IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i14p3598-d383779.html
   My bibliography  Save this article

A Statistical Approach for Predicting Airtightness in Residential Units of Reinforced Concrete Apartment Buildings in Korea

Author

Listed:
  • Kyung-Hwan Ji

    (Department of Architectural Engineering, Inha University, Incheon 22212, Korea)

  • Hyun-Kook Shin

    (Department of Architectural Engineering, Inha University, Incheon 22212, Korea)

  • Seungwoo Han

    (Department of Architectural Engineering, Inha University, Incheon 22212, Korea)

  • Jae-Hun Jo

    (Department of Architectural Engineering, Inha University, Incheon 22212, Korea)

Abstract

In this study, a model equation is derived that uses a statistical analysis based on empirical models to predict the airtightness of reinforced concrete apartment buildings popular in Asian regions. Airtightness data from 486 units personally measured by the authors in the past eight years are used. As major variables used in the prediction model, two groups of variables are configured for the geometric components of the envelope, which is a major path of airflow in a building and is where air infiltration and leakage occur. The two groups of variables represent (1) the areas of the individual components forming the envelope and (2) the connection lengths between different components of the envelope. For the effective prediction of airtightness, correlation analysis and multiple regression analysis were applied step by step in this study. The results of the correlation analysis indicated that the areas of the slab and the window are the area variables that present the greatest impact, whereas the perimeter length of the window is the connection length variable that presents the greatest impact. Using a multiple linear regression analysis method, airtightness prediction model equations can be derived, and it is found that the model with variables for area is able to predict airtightness more accurately compared to the two models derived from variables for connection length and all variables for area and connection length. Although the statistical approach in this study shows a limitation in that the prediction results may vary depending on the attributes and type of data collected by countries, the methodology and procedure in this study contribute to similar studies for making prediction models and finding the influence of variables in the future with high applicability and feasibility.

Suggested Citation

  • Kyung-Hwan Ji & Hyun-Kook Shin & Seungwoo Han & Jae-Hun Jo, 2020. "A Statistical Approach for Predicting Airtightness in Residential Units of Reinforced Concrete Apartment Buildings in Korea," Energies, MDPI, vol. 13(14), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3598-:d:383779
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/14/3598/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/14/3598/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sakdirat Kaewunruen & Jessada Sresakoolchai & Lalida Kerinnonta, 2019. "Potential Reconstruction Design of an Existing Townhouse in Washington DC for Approaching Net Zero Energy Building Goal," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    2. Sakdirat Kaewunruen & Panrawee Rungskunroch & Joshua Welsh, 2018. "A Digital-Twin Evaluation of Net Zero Energy Building for Existing Buildings," Sustainability, MDPI, vol. 11(1), pages 1-22, December.
    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. Valdas Paukštys & Gintaris Cinelis & Jūratė Mockienė & Mindaugas Daukšys, 2021. "Airtightness and Heat Energy Loss of Mid-Size Terraced Houses Built of Different Construction Materials," Energies, MDPI, vol. 14(19), pages 1-23, October.

    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. Sakdirat Kaewunruen & Jessada Sresakoolchai & Wentao Ma & Olisa Phil-Ebosie, 2021. "Digital Twin Aided Vulnerability Assessment and Risk-Based Maintenance Planning of Bridge Infrastructures Exposed to Extreme Conditions," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    2. Sakdirat Kaewunruen & Jessada Sresakoolchai & Zhihao Zhou, 2020. "Sustainability-Based Lifecycle Management for Bridge Infrastructure Using 6D BIM," Sustainability, MDPI, vol. 12(6), pages 1-13, March.
    3. Hossein Omrany & Karam M. Al-Obaidi & Amreen Husain & Amirhosein Ghaffarianhoseini, 2023. "Digital Twins in the Construction Industry: A Comprehensive Review of Current Implementations, Enabling Technologies, and Future Directions," Sustainability, MDPI, vol. 15(14), pages 1-26, July.
    4. Rafaela Bortolini & Raul Rodrigues & Hamidreza Alavi & Luisa Felix Dalla Vecchia & Núria Forcada, 2022. "Digital Twins’ Applications for Building Energy Efficiency: A Review," Energies, MDPI, vol. 15(19), pages 1-17, September.
    5. Fabrizio M. Amoruso & Udo Dietrich & Thorsten Schuetze, 2019. "Integrated BIM-Parametric Workflow-Based Analysis of Daylight Improvement for Sustainable Renovation of an Exemplary Apartment in Seoul, Korea," Sustainability, MDPI, vol. 11(9), pages 1-29, May.
    6. Giuseppe Desogus & Emanuela Quaquero & Giulia Rubiu & Gianluca Gatto & Cristian Perra, 2021. "BIM and IoT Sensors Integration: A Framework for Consumption and Indoor Conditions Data Monitoring of Existing Buildings," Sustainability, MDPI, vol. 13(8), pages 1-22, April.
    7. Sakdirat Kaewunruen & Jessada Sresakoolchai & Lalida Kerinnonta, 2019. "Potential Reconstruction Design of an Existing Townhouse in Washington DC for Approaching Net Zero Energy Building Goal," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    8. Elżbieta Jadwiga Szymańska & Maria Kubacka & Joanna Woźniak & Jan Polaszczyk, 2022. "Analysis of Residential Buildings in Poland for Potential Energy Renovation toward Zero-Emission Construction," Energies, MDPI, vol. 15(24), pages 1-24, December.
    9. Jaromir Vrbka & Tomas Krulicky & Tomas Brabenec & Jan Hejda, 2020. "Determining the Increase in a Building’s Appreciation Rate Due to a Reconstruction," Sustainability, MDPI, vol. 12(18), pages 1-13, September.
    10. Maria Kozlovska & Stefan Petkanic & Frantisek Vranay & Dominik Vranay, 2023. "Enhancing Energy Efficiency and Building Performance through BEMS-BIM Integration," Energies, MDPI, vol. 16(17), pages 1-23, August.
    11. Casey R. Corrado & Suzanne M. DeLong & Emily G. Holt & Edward Y. Hua & Andreas Tolk, 2022. "Combining Green Metrics and Digital Twins for Sustainability Planning and Governance of Smart Buildings and Cities," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    12. Youngduk Cho & Sanghyo Lee & Joosung Lee & Jaejun Kim, 2021. "Analysis of the Repair Time of Finishing Works Using a Probabilistic Approach for Efficient Residential Buildings Maintenance Strategies," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    13. Lauren Etxepare & Iñigo Leon & Maialen Sagarna & Iñigo Lizundia & Eneko Jokin Uranga, 2020. "Advanced Intervention Protocol in the Energy Rehabilitation of Heritage Buildings: A Miñones Barracks Case Study," Sustainability, MDPI, vol. 12(15), pages 1-33, August.
    14. Xinman Guo & Sunliang Cao & Yang Xu & Xiaolin Zhu, 2021. "The Feasibility of Using Zero-Emission Electric Boats to Enhance the Techno-Economic Performance of an Ocean-Energy-Supported Coastal Hotel Building," Energies, MDPI, vol. 14(24), pages 1-42, December.
    15. Sakdirat Kaewunruen & Shijie Peng & Olisa Phil-Ebosie, 2020. "Digital Twin Aided Sustainability and Vulnerability Audit for Subway Stations," Sustainability, MDPI, vol. 12(19), pages 1-17, September.
    16. do Amaral, J.V.S. & dos Santos, C.H. & Montevechi, J.A.B. & de Queiroz, A.R., 2023. "Energy Digital Twin applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    17. Bianca Goia & Tudor Cioara & Ionut Anghel, 2022. "Virtual Power Plant Optimization in Smart Grids: A Narrative Review," Future Internet, MDPI, vol. 14(5), pages 1-22, April.
    18. Meijiao Song & Jun Cai & Yisi Xue, 2023. "From Technological Sustainability to Social Sustainability: An Analysis of Hotspots and Trends in Residential Design Evaluation," Sustainability, MDPI, vol. 15(13), pages 1-19, June.

    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:14:p:3598-:d:383779. 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.