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A Combined Method to Build Bayesian Network for Fire Risk Assessment of Historical Buildings

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Abstract

At present, there are few fire risk assessment models for historical buildings with wooden components that are highly vulnerable to fires. Bayesian network (BN) is a practical risk assessment method that is helpful to risk management. Because many indicators affect the fire risk of historical buildings, the BN may have many nodes and a complex structure. As the parent nodes increase, the number of conditional probability distributions required to determine the child node’s conditional probability table (CPT) grows exponentially. Therefore, a combined method is proposed to reduce the complexity of determining CPTs by integrating various methods, including historical data collection, expert knowledge, logical relationships, and empirical formulas. Based on the proposed combined method, combining fire protection codes and provisions in China, a BN-based fire risk assessment model is presented for historical buildings, which integrates static indicators, such as building parameters and cultural significance, and dynamic indicators, such as environmental factors. In the model, the variable weight synthesis theory is introduced to automatically adjust the nodes’ relative importance (weights) to improve the rationality of the BN for the special scenarios and the unconventional emergency scenarios. The proposed BN model can reflect the fire development and spread trend, assess the fire risk of historical buildings, analyze key factors, and propose fire improvement measures. Taking Nanyue Temple as a case study, based on the collected field data, the assessment results have demonstrated and verified the proposed model and method are feasible. The sensitivity analysis results have verified the rationality of the method and identified the key factors.

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All the data used in this research will be made available upon request.

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References

  1. Kuruscu AO, Arun G (2013) Failures of the listed wooden buildings in istanbul. Adv Mater Res 778:26–33. https://doi.org/10.4028/www.scientific.net/AMR.778.26

    Article  Google Scholar 

  2. Zhang Z (2019) In the past decade, China has received 392 reports of fires in historical buildings. China News, Beijing

    Google Scholar 

  3. Australia ICOMOS Incorporated International Council on Monuments and Sites (2013) The Burra Charter: The Australia ICOMOS Charter for places of cultural significance. Burra, Australia

  4. Uzun Z, Köse C, Köse N (2018) A multidisciplinary study to reveal the historical value of wooden structures and to develop a conservation approach: Dere and Karh Mosques in Samsun, Turkey. J Cult Herit 32:60–72. https://doi.org/10.1016/j.culher.2018.01.010

    Article  Google Scholar 

  5. Haikou Longhua Fire Rescue Brigade (2016) Old town of Lijiang on fire. In: Sohu. https://www.sohu.com/a/121495839_480154. Accessed 17 Nov 2022

  6. Ries B, Rocha V, Picheta R, et al (2019) Fire at Notre Dame Cathedral. In: Cable News Netw. https://edition.cnn.com/world/live-news/notre-dame-fire. Accessed 17 Nov 2022

  7. Li J, Li H, Zhou B et al (2020) Investigation and statistical analysis of fire loads of 83 historic buildings in Beijing. Int J Archit Herita 14:471–482. https://doi.org/10.1080/15583058.2018.1550535

    Article  Google Scholar 

  8. Su H, Tung S, Tzeng C, Lai C (2020) Survey and experimental investigation of movable fire loads in Japanese-style wooden historical buildings. Int J Archit Herit 14:931–942. https://doi.org/10.1080/15583058.2019.1587040

    Article  Google Scholar 

  9. Huang D, Xu C, Li L et al (2009) Recent progresses in research of fire protection on historic buildings. J Appl Fire Sci 19:63–81. https://doi.org/10.2190/AF.19.1.d

    Article  Google Scholar 

  10. Laban M, Milanko V, Mišulić TK, Draganić S (2017) Fire statistics and risk analysis in wooden building structures in Serbia. Int Wood Prod J 8:62–70. https://doi.org/10.1080/20426445.2017.1309808

    Article  Google Scholar 

  11. Xiang Z, Zhebiao Y, Zhen X, Xinzheng L (2017) Fire spread simulations of building groups in rural areas. J Tsinghua Univ Sci Technol 57:1331–1337. https://doi.org/10.16511/j.cnki.qhdxxb.2017.25.060

    Article  Google Scholar 

  12. Kraaijeveld A, Gunnarshaug A, Schei B, Log T (2016) Burning rate and time to flashover in wooden 1/4 scale compartments as a function of fuel moisture content. In: Proceedings of the 10th International Fire Science & Engineering Conference. Windsor, pp 553–558

  13. Log T (2016) Cold climate fire risk: a case study of the L’rdalsoyri fire, January 2014. Fire Technol 52:1825–1843. https://doi.org/10.1007/s10694-015-0532-8

    Article  Google Scholar 

  14. Log T (2017) Indoor relative humidity as a fire risk indicator. Build Environ 111:238–248. https://doi.org/10.1016/j.buildenv.2016.11.002

    Article  Google Scholar 

  15. Log T (2018) Consumer grade weather stations for wooden structure fire risk assessment. Sensors 18:1–15. https://doi.org/10.3390/s18103244

    Article  Google Scholar 

  16. Log T (2019) Modeling indoor relative humidity and wood moisture content as a proxy for wooden home fire risk. Sensors 19:1–22. https://doi.org/10.3390/s19225050

    Article  Google Scholar 

  17. Himoto K, Nakamura T (2014) An analysis of the post-earthquake fire safety of historic buildings in Kyoto, Japan. Fire Technol 50:1107–1125. https://doi.org/10.1007/s10694-013-0330-0

    Article  Google Scholar 

  18. Guan Y, Fang Z, Wang T (2018) Fire risk assessment and daily maintenance management of cultural relic buildings based on ZigBee technology. Procedia Eng 211:192–198. https://doi.org/10.1016/j.proeng.2017.12.004

    Article  Google Scholar 

  19. Benešová R, Rusinová M, Sibilla L (2017) Analysis of fire protection with focuse on the specific conditions of the historic roofs. Appl Mech Mater Procedia Eng 861:120–128. https://doi.org/10.4028/www.scientific.net/AMM.861.120

    Article  Google Scholar 

  20. Kim K, Konishi T, Ziemba T et al (2015) Fire protection analysis and potential improvements for wooden cultural heritage sites in Japan. J Disaster Res 10:586–594. https://doi.org/10.20965/jdr.2015.p0586

    Article  Google Scholar 

  21. Hasemi Y, Yasui N, Akizuki M et al (2002) Fire safety performance of Japanese traditional wood/soil walls and implications for the restoration of historic buildings in urban districts. Fire Technol 38:391–402. https://doi.org/10.1023/A:1020178618608

    Article  Google Scholar 

  22. Oki T, Osaragi T (2016) Modeling human behavior of local residents in the aftermath of a large earthquake-wide-area evacuation, rescue and firefighting in densely built-up wooden residential areas. J Disaster Res 11:188–197

    Article  Google Scholar 

  23. Du F, Kobayashi H, Okazakic K, Ochiai C (2015) Research on the disaster coping capability of a historical village in a mountainous area of China: case study in Shangli, Sichuan. In: Lee S, Kim J (eds) 11th International Conference of the International Institute for Infrastructure Resilience and Reconstruction(I3R2): Complex Disasters and Disaster Risk Management. Procedia - Social and Behavioral Sciences, pp 118–130

  24. Du F, Okazakic K (2016) Building improvement responses to multi-hazard risk in the historic Dali Dong Village, Guizhou, China. Int J Disaster Risk Reduct 19:64–74. https://doi.org/10.1016/j.ijdrr.2016.08.014

    Article  Google Scholar 

  25. Du F, Okazakic K, Ochiai C (2017) Disaster coping capacity of a fire-prone historical dong village in China: a case study in Dali Village, Guizhou. Int J Disaster Risk Reduct 21:85–98. https://doi.org/10.1016/j.ijdrr.2016.10.016

    Article  Google Scholar 

  26. Larasatie P, Guerrero JE, Conroy K et al (2018) What does the public believe about tall wood buildings? an exploratory study in the US Pacific Northwest. J For 116:429–436. https://doi.org/10.1093/jofore/fvy025

    Article  Google Scholar 

  27. Cousins J, Thomas G, Heron D, Smith W (2012) Probabilistic modeling of post-earthquake fire in Wellington, New Zealand. Earthq Spectra 28:553–571. https://doi.org/10.1193/1.4000002

    Article  Google Scholar 

  28. Ibrahim MN, Abdul-Hamid K, Ibrahim MS et al (2011) The development of fire risk assessment method for heritage building. Procedia Eng 20:317–324. https://doi.org/10.1016/j.proeng.2011.11.172

    Article  Google Scholar 

  29. Ibrahim MN, Ibrahim MS, Mohd-Din A et al (2011) Fire risk assessment of heritage building – perspectives of regulatory authority, restorer and building stakeholder. Procedia Eng 20:325–328. https://doi.org/10.1016/j.proeng.2011.11.173

    Article  Google Scholar 

  30. Watts JM, Kaplan ME (1998) Development of a prototypical historic fire risk index to evaluate fire safety in historic buildings. APT Bull. https://doi.org/10.2307/1504640

    Article  Google Scholar 

  31. Watts JM, Kaplan ME (2001) Fire risk index for historic buildings. Fire Technol 37:167–180. https://doi.org/10.1023/A:1011649802894

    Article  Google Scholar 

  32. Salazar LGF, Romão X, Paupério E (2021) Review of vulnerability indicators for fire risk assessment in cultural heritage. Int J Disaster Risk Reduct 60:102286. https://doi.org/10.1016/j.ijdrr.2021.102286

    Article  Google Scholar 

  33. Fafet C, Zajmi EM (2021) Qualitative fire vulnerability assessments for museums and their collections: a case study from Kosovo. Fire 4:1–25. https://doi.org/10.3390/fire4010011

    Article  Google Scholar 

  34. Yuan C, He Y, Feng Y, Wang P (2018) Fire hazards in heritage villages: a case study on Dangjia Village in China. Int J Disaster Risk Reduct 28:748–757. https://doi.org/10.1016/j.ijdrr.2018.02.002

    Article  Google Scholar 

  35. Granda S, Ferreira TM (2019) Assessing vulnerability and fire risk in old urban areas: application to the historical centre of Guimarães. Fire Technol 55:105–127. https://doi.org/10.1007/S10694-018-0778-Z/FIGURES/9

    Article  Google Scholar 

  36. Copping AG (2002) The development of a fire safety evaluation procedure for the property protection of parish churches. Fire Technol 38:319–334. https://doi.org/10.1023/A:1020114314974

    Article  Google Scholar 

  37. Bengtsson H (2014) Development of a framework for application of Bayesian networks in fire safety engineering in Denmark. University of Stavanger, Stavanger

    Google Scholar 

  38. Hanea DM, Ale B (2009) Risk of human fatality in building fires: a decision tool using Bayesian networks. Fire Saf J 44:704–710. https://doi.org/10.1016/j.firesaf.2009.01.006

    Article  Google Scholar 

  39. Holický M (2010) Fire risk assessment in buildings. In: Soares CG (ed) Safety and reliability of industrial products, systems and structures, 1st edn. CRC Press, Boca Raton, pp 133–138

    Chapter  Google Scholar 

  40. Sanctis G De, Fischer K, Köhler J, Fontana M (2011) A probabilistic framework for generic fire risk assessment and risk-based decision making in buildings. In: Faber MH, Köhler J, Nishijima K (eds) 11th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP. Zurich, pp 2000–2007

  41. Pei J, Wang G (2020) Bayesian mutual information reliability model for fire risk assessment of high-rise buildings. Int J Electr Eng Educ. https://doi.org/10.1177/0020720919894197

    Article  Google Scholar 

  42. Das B (2008) Generating conditional probabilities for Bayesian networks: easing the knowledge acquisition problem. Artif Intell. https://doi.org/10.48550/arXiv.cs/0411034

    Article  Google Scholar 

  43. Friedman N, Goldszmidt M (2013) Learning Bayesian networks with local structure. Cosmol Nongalactic Astrophys. https://doi.org/10.48550/arXiv.1105.4165

    Article  MATH  Google Scholar 

  44. Xiang Y, Jia N (2007) Modeling causal reinforcement and undermining for efficient CPT elicitation. IEEE Trans Knowl Data Eng 19:1708–1718. https://doi.org/10.1109/TKDE.2007.190659

    Article  Google Scholar 

  45. Dfez FJ (1993) Parameter adjustment in Bayes networks. The generalized noisy OR-gate. In: Heckerman D, Mamdani A (eds) Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers, San Franciso, pp 99–105

    Google Scholar 

  46. Ji Z, Xia Q, Meng G (2015) A review of parameter learning methods in Bayesian network. In: Huang DS, Han K (eds) Lecture notes in computer science. Springer, New York, pp 3–12

    Google Scholar 

  47. Laitila P, Virtanen K (2016) Improving construction of conditional probability tables for ranked nodes in Bayesian networks. IEEE Trans Knowl Data Eng 28:1691–1705

    Article  Google Scholar 

  48. Li H, Li L, Wang J et al (2004) Fuzzy decision making based on variable weights. Math Comput Model 39:169–179. https://doi.org/10.1016/S0895-7177(04)90005-2

    Article  MathSciNet  MATH  Google Scholar 

  49. Zhang L, Guo H (2006) Introduction to Bayesian Network. China Science Publishing & Media Ltd.(CSPM), Beijing

    Google Scholar 

  50. Pearl J (1986) Fusion, propagation, and structuring in belief networks. Artif Intell 29:241–288. https://doi.org/10.1016/0004-3702(86)90072-X

    Article  MathSciNet  MATH  Google Scholar 

  51. Khakzad N, Khan F, Amyotte P, Cozzani V (2013) Domino effect analysis using Bayesian networks. Risk Anal 33:292–306. https://doi.org/10.1111/j.1539-6924.2012.01854.x

    Article  Google Scholar 

  52. Ramli N, Ghani NA, Ahmad N, Hashim IHM (2021) Psychological response in fire: a fuzzy bayesian network approach using expert judgment. Fire Technol 57:2305–2338. https://doi.org/10.1007/s10694-021-01106-0

    Article  Google Scholar 

  53. Francis RA, Guikema SD, Henneman L (2014) Bayesian belief networks for predicting drinking water distribution system pipe breaks. Reliab Eng Syst Saf 130:1–11. https://doi.org/10.1016/j.ress.2014.04.024

    Article  Google Scholar 

  54. Aven T, Heide B (2009) Reliability and validity of risk analysis. Reliab Eng Syst Saf 94:1862–1868. https://doi.org/10.1016/j.ress.2009.06.003

    Article  Google Scholar 

  55. Dempster AP (1990) Construction and local computation aspects of network belief functions.

  56. Chen J, Jia J, Ding L, Wu J (2020) Fire risk assessment in cotton storage based on fuzzy comprehensive evaluation and Bayesian network. Fire Mater 44:1–10. https://doi.org/10.1002/fam.2832

    Article  Google Scholar 

  57. Wu J, Hu Z, Chen J, Li Z (2018) Risk assessment of underground subway stations to fire disasters using Bayesian network. Sustainability 10:3810. https://doi.org/10.3390/su10103810

    Article  Google Scholar 

  58. Wikipedia (2021) Cronbach’s alpha. In: Wikipedia

  59. Zangenehmadar Z, Moselhi O (2016) Prioritizing deterioration factors of water pipelines using Delphi method. Measurement 90:419–499. https://doi.org/10.1016/j.measurement.2016.05.001

    Article  Google Scholar 

  60. Guo X, Ji J, Khan F et al (2021) Fuzzy Bayesian network based on an improved similarity aggregation method for risk assessment of storage tank accident. Process Saf Environ Prot 149:817–830. https://doi.org/10.1016/J.PSEP.2021.03.017

    Article  Google Scholar 

  61. Senol YE, Aydogdu YV, Sahin B, Kilic I (2015) Fault Tree Analysis of chemical cargo contamination by using fuzzy approach. Expert Syst Appl 42:5232–5244. https://doi.org/10.1016/J.ESWA.2015.02.027

    Article  Google Scholar 

  62. Wang T, Liao P, Ma X et al (2010) Using Bayesian network to develop an approach for construction safety risk assessment. China Civ Eng J 43:384–391

    Google Scholar 

  63. Norsys Netica help. In: Norsys. https://www.norsys.com/WebHelp/NETICA.htm

  64. Liu S, Yu W, Liu L, Hu Y (2019) Variable weights theory and its application to multi-attribute group decision making with intuitionistic fuzzy numbers on determining decision maker’s weights. PLoS One 14:e0212636. https://doi.org/10.1371/journal.pone.0212636

    Article  Google Scholar 

  65. Zhang Y, Li H (2006) Variable weighted synthesis inference method for fuzzy reasoning and fuzzy systems. Comput Math with Appl 52:305–322. https://doi.org/10.1016/j.camwa.2006.08.021

    Article  MathSciNet  MATH  Google Scholar 

  66. Pan K, Shi J (2009) Application of variable weight theory and relative difference function in safety assessment of urban subway operation. J China Railw Soc 31:20–25. https://doi.org/10.3969/j.issn.1001-8360.2009.03.004(inChinese)

    Article  Google Scholar 

  67. Shi J, Pan K (2008) Application of weight-variable theory and the relative difference function construction fire risk assessment. J Saf Environ 8:157–159. https://doi.org/10.3969/j.issn.1009-6094.2008.04.039(inChinese)

    Article  Google Scholar 

  68. Guo X (2019) Bulletin of the Cultural Relics Bureau on the recent fire accident of cultural heritage. In: State Counc. People’s Repub. China. http://www.gov.cn/fuwu/2019-04/17/content_5383666.html(in Chinese). Accessed 4 Jan 2022

  69. Li M, Hasemi Y, Nozoe Y, Nagasawa M (2021) Study on strategy for fire safety planning based on local resident cooperation in a preserved historical mountain village in Japan. Int J Disaster Risk Reduct 56:102081. https://doi.org/10.1016/J.IJDRR.2021.102081

    Article  Google Scholar 

  70. The ministry of public security of the People’s Republic of China (2018) Code for fire protection design of buildings. China

  71. The State Council Leading Group Office of the First National Census of Removable Cultural Relics (2017) The first national survey of movable cultural relics data bulletin. In: Natl. Cult. Herit. Adm. People’s Repub. China. http://www.ncha.gov.cn/art/2017/4/7/art_1983_139586.html(in Chinese). Accessed 4 Jan 2022

  72. PRC State Council (2014) Byelaw governing reporting, investigation and handling of production safety accidents

  73. ICOMOS China (2015) Principles for the conservation of heritage sites in China. Beijing

  74. General administration of quality supervision inspection and quarantine of the People’s Republic of China and Standardization administration of the People’s Republic of China (2006) Technical regulations for the nature reserve ecotourism plan. China

  75. Ministry of housing and urban-rural development of the People’s Republic of China (2018) Standard for scenic and historic area general planning.

  76. China.org.cn Nanyue Temple on Mount Heng. In: China.org.cn. http://www.china.org.cn/travel/2010-03/30/content_19713997.htm#p=1&r=0.7994249430911646

  77. Google (2022) Google map. In: Google. https://www.google.com.hk/maps/@27.2459352,112.7300985,2536m/data=!3m1!1e3?hl=zh-CN. Accessed 4 Jan 2022

  78. Norsys Norsys Software Corp. https://www.norsys.com. Accessed 31 Dec 2021

  79. Ding L, Ji J, Khan F et al (2020) Quantitative fire risk assessment of cotton storage and a criticality analysis of risk control strategies. Fire Mater 44:165–179. https://doi.org/10.1002/fam.2761

    Article  Google Scholar 

  80. van der Gaag LC, Renooij S, Coupé VM (2007) Sensitivity analysis of probabilistic networks. In: Lucas P, Gámez JA, Salmerón A (eds) Advances in probabilistic graphical models. Springer, Berlin, pp 103–124

    Chapter  MATH  Google Scholar 

  81. Matellini DB, Wall AD, Jenkinson ID et al (2013) Modelling dwelling fire development and occupancy escape using Bayesian network. Reliab Eng Syst Saf 114:75–91. https://doi.org/10.1016/j.ress.2013.01.001

    Article  Google Scholar 

  82. Tong Q, Yang M, Zinetullina A (2020) A dynamic bayesian network-based approach to resilience assessment of engineered systems. J Loss Prev Process Ind 65:104152. https://doi.org/10.1016/J.JLP.2020.104152

    Article  Google Scholar 

  83. Zinetullina A, Yang M, Khakzad N et al (2021) Quantitative resilience assessment of chemical process systems using functional resonance analysis method and Dynamic Bayesian network. Reliab Eng Syst Saf 205:107232. https://doi.org/10.1016/J.RESS.2020.107232

    Article  Google Scholar 

  84. Ministry of housing and urban-rural development of the People’s Republic of China, General administration of quality supervision inspection and quarantine of the People’s Republic of China and Standardization administration of the People’s Republic of China (2014) Technical code for lightning protection engineering of ancient buildings. China

  85. Ministry of housing and urban-rural development of the People’s Republic of China, General administration of quality supervision inspection and quarantine of the People’s Republic of China and Standardization administration of the People’s Republic of China (2013) Code for design of automatic fire alarm system. China

  86. Bai X, Li J (2014) A fire risk analysis of historical buildings. J China People’s Police Univ 29:69–71

    Google Scholar 

  87. National Cultural Heritage Adminitration (2015) Guidelines for Fire Protection Design of Historical Buildings (Trial). China

  88. Ministry of housing and urban-rural development of the People’s Republic of China, General administration of quality supervision inspection and quarantine of the People’s Republic of China and Standardization administration of the People’s Republic of China (2014) Technical code for fire protection water supply and hydrant systems. China

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Acknowledgements

This work was supported by the National Key Research & Development (R&D) Plan of China [grant number 2020YFC1522800].

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  1. State Key Laboratory of Fire Science, University of Science and Technology of China, 96 Jinzhai Road, Hefei, 230026, Anhui, China

    Jinyue Chen, Long Ding, Jie Ji & Jiping Zhu

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  1. Jinyue Chen
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Contributions

JC: Investigation, Methodology, Software, Writing—Original draft, Data curation. LD: Investigation, Conceptualization, Writing—Review & Editing. JJ: Conceptualization, Supervision, Project administration, Funding acquisition. JZ: Investigation, Data analysis, Writing, Review. All authors read and approved the final manuscript.

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Correspondence to Long Ding or Jie Ji.

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Appendix

Appendix

The state and description of nodes A ~ AA are shown in the main body of the text, and descriptions of other nodes are shown in this section (Table 11 in the appendix). The selection of each node and the determination of node state are mainly based on statistical data, literature research results, and the codes of China. For example, nodes A1 ~ A10 are set based on our statistics, the causes of 140 historical building fires from 2009 to 2018 (26.43% of electrical fire, 1% of spontaneous ignition, 5% of arson, 2.14% of fireworks, firecrackers, and bonfire, 2.86% of production and maintenance, 4.29% of heating and cooking, 10.71% of religious related fires, 2.41% of lightning, 5% of fire spread from surroundings, 39.29% of unknown causes) and the Fire Rescue Department Ministry of Emergency Management of China’s statistics of historical building fire causes (30.2% of electrical fire, 19.8% of careless use of fire, 5.3% of playing with fire, 5.3% of smoking, 5% of arson, 2.9% of production, 1.9% of spontaneous ignition, 0.8% of lightning, 28.9% of unknown and other causes [2]. The states and descriptions these nodes are shown in Table 11.

Table 11 The States and Descriptions of the Remaining Other Nodes
Full size table

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Chen, J., Ding, L., Ji, J. et al. A Combined Method to Build Bayesian Network for Fire Risk Assessment of Historical Buildings. Fire Technol 59, 3525–3563 (2023). https://doi.org/10.1007/s10694-023-01475-8

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