GraFar - Repository of the Faculty of Civil Engineering
Faculty of Civil Engineering of the University of Belgrade
    • English
    • Српски
    • Српски (Serbia)
  • English 
    • English
    • Serbian (Cyrillic)
    • Serbian (Latin)
  • Login
View Item 
  •   GraFar
  • GraFar
  • Катедра за управљање пројектима у грађевинарству
  • View Item
  •   GraFar
  • GraFar
  • Катедра за управљање пројектима у грађевинарству
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Sampling and Machine Learning Methods for a Rapid Earthquake Loss Assessment System

Authorized Users Only
2018
Authors
Kovačević, Miloš
Stojadinović, Zoran I.
Marinković, Dejan
Stojadinović, Božidar
Conference object (Published version)
Metadata
Show full item record
Abstract
Rapid earthquake damage and loss assessment is crucial both for insuring the safety of inhabitants in the immediate aftermath of an earthquake and for the recovery of the stricken communities in the long run. This paper investigates the potential of different machine learning methods for building a rapid earthquake loss assessment system intended for residential houses in a municipal area. The system is trained on a pre-earthquake selected representative set of residential houses, after observing their damage and loss states. Two representative sampling strategies and three machine learning algorithms are described and evaluated on the 2010 Kraljevo M5.4 earthquake data set. The proposed models showed satisfactory accuracy in predicting the total expected repair cost (less than 20% error with the representative sample size of 10% of the inventory). The approach is independent of geological and earthquake data and does not require local peak ground acceleration values.
Source:
11th National Conference on Earthquake Engineering, 2018
Funding / projects:
  • Resilient Kraljevo: Management of the Post-Earthquake Community Reconstruction Effort
[ Google Scholar ]
Handle
https://hdl.handle.net/21.15107/rcub_grafar_2176
URI
https://grafar.grf.bg.ac.rs/handle/123456789/2176
Collections
  • Катедра за управљање пројектима у грађевинарству
Institution/Community
GraFar
TY  - CONF
AU  - Kovačević, Miloš
AU  - Stojadinović, Zoran I.
AU  - Marinković, Dejan
AU  - Stojadinović, Božidar
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2176
AB  - Rapid earthquake damage and loss assessment is crucial both for insuring the safety of inhabitants in the immediate aftermath of an earthquake and for the recovery of the stricken communities in the long run. This paper investigates the potential of different machine learning methods for building a rapid earthquake loss assessment system intended for residential houses in a municipal area. The system is trained on a pre-earthquake selected representative set of residential houses, after observing their damage and loss states. Two representative sampling strategies and three machine learning algorithms are described and evaluated on the 2010 Kraljevo M5.4 earthquake data set. The proposed models showed satisfactory accuracy in predicting the total expected repair cost (less than 20% error with the representative sample size of 10% of the inventory). The approach is independent of geological and earthquake data and does not require local peak ground acceleration values.
C3  - 11th National Conference on Earthquake Engineering
T1  - Sampling and Machine Learning Methods for a Rapid Earthquake Loss Assessment System
UR  - https://hdl.handle.net/21.15107/rcub_grafar_2176
ER  - 
@conference{
author = "Kovačević, Miloš and Stojadinović, Zoran I. and Marinković, Dejan and Stojadinović, Božidar",
year = "2018",
abstract = "Rapid earthquake damage and loss assessment is crucial both for insuring the safety of inhabitants in the immediate aftermath of an earthquake and for the recovery of the stricken communities in the long run. This paper investigates the potential of different machine learning methods for building a rapid earthquake loss assessment system intended for residential houses in a municipal area. The system is trained on a pre-earthquake selected representative set of residential houses, after observing their damage and loss states. Two representative sampling strategies and three machine learning algorithms are described and evaluated on the 2010 Kraljevo M5.4 earthquake data set. The proposed models showed satisfactory accuracy in predicting the total expected repair cost (less than 20% error with the representative sample size of 10% of the inventory). The approach is independent of geological and earthquake data and does not require local peak ground acceleration values.",
journal = "11th National Conference on Earthquake Engineering",
title = "Sampling and Machine Learning Methods for a Rapid Earthquake Loss Assessment System",
url = "https://hdl.handle.net/21.15107/rcub_grafar_2176"
}
Kovačević, M., Stojadinović, Z. I., Marinković, D.,& Stojadinović, B.. (2018). Sampling and Machine Learning Methods for a Rapid Earthquake Loss Assessment System. in 11th National Conference on Earthquake Engineering.
https://hdl.handle.net/21.15107/rcub_grafar_2176
Kovačević M, Stojadinović ZI, Marinković D, Stojadinović B. Sampling and Machine Learning Methods for a Rapid Earthquake Loss Assessment System. in 11th National Conference on Earthquake Engineering. 2018;.
https://hdl.handle.net/21.15107/rcub_grafar_2176 .
Kovačević, Miloš, Stojadinović, Zoran I., Marinković, Dejan, Stojadinović, Božidar, "Sampling and Machine Learning Methods for a Rapid Earthquake Loss Assessment System" in 11th National Conference on Earthquake Engineering (2018),
https://hdl.handle.net/21.15107/rcub_grafar_2176 .

DSpace software copyright © 2002-2015  DuraSpace
About the GraFar Repository | Send Feedback

OpenAIRERCUB
 

 

All of DSpaceCommunitiesAuthorsTitlesSubjectsThis institutionAuthorsTitlesSubjects

Statistics

View Usage Statistics

DSpace software copyright © 2002-2015  DuraSpace
About the GraFar Repository | Send Feedback

OpenAIRERCUB