Metal intrusion enhanced deep learning-based high temperature deterioration analysis of rock materials

Gao, Yuan; Yu, Zixuan; Yu, Shuyang; Sui, Hao; Feng, Tian; Liu, Yanming

doi:10.1016/j.enggeo.2024.107534

Engineering Geology

2024

DOI: 10.1016/j.enggeo.2024.107534

|View full text |Cite

Metal intrusion enhanced deep learning-based high temperature deterioration analysis of rock materials

Yuan Gao,

Zixuan Yu,

Shuyang Yu

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...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

UAV-Embedded Sensors and Deep Learning for Pathology Identification in Building Façades: A Review

Meira,

Guedes,

Bias

2024

Drones

View full text Add to dashboard Cite

The use of geotechnologies in the field of diagnostic engineering has become ever more present in the identification of pathological manifestations in buildings. The implementation of Unmanned Aerial Vehicles (UAVs) and embedded sensors has stimulated the search for new data processing and validation methods, considering the magnitude of the data collected during fieldwork and the absence of specific methodologies for each type of sensor. Regarding data processing, the use of deep learning techniques has become widespread, especially for the automation of processes that involve a great amount of data. However, just as with the increasing use of embedded sensors, deep learning necessitates the development of studies, particularly those focusing on neural networks that better represent the data to be analyzed. It also requires the enhancement of practices to be used in fieldwork, especially regarding data processing. In this context, the objective of this study is to review the existing literature on the use of embedded technologies in UAVs and deep learning for the identification and characterization of pathological manifestations present in building façades in order to develop a robust knowledge base that is capable of contributing to new investigations in this field of research.

show abstract

UAV-Embedded Sensors and Deep Learning for Pathology Identification in Building Façades: A Review

Meira,

Guedes,

Bias

2024

Drones

View full text Add to dashboard Cite

show abstract

Deep Learning-Assisted Analysis of GO-Reinforcing Effects on the Interfacial Transition Zone of CWRB

Yu,

Chen,

et al. 2024

Materials

View full text Add to dashboard Cite

Understanding the enhancing mechanisms of graphene oxide (GO) on the pore structure characteristics in the interfacial transition zone (ITZ) plays a crucial role in cemented waste rock backfill (CWRB) nanoreinforcement. In the present work, an innovative method based on metal intrusion techniques, backscattered electron (BSE) images, and deep learning is proposed to analyze the micro/nanoscale characteristics of microstructures in the GO-enhanced ITZ. The results showed that the addition of GO reduced the interpore connectivity and the porosity at different pore throats by 53.5–53.8%. GO promotes hydration reaction in the ITZ region; reduces pore circularity, solidity, and aspect ratio; enhances the mechanical strength of CWRB; and reduces transport performance to form a dense microstructure in the ITZ. Deep learning-based analyses were then proposed to classify and recognize BSE image features, with a high average recognition accuracy of 95.8%. After that, the deep Taylor decomposition (DTD) algorithm successfully located the enhanced features of graphene oxide modification in the ITZ. The calculation and verification of the typical pore optimization area of the location show that the optimization efficiency reaches 9.6–9.8%. This study not only demonstrated the deepening of the enhancement effect of GO on the pore structure in cement composites and provided new insights for the structural modification application of GO but also revealed the application prospect of GO in the strengthening of CWRB composites and solid waste recycling.

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.

Metal intrusion enhanced deep learning-based high temperature deterioration analysis of rock materials

Cited by 2 publications

References 57 publications

UAV-Embedded Sensors and Deep Learning for Pathology Identification in Building Façades: A Review

UAV-Embedded Sensors and Deep Learning for Pathology Identification in Building Façades: A Review

Deep Learning-Assisted Analysis of GO-Reinforcing Effects on the Interfacial Transition Zone of CWRB

Contact Info

Product

Resources

About