IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i8p4334-d535440.html
   My bibliography  Save this article

Machine Learning-Based Approach for Seismic Damage Prediction Method of Building Structures Considering Soil-Structure Interaction

Author

Listed:
  • Jongmuk Won

    (Department of Civil and Environmental Engineering, University of Ulsan, Daehak-ro 93, Nam-gu, Ulsan 680-749, Korea)

  • Jiuk Shin

    (Department of Architectural Engineering, Gyeongsang National University (GNU), Jinju-daero, Jinju 660-701, Korea)

Abstract

Conventional seismic performance evaluation methods for building structures with soil–structure interaction effects are inefficient for regional seismic damage assessment as a predisaster management system. Therefore, this study presented the framework to develop an artificial neural network-based model, which can rapidly predict seismic responses with soil–structure interaction effects and determine the seismic performance levels. To train, validate and test the model, 11 input parameters were selected as main parameters, and the seismic responses with the soil–structure interaction were generated using a multistep analysis process proposed in this study. The artificial neural network model generated reliable seismic responses with the soil–structure interaction effects, and it rapidly extended the seismic response database using a simple structure and soil information. This data generation method with high accuracy and speed can be utilized as a regional seismic assessment tool for safe and sustainable structures against natural disasters.

Suggested Citation

  • Jongmuk Won & Jiuk Shin, 2021. "Machine Learning-Based Approach for Seismic Damage Prediction Method of Building Structures Considering Soil-Structure Interaction," Sustainability, MDPI, vol. 13(8), pages 1-14, April.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4334-:d:535440
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/8/4334/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/8/4334/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hoang Dang-Vu & Jiuk Shin & Kihak Lee, 2020. "Seismic Fragility Assessment of Columns in a Piloti-Type Building Retrofitted with Additional Shear Walls," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
    2. Junwon Seo & Jong Wan Hu & Burte Davaajamts, 2015. "Seismic Performance Evaluation of Multistory Reinforced Concrete Moment Resisting Frame Structure with Shear Walls," Sustainability, MDPI, vol. 7(10), pages 1-22, October.
    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. Jaime de-Miguel-Rodríguez & Antonio Morales-Esteban & María-Victoria Requena-García-Cruz & Beatriz Zapico-Blanco & María-Luisa Segovia-Verjel & Emilio Romero-Sánchez & João Manuel Carvalho-Estêvão, 2022. "Fast Seismic Assessment of Built Urban Areas with the Accuracy of Mechanical Methods Using a Feedforward Neural Network," Sustainability, MDPI, vol. 14(9), pages 1-27, April.

    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.

      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:jsusta:v:13:y:2021:i:8:p:4334-:d:535440. 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.