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

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. HIMOTO, Keisuke & SAWADA, Yuto & OHMIYA, Yoshifumi, 2024. "Quantifying fire resilience of buildings considering the impact of water damage accompanied by fire extinguishment," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. 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).
    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.
    5. 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).

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