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Automated building classification framework using convolutional neural network

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Adha, Augusta, Pamuncak, Arya Panji, Qiao, Wen and Laory, Irwanda (2022) Automated building classification framework using convolutional neural network. Cogent Engineering, 9 (1). doi:10.1080/23311916.2022.2065900

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Official URL: https://doi.org/10.1080/23311916.2022.2065900

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Abstract

Despite extensive study, performing Rapid visual screening is still a challenging task for many countries. The challenges include the lack of trained engineers, limited resources, and a large building inventory to detect. One of the most important aspect in rapid visual screening is to establish the building classification based on the guidelines’ specific criteria. This study proposes a general framework based on Convolutional Neural Network to perform automated building classification for the rapid visual screening procedure. The method classifies buildings based on the Federal Emergency Management Agency (FEMA)-154 guidelines and uses transfer learning techniques from a pre-trained network. The Indonesian building portfolio is used as a case study and a dataset of building images generated through web-scraping on Google Search™ engines and Google StreetView™ website is used for the method validation. Results show that the proposed framework has promising potential to automate the building classification based on FEMA-154 guidelines.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > T Technology (General)
T Technology > TH Building construction
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Buildings -- Earthquake effects , Buildings -- Earthquake effects -- Computer simulation, Buildings -- Classification -- Data processing, Automation , Human-computer interaction , Neural networks (Computer science) , Computer graphics , Buildings -- Earthquake effects -- Computer-aided design
Journal or Publication Title: Cogent Engineering
Publisher: Informa UK Limited
ISSN: 2331-1916
Official Date: 2022
Dates:
DateEvent
2022Published
2 May 2022Available
5 April 2022Accepted
Volume: 9
Number: 1
DOI: 10.1080/23311916.2022.2065900
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
S-2160/ LPDP.4/2019Indonesia Endowment Fund for Education https://lpdp.kemenkeu.go.id/en/investasi/dana-abadi/
PRJ-589/LPDP.3/2017Indonesia Endowment Fund for Education ttps://lpdp.kemenkeu.go.id/en/investasi/dana-abadi/

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