IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i8p2060-d348085.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ehsan Harirchian & Tom Lahmer & Shahla Rasulzade, 2020. "Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network," Energies, MDPI, vol. 13(8), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2060-:d:348085
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/8/2060/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/8/2060/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mehmet Inel & Sevket Senel & Selcuk Toprak & Yasemin Manav, 2008. "Seismic risk assessment of buildings in urban areas: a case study for Denizli, Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 46(3), pages 265-285, September.
    2. Jiangang Hao & Tin Kam Ho, 2019. "Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language," Journal of Educational and Behavioral Statistics, , vol. 44(3), pages 348-361, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ehsan Harirchian & Tom Lahmer & Vandana Kumari & Kirti Jadhav, 2020. "Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings," Energies, MDPI, vol. 13(13), pages 1-15, June.
    2. Angelo Cardellicchio & Sergio Ruggieri & Valeria Leggieri & Giuseppina Uva, 2021. "View VULMA: Data Set for Training a Machine-Learning Tool for a Fast Vulnerability Analysis of Existing Buildings," Data, MDPI, vol. 7(1), pages 1-14, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hung-Chih Hung & Ming-Chin Ho & Yi-Jie Chen & Chang-Yi Chian & Su-Ying Chen, 2013. "Integrating long-term seismic risk changes into improving emergency response and land-use planning: a case study for the Hsinchu City, Taiwan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 69(1), pages 491-508, October.
    2. Ehab Issa El-Sayed & Salah K. ElSayed & Mohammad Alsharef, 2024. "Data-Driven Approaches for State-of-Charge Estimation in Battery Electric Vehicles Using Machine and Deep Learning Techniques," Sustainability, MDPI, vol. 16(21), pages 1-21, October.
    3. Muideen Adegoke & Alaka Hafiz & Saheed Ajayi & Razak Olu-Ajayi, 2022. "Application of Multilayer Extreme Learning Machine for Efficient Building Energy Prediction," Energies, MDPI, vol. 15(24), pages 1-21, December.
    4. Taleh Agasiev & Anatoly Karpenko, 2024. "Exploratory Landscape Validation for Bayesian Optimization Algorithms," Mathematics, MDPI, vol. 12(3), pages 1-21, January.
    5. Harold Doran, 2023. "A Collection of Numerical Recipes Useful for Building Scalable Psychometric Applications," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 37-69, February.
    6. A. Sarris & C. Loupasakis & P. Soupios & V. Trigkas & F. Vallianatos, 2010. "Earthquake vulnerability and seismic risk assessment of urban areas in high seismic regions: application to Chania City, Crete Island, Greece," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 54(2), pages 395-412, August.
    7. Lilia Tightiz & Joon Yoo, 2022. "A Review on a Data-Driven Microgrid Management System Integrating an Active Distribution Network: Challenges, Issues, and New Trends," Energies, MDPI, vol. 15(22), pages 1-24, November.
    8. Joselyne Solórzano & Fernando Morante-Carballo & Néstor Montalván-Burbano & Josué Briones-Bitar & Paúl Carrión-Mero, 2022. "A Systematic Review of the Relationship between Geotechnics and Disasters," Sustainability, MDPI, vol. 14(19), pages 1-31, October.
    9. Anthony Njuguna Matheri & Zanele Blessed Sithole & Belaid Mohamed, 2024. "Data-Driven Circular Economy of Biowaste to Bioenergy with Conventional Prediction Modelling and Machine Learning," Circular Economy and Sustainability, Springer, vol. 4(2), pages 929-950, June.
    10. Muhammad Khalid Anser & Munir Ahmad & Muhammad Azhar Khan & Abdelmohsen A. Nassani & Mohamed Haffar & Khalid Zaman, 2024. "The “IMPACT” of Web of Science Coverage and Scientific and Technical Journal Articles on the World’s Income: Scientific Informatics and the Knowledge-Driven Economy," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 3147-3173, March.
    11. Ayed Alwadain & Rao Faizan Ali & Amgad Muneer, 2023. "Estimating Financial Fraud through Transaction-Level Features and Machine Learning," Mathematics, MDPI, vol. 11(5), pages 1-15, February.
    12. Maksymilian Mądziel & Tiziana Campisi, 2023. "Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database," Energies, MDPI, vol. 16(3), pages 1-18, February.
    13. Ann-Kathrin Edrich & Anil Yildiz & Ribana Roscher & Alexander Bast & Frank Graf & Julia Kowalski, 2024. "A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(9), pages 8953-8982, July.
    14. Campos, Inês & Korsnes, Marius & Labanca, Nicola & Bertoldi, Paolo, 2024. "Can renewable energy prosumerism cater for sufficiency and inclusion?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    15. Saheed, Yakub Kayode & Abdulganiyu, Oluwadamilare Harazeem & Majikumna, Kaloma Usman & Mustapha, Musa & Workneh, Abebaw Degu, 2024. "ResNet50-1D-CNN: A new lightweight resNet50-One-dimensional convolution neural network transfer learning-based approach for improved intrusion detection in cyber-physical systems," International Journal of Critical Infrastructure Protection, Elsevier, vol. 45(C).
    16. Wenhua Qi & Guiwu Su & Lei Sun & Fan Yang & Yang Wu, 2017. "“Internet+” approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth: a case study in Tangshan, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 86(1), pages 107-139, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2060-:d:348085. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.