoey, Fadi; Deifalla, Ahmed Farouk

doi:10.1016/j.mtcomm.2024.108043

Materials Today Communications

2024

DOI: 10.1016/j.mtcomm.2024.108043

|View full text |Cite

Predicting the crack repair rate of self-healing concrete using soft-computing tools

Yuanfeng Lou,

Huiling Wang,

Muhammad Nasir Amin

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Investigating the effectiveness of carbon nanotubes for the compressive strength of concrete using AI-aided tools

Sun,

Amin,

Qadir

et al. 2024

Case Studies in Construction Materials

View full text Add to dashboard Cite

Investigating the effectiveness of carbon nanotubes for the compressive strength of concrete using AI-aided tools

Sun,

Amin,

Qadir

et al. 2024

Case Studies in Construction Materials

View full text Add to dashboard Cite

Drying shrinkage and crack width prediction using machine learning in mortars containing different types of industrial by-product fine aggregates

Ocak,

Bekdaş,

Işıkdağ

et al. 2024

Journal of Building Engineering

View full text Add to dashboard Cite

Analyzing chloride diffusion for durability predictions of concrete using contemporary machine learning strategies

Zhang,

Li,

Amin

et al. 2024

Materials Today Communications

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Predicting the crack repair rate of self-healing concrete using soft-computing tools

Cited by 3 publications

References 52 publications

Investigating the effectiveness of carbon nanotubes for the compressive strength of concrete using AI-aided tools

Investigating the effectiveness of carbon nanotubes for the compressive strength of concrete using AI-aided tools

Drying shrinkage and crack width prediction using machine learning in mortars containing different types of industrial by-product fine aggregates

Analyzing chloride diffusion for durability predictions of concrete using contemporary machine learning strategies

Contact Info

Product

Resources

About