Autonomous damage segmentation of post-fire reinforced concrete structural components

Liu, Caiwei; Wang, Pengfei; Wang, Xinyu; Miao, Jijun

doi:10.1016/j.aei.2024.102498

Advanced Engineering Informatics

2024

DOI: 10.1016/j.aei.2024.102498

|View full text |Cite

Autonomous damage segmentation of post-fire reinforced concrete structural components

Caiwei Liu,

Pengfei Wang,

Xinyu Wang

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 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Multi-Level Optimisation of Feature Extraction Networks for Concrete Surface Crack Detection

Elghaish,

Matarneh,

Rahimian

et al. 2024

Developments in the Built Environment

View full text Add to dashboard Cite

Multi-Level Optimisation of Feature Extraction Networks for Concrete Surface Crack Detection

Elghaish,

Matarneh,

Rahimian

et al. 2024

Developments in the Built Environment

View full text Add to dashboard Cite

Kernel extreme learning machine and finite element method fusion fire damage prediction of concrete structures

Sun,

2024

Structures

View full text Add to dashboard Cite

Multicategory fire damage detection of post‐fire reinforced concrete structural components

Wang,

Liu,

Wang

et al. 2024

Computer aided Civil Eng

View full text Add to dashboard Cite

This paper introduces an enhanced you only look once (YOLO) v5s‐D network customized for detecting various categories of damage to post‐fire reinforced concrete (RC) components. These damage types encompass surface soot, cracks, concrete spalling, and rebar exposure. A dataset containing 1536 images depicting damaged RC components was compiled. By integrating ShuffleNet, adaptive attention mechanisms, and a feature enhancement module, the capability of the network for multi‐scale feature extraction in complex backgrounds was improved, alongside a reduction in model parameters. Consequently, YOLOv5s‐D achieved a detection accuracy of 93%, marking an 11% enhancement over the baseline YOLOv5s network. Comparison and ablation tests conducted on different modules, varying dataset sizes, against other state‐of‐the‐art networks, and on public datasets validate the resilience, superiority, and generalization capability of YOLOv5s‐D. Finally, an application leveraging YOLOv5s‐D was developed and integrated into a mobile device to facilitate real‐time detection of post‐fire damaged RC components. This application can integrate diverse fire scenarios and data types, expanding its scope in future. The proposed detection method compensates for the subjective limitations of manual inspections, providing a reference for damage assessment.

show abstract

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

customersupport@researchsolutions.com

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

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

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.

Autonomous damage segmentation of post-fire reinforced concrete structural components

Cited by 3 publications

References 47 publications

Multi-Level Optimisation of Feature Extraction Networks for Concrete Surface Crack Detection

Multi-Level Optimisation of Feature Extraction Networks for Concrete Surface Crack Detection

Kernel extreme learning machine and finite element method fusion fire damage prediction of concrete structures

Multicategory fire damage detection of post‐fire reinforced concrete structural components

Contact Info

Product

Resources

About