• search hit 4 of 2588
Back to Result List

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

  • 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 sixThe 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.show moreshow less

Download full text files

  • Volltexteng
    (1349KB)

    Gefördert durch das Programm Open Access Publizieren der DFG und den Publikationsfonds der Bauhaus-Universität Weimar.

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Document Type:Article
Author: Ehsan HarirchianORCiD, Tom LahmerORCiDGND, Shahla RasulzadeORCiD
DOI (Cite-Link):https://doi.org/10.3390/en13082060Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200504-41575Cite-Link
URL:https://www.mdpi.com/1996-1073/13/8/2060/htm
Parent Title (German):Energies
Publisher:MDPI
Place of publication:Basel
Language:English
Date of Publication (online):2020/04/20
Date of first Publication:2020/04/20
Release Date:2020/05/04
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:Volume 13, Issue 8, 2060
Pagenumber:16
Tag:artificial neural network; earthquake damage; seismic vulnerability
GND Keyword:Erdbeben; Maschinelles Lernen
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften
BKL-Classification:50 Technik allgemein
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2020
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)