IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v223y2022ics0951832022001582.html
   My bibliography  Save this article

Probabilistic demand models and performance-based fragility estimates for concrete protective structures subjected to missile impact

Author

Listed:
  • Gangolu, Jaswanth
  • Kumar, Ajay
  • Bhuyan, Kasturi
  • Sharma, Hrishikesh

Abstract

The manifold missile attacks upon structures and deficiency of codal provisions motivated the current study to develop probabilistic demand models for protective structures subjected to hard missile impact. These energy-based models are estimated using a defined performance-based design framework (PBD) with three performance levels associated with four damage states, i.e. from minor damage to total collapse. The evaluation of unknown model parameters is constructed using the Bayesian approach. The current and upcoming range of structural variables is chosen for numerical experimental design. LS-DYNA, a finite element (FE) numerical method, is used to generate necessary probabilistic data of Reinforced Concrete (RC) & Prestressed Concrete (PC) panels subject to a hard missile. Novel formulations for local damages of a target, such as perforation limit of RC panel & Ballistic limit of the missile for RC & PC panels, are estimated. The developed demand models are used to assess the fragility estimation of either panel under chosen performance levels for a containment structure located in Tarapur, Palghar, India. An accurate fragility plot and comparative study shows the consistency of probabilistic models with test results. These models consider aleatory and epistemic uncertainties in modeling, material & geometrical properties, strain rate & inertia effect, and complex interaction involved in an impact scenario.

Suggested Citation

  • Gangolu, Jaswanth & Kumar, Ajay & Bhuyan, Kasturi & Sharma, Hrishikesh, 2022. "Probabilistic demand models and performance-based fragility estimates for concrete protective structures subjected to missile impact," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
  • Handle: RePEc:eee:reensy:v:223:y:2022:i:c:s0951832022001582
    DOI: 10.1016/j.ress.2022.108497
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022001582
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2022.108497?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. Zhang, Xiaoge & Mahadevan, Sankaran, 2021. "Bayesian network modeling of accident investigation reports for aviation safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    2. Kammouh, Omar & Gardoni, Paolo & Cimellaro, Gian Paolo, 2020. "Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    3. Huang, Wencheng & Zhang, Yue & Kou, Xingyi & Yin, Dezhi & Mi, Rongwei & Li, Linqing, 2020. "Railway dangerous goods transportation system risk analysis: An Interpretive Structural Modeling and Bayesian Network combining approach," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Choe, Do-Eun & Gardoni, Paolo & Rosowsky, David & Haukaas, Terje, 2008. "Probabilistic capacity models and seismic fragility estimates for RC columns subject to corrosion," Reliability Engineering and System Safety, Elsevier, vol. 93(3), pages 383-393.
    5. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    6. Andreini, Marco & Gardoni, Paolo & Pagliara, Stefano & Sassu, Mauro, 2019. "Probabilistic models for the erosion rate in embankments and reliability analysis of earth dams," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 142-155.
    7. Gerassis, S. & Albuquerque, M.T.D. & García, J.F. & Boente, C. & Giráldez, E. & Taboada, J. & Martín, J.E., 2019. "Understanding complex blasting operations: A structural equation model combining Bayesian networks and latent class clustering," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 195-204.
    8. Cai, Baoping & Zhang, Yanping & Wang, Haifeng & Liu, Yonghong & Ji, Renjie & Gao, Chuntan & Kong, Xiangdi & Liu, Jing, 2021. "Resilience evaluation methodology of engineering systems with dynamic-Bayesian-network-based degradation and maintenance," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    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. Li, Xinbo & Gong, Jinxin, 2024. "Probabilistic evaluation of the leak-tightness function of the nuclear containment structure subjected to internal pressure," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Bhuyan, Kasturi & Sharma, Hrishikesh, 2024. "Probabilistic capacity models and fragility estimate for NRC and UHSC panels subjected to contact blast," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. Kishore, Katchalla Bala & Gangolu, Jaswanth & Ramancha, Mukesh K. & Bhuyan, Kasturi & Sharma, Hrishikesh, 2022. "Performance-based probabilistic deflection capacity models and fragility estimation for reinforced concrete column and beam subjected to blast loading," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    4. Kumar, Suman & Saxena, Sanchit & Sharma, Hrishikesh & Gangolu, Jaswanth & Prabhu, T. Ajeeth, 2023. "Development of design guidelines using probabilistic framework for the development of smart thickening fluid based ultra resistant adaptive kinematic soft human armor (SURAKSHA)," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    5. Zheng, Zhi & Tian, Aonan & Pan, Xiaolan & Ji, Duofa & Wang, Yong, 2024. "The damage-based fragility analysis and probabilistic safety assessment of containment under internal pressure," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Liu, Qiang & Huang, Delong & Zhang, Bin & Tang, Aiping & Xu, Xiuchen, 2024. "Developing a probability-based technique to improve the measurement of landslide vulnerability on regional roads," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    7. Bhuyan, Kasturi & Sharma, Hrishikesh, 2022. "Reliability analysis & performance-based code calibration for slabs/walls of protective structures subject to air blast loading," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

    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. Bhuyan, Kasturi & Sharma, Hrishikesh, 2022. "Reliability analysis & performance-based code calibration for slabs/walls of protective structures subject to air blast loading," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    2. 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).
    3. 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).
    4. Mottahedi, Adel & Sereshki, Farhang & Ataei, Mohammad & Qarahasanlou, Ali Nouri & Barabadi, Abbas, 2021. "Resilience estimation of critical infrastructure systems: Application of expert judgment," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Guo, Jian & Ma, Kaijiang, 2024. "Risk analysis for hazardous chemical vehicle-bridge transportation system: A dynamic Bayesian network model incorporating vehicle dynamics," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    6. Jiang, Qiangqiang & Cai, Baoping & Zhang, Yanping & Xie, Min & Liu, Cuiwei, 2023. "Resilience assessment methodology of natural gas network system under random leakage," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    7. He, Jingjing & Huang, Min & Wang, Wei & Wang, Shaohua & Guan, Xuefei, 2021. "An asymptotic stochastic response surface approach to reliability assessment under multi-source heterogeneous uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    8. Ballester-Ripoll, Rafael & Leonelli, Manuele, 2022. "Computing Sobol indices in probabilistic graphical models," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    9. Ramadhani, Adhitya & Khan, Faisal & Colbourne, Bruce & Ahmed, Salim & Taleb-Berrouane, Mohammed, 2022. "Resilience assessment of offshore structures subjected to ice load considering complex dependencies," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    10. Chen, Xiyuan & Ma, Xiaoping & Jia, Limin & Zhang, Zhipeng & Chen, Fei & Wang, Ruojin, 2024. "Causative analysis of freight railway accident in specific scenes using a data-driven Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    11. Sun, Hao & Yang, Ming & Wang, Haiqing, 2022. "A virtual experiment for measuring system resilience: A case of chemical process systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    12. Wang, Fang & Bai, Jie & Liu, Linlin & Ye, Tianyuan, 2024. "Temporal noisy-adder of bayesian network for scalable consecutive-k-out-of-n:F system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    13. Zhang, Yanping & Cai, Baoping & Liu, Yiliu & Jiang, Qiangqiang & Li, Wenchao & Feng, Qiang & Liu, Yonghong & Liu, Guijie, 2021. "Resilience assessment approach of mechanical structure combining finite element models and dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    14. Zhang, Lu & Cui, Li & Chen, Lujie & Dai, Jing & Jin, Ziyi & Wu, Hao, 2023. "A hybrid approach to explore the critical criteria of online supply chain finance to improve supply chain performance," International Journal of Production Economics, Elsevier, vol. 255(C).
    15. Wen, Tao & Gao, Qiuya & Chen, Yu-wang & Cheong, Kang Hao, 2022. "Exploring the vulnerability of transportation networks by entropy: A case study of Asia–Europe maritime transportation network," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    16. Wang, Tiao & Li, Chunhe & Zheng, Jian-jun & Hackl, Jürgen & Luan, Yao & Ishida, Tetsuya & Medepalli, Satya, 2023. "Consideration of coupling of crack development and corrosion in assessing the reliability of reinforced concrete beams subjected to bending," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    17. Kishore, Katchalla Bala & Gangolu, Jaswanth & Ramancha, Mukesh K. & Bhuyan, Kasturi & Sharma, Hrishikesh, 2022. "Performance-based probabilistic deflection capacity models and fragility estimation for reinforced concrete column and beam subjected to blast loading," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    18. Laihao Ma & Xiaoxue Ma & Jingwen Zhang & Qing Yang & Kai Wei, 2021. "Identifying the Weaker Function Links in the Hazardous Chemicals Road Transportation System in China," IJERPH, MDPI, vol. 18(13), pages 1-17, July.
    19. 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).
    20. Ziegler Haselein, Bruno & da Silva, Jonny Carlos & Hooey, Becky L., 2024. "Multiple machine learning modeling on near mid-air collisions: An approach towards probabilistic reasoning," Reliability Engineering and System Safety, Elsevier, vol. 244(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:223:y:2022:i:c:s0951832022001582. 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.