IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v216y2021ics0951832021005329.html
   My bibliography  Save this article

Computational framework for assessing the fire resilience of buildings using the multi-layer zone model

Author

Listed:
  • Himoto, Keisuke
  • Suzuki, Keichi

Abstract

Fire resilience is a measure that quantifies functional continuity of a building damaged by a fire. Despite its numerous advantages, only a few studies have attempted to assess fire resilience. In this study, a computational framework using the multi-layer zone model was developed for assessing the fire resilience of buildings. The multi-layer zone model is an advanced form of the classical one-layer and two-layer zone models; the model divides rooms of analysis into multiple horizontal control volumes, called zones, for the governing equations of the fire induced environment behavior. This model is suitable for evaluating damage of building components in the fully developed stage of a fire with almost uniform temperature distribution inside the rooms and in the earlier stages with vertically stratified temperature distributions. This is an important feature of fire hazard evaluation because a small rise in temperature or dispersion of smoke can cause damage to certain types of building components, such as non-structural members, equipment systems, and stored items with relatively low fire resistivity. All the components should remain undamaged for the functional continuity of buildings. The framework assesses the damage ratio of a building by aggregating the building components of each zone that is calculated using vulnerability functions. Based on recent statistics on fire incidents and building refurbishment in Japan, the damage ratio is further converted to recovery cost and time required for calculating the fire resilience. Hazard mitigating functions of fire protection equipment systems, i.e., fire extinguisher, indoor fire hydrant, sprinkler system, mechanical smoke exhaust system, and fire alarm system, were incorporated in the framework considering occupants’ response to a fire. As a case study, the fire resilience of a five-story office building was assessed using the Monte Carlo approach, where uncertain parameters associated with the fire source, fire protection equipment systems, occupants, and fire service were considered as variables. As a result, although a building component with relatively high fire resistivity (i.e., structural members) is a major factor influencing the cost and time required for recovery, extinguishment in the early stage of a fire was particularly important to improve the fire resilience of buildings. In this study, the damage ratios of building components were evaluated by the multi-layer zone model; the proposed framework provides an overview of the computational procedure for assessing the fire resilience of buildings, which can be a guide for the other types of fire hazard models.

Suggested Citation

  • Himoto, Keisuke & Suzuki, Keichi, 2021. "Computational framework for assessing the fire resilience of buildings using the multi-layer zone model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
  • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021005329
    DOI: 10.1016/j.ress.2021.108023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021005329
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2021.108023?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ouyang, Min & Wang, Zhenghua, 2015. "Resilience assessment of interdependent infrastructure systems: With a focus on joint restoration modeling and analysis," Reliability Engineering and System Safety, Elsevier, vol. 141(C), pages 74-82.
    2. Nozhati, Saeed & Sarkale, Yugandhar & Ellingwood, Bruce & K.P. Chong, Edwin & Mahmoud, Hussam, 2019. "Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 116-126.
    3. Goldbeck, Nils & Angeloudis, Panagiotis & Ochieng, Washington Y., 2019. "Resilience assessment for interdependent urban infrastructure systems using dynamic network flow models," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 62-79.
    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. Wang, Ning & Xu, Yan & Wang, Sutong, 2022. "Interpretable boosting tree ensemble method for multisource building fire loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Sun, Hao & Wang, Haiqing & Yang, Ming & Reniers, Genserik, 2022. "A STAMP-based approach to quantitative resilience assessment of chemical process systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Yin, Jiateng & Ren, Xianliang & Liu, Ronghui & Tang, Tao & Su, Shuai, 2022. "Quantitative analysis for resilience-based urban rail systems: A hybrid knowledge-based and data-driven approach," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. Xianghua Xu & Ningshuang Zeng & Mengmei Li & Yan Liu & Qiming Li, 2024. "Enhancing Fire Resilience in High-Tech Electronic Plants for Sustainable Development: Combining System Composition with Organizational Management," Sustainability, MDPI, vol. 16(4), pages 1-17, February.

    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. Trucco, Paolo & Petrenj, Boris, 2023. "Characterisation of resilience metrics in full-scale applications to interdependent infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Lu, Qing-Chang & Xu, Peng-Cheng & Zhao, Xiangmo & Zhang, Lei & Li, Xiaoling & Cui, Xin, 2022. "Measuring network interdependency between dependent networks: A supply-demand-based approach," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    3. Poulin, Craig & Kane, Michael B., 2021. "Infrastructure resilience curves: Performance measures and summary metrics," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. Ilalokhoin, Ohis & Pant, Raghav & Hall, Jim W., 2023. "A model and methodology for resilience assessment of interdependent rail networks – Case study of Great Britain's rail network," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    5. Sang, Maosheng & Ding, Yi & Bao, Minglei & Li, Siying & Ye, Chengjin & Fang, Youtong, 2021. "Resilience-based restoration strategy optimization for interdependent gas and power networks," Applied Energy, Elsevier, vol. 302(C).
    6. Shuai Lin & Limin Jia & Hengrun Zhang & Yanhui Wang, 2021. "A method for assessing resilience of high-speed EMUs considering a network-based system topology and performance data," Journal of Risk and Reliability, , vol. 235(5), pages 877-895, October.
    7. Caputo, A.C. & Donati, L. & Salini, P., 2023. "Estimating resilience of manufacturing plants to physical disruptions: Model and application," International Journal of Production Economics, Elsevier, vol. 266(C).
    8. Adel Mottahedi & Farhang Sereshki & Mohammad Ataei & Ali Nouri Qarahasanlou & Abbas Barabadi, 2021. "The Resilience of Critical Infrastructure Systems: A Systematic Literature Review," Energies, MDPI, vol. 14(6), pages 1-32, March.
    9. Zou, Qiling & Chen, Suren, 2021. "Resilience-based Recovery Scheduling of Transportation Network in Mixed Traffic Environment: A Deep-Ensemble-Assisted Active Learning Approach," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    10. Yang, Zhuyu & Barroca, Bruno & Laffréchine, Katia & Weppe, Alexandre & Bony-Dandrieux, Aurélia & Daclin, Nicolas, 2023. "A multi-criteria framework for critical infrastructure systems resilience," International Journal of Critical Infrastructure Protection, Elsevier, vol. 42(C).
    11. Reilly, Allison C. & Baroud, Hiba & Flage, Roger & Gerst, Michael D., 2021. "Sources of uncertainty in interdependent infrastructure and their implications," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    12. Hao, Yucheng & Jia, Limin & Zio, Enrico & Wang, Yanhui & Small, Michael & Li, Man, 2023. "Improving resilience of high-speed train by optimizing repair strategies," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    13. Yang, Bofan & Zhang, Lin & Zhang, Bo & Xiang, Yang & An, Lei & Wang, Wenfeng, 2022. "Complex equipment system resilience: Composition, measurement and element analysis," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    14. Zou, Qiling & Chen, Suren, 2019. "Enhancing resilience of interdependent traffic-electric power system," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    15. Liu, Huan & Tatano, Hirokazu & Pflug, Georg & Hochrainer-Stigler, Stefan, 2021. "Post-disaster recovery in industrial sectors: A Markov process analysis of multiple lifeline disruptions," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    16. Ahmadi, Somayeh & Saboohi, Yadollah & Vakili, Ali, 2021. "Frameworks, quantitative indicators, characters, and modeling approaches to analysis of energy system resilience: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    17. Kameshwar, Sabarethinam & Cox, Daniel T. & Barbosa, Andre R. & Farokhnia, Karim & Park, Hyoungsu & Alam, Mohammad S. & van de Lindt, John W., 2019. "Probabilistic decision-support framework for community resilience: Incorporating multi-hazards, infrastructure interdependencies, and resilience goals in a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    18. Han, Lin & Zhao, Xudong & Chen, Zhilong & Gong, Huadong & Hou, Benwei, 2021. "Assessing resilience of urban lifeline networks to intentional attacks," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    19. Fang, Yi-Ping & Sansavini, Giovanni, 2019. "Optimum post-disruption restoration under uncertainty for enhancing critical infrastructure resilience," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 1-11.
    20. Xu, Min & Ouyang, Min & Hong, Liu & Mao, Zijun & Xu, Xiaolin, 2022. "Resilience-driven repair sequencing decision under uncertainty for critical infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 221(C).

    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:eee:reensy:v:216:y:2021:i:c:s0951832021005329. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.