CIM-powered physics-based assessment of wind damages to building clusters considering trees

Gu, Donglian; Shuai, Qianwen; Wang, Yixing

doi:10.1016/j.dibe.2023.100178

Developments in the Built Environment

2023

DOI: 10.1016/j.dibe.2023.100178

|View full text |Cite

CIM-powered physics-based assessment of wind damages to building clusters considering trees

Donglian Gu,

Qianwen Shuai,

Yixing Wang

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 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Digital twin approach for enhancing urban resilience: A cycle between virtual space and the real world

Wang,

Yue,

et al. 2024

Resilient Cities and Structures

View full text Add to dashboard Cite

Digital twin approach for enhancing urban resilience: A cycle between virtual space and the real world

Wang,

Yue,

et al. 2024

Resilient Cities and Structures

View full text Add to dashboard Cite

Multi‐view street view image fusion for city‐scale assessment of wind damage to building clusters

Gu,

Shuai,

Zhang

et al. 2024

Computer aided Civil Eng

View full text Add to dashboard Cite

Global warming amplifies the risk of wind‐induced building damage in coastal cities worldwide. Existing numerical methods for predicting building damage under winds have been limited to virtual environments, given the prohibitive costs associated with establishing city‐scale window inventories. Hence, this study introduces a cost‐effective workflow for wind damage prediction of real built environments, where the window inventory can be established with the multi‐view street view image (SVI) fusion and artificial intelligence large model. The feasibility of the method is demonstrated based on two real‐world urban areas. Notably, the proposed multi‐view method surpasses both the single‐view and aerial image‐based methods in terms of window recognition accuracy. The increasing availability of SVIs opens up opportunities for applying the proposed method not only in disaster prevention but also in environmental and energy topics, thereby enhancing the resilience of cities and communities from multiple perspectives.

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

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.

CIM-powered physics-based assessment of wind damages to building clusters considering trees

Cited by 2 publications

References 35 publications

Digital twin approach for enhancing urban resilience: A cycle between virtual space and the real world

Digital twin approach for enhancing urban resilience: A cycle between virtual space and the real world

Multi‐view street view image fusion for city‐scale assessment of wind damage to building clusters

Contact Info

Product

Resources

About