Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network

Ehsan Harirchian, Tom Lahmer and Shahla Rasulzade
Additional contact information
Ehsan Harirchian: Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, Marienstr. 15, D-99423 Weimar, Germany
Tom Lahmer: Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, Marienstr. 15, D-99423 Weimar, Germany
Shahla Rasulzade: Research Group Theoretical Computer Science/Formal methods, School of Electrical Engineering and Computer Science, Universität Kassel, Wilhelmshöher Allee 73, D-34131 Kassel, Germany

Energies, 2020, vol. 13, issue 8, 1-16

Abstract: The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the Düzce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs.

Keywords: earthquake damage; seismic vulnerability; artificial neural network; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/8/2060/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/8/2060/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:8:p:2060-:d:348085

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().