Fire Technology

, Volume 45, Issue 4, pp 431–449

An Artificial Neural-network Based Predictive Model for Pre-evacuation Human Response in Domestic Building Fire

  • S. M. Lo
  • M. Liu
  • P. H. Zhang
  • Richard K. K. Yuen


The post-1993 WTC attack study (Proulx and Fahy, In: Proceedings of ASIAFLAM’95—An International Conference on Fire Science and Engineering, Hong Kong, 1995, pp 199–210) revealed that occupants took 1–3 h to leave the 110-storey buildings, and the pre-movement reactions could account for over two-thirds of the overall evacuation time. This indicates that a thorough understanding of the pre-evacuation behavioral response of people under fire situations is of prime importance to fire safety design in buildings, especially for complex and ultra high-rise buildings. In view of the stochastic (the positions of the occupants) and fuzzy (uncertainty) nature of human behavior (Fraser-Mitchell, Fire Mater 23:349–355, 1999), conventional linear and polynomial predictive methods may not satisfactorily predict the people’s response. An alternative approach, Adaptive Network based Fuzzy Inference System (ANFIS), is proposed to predict the pre-evacuation behavior of peoples, which is an artificial neural network (ANN) based predictive model and integrates fuzzy logic (if-then rules) and neural network (based on back propagation learning procedures The ANFIS learning architecture can be trained by structured human behavioral data, and different fuzzy human decision rules. The applicability in simulating human behavior in fire is worth exploring.


pre-evacuation human behavior predictive model fuzzy-neural network 


  1. 1.
    Proulx G, Fahy R (1995) A study of the New York Trade Centre evacuation. In: Proceedings of ASIAFLAM’95—an international conference on fire science and engineering, Hong Kong, pp 199–210Google Scholar
  2. 2.
    Lo SM, Fang ZA (2000) Spatial-grid evacuation model for buildings. J Fire Sci 18(5):376–394Google Scholar
  3. 3.
    Wood PG (1972) The behaviour of people in fires. Department of Environment and Fire Officers’ Committee, Joint Fire Research Organization, UKGoogle Scholar
  4. 4.
    Wood PG (1980) A survey of behavior in fires. In: Canter D (ed) Fires and human behaviour. John Wiley & Sons, Ltd, Chichester, pp 83–97Google Scholar
  5. 5.
    Bryan JL (1977) Smoke as a determinant human behaviour in fire situation. Department of Fire Protection Eng., University of Maryland, College Park, MDGoogle Scholar
  6. 6.
    Canter D, Breaux J, Sime J (1980) Domestic, multiple occupancy and hospital fires. Fires and human behaviour. John Wiley & Sons, Ltd., Chichester, pp 117–137Google Scholar
  7. 7.
    Sime J (1980) The concept of panic. Fires and human behaviour. John Wiley & Sons, Ltd., Chichester, pp 63–81Google Scholar
  8. 8.
    Canter D (1985) Studies of human behaviour in fire: empirical results and their implications for education and design. Building Research Establishment Report, UKGoogle Scholar
  9. 9.
    Sime J (1992) Human behaviour in fires—summary report. Report No. 45, Building Use & Safety Research Unit, School of Architecture, Portsmonth Polytechnic, UKGoogle Scholar
  10. 10.
    Proulx G (1994) Human response to fires. Fire research news. Natl Res Counc Can 71:1–3Google Scholar
  11. 11.
    Bryan JL (1995) Behavior response to fire and smoke. The SFPE handbook of fire protection engineering, 2nd edn. NFPA, Quincy, Mass., pp 3-241–3-262Google Scholar
  12. 12.
    Proulx G (1995) Evacuation time and movement in apartment building. Fire Saf J 24:229–246. doi:10.1016/0379-7112(95)00023-M CrossRefGoogle Scholar
  13. 13.
    Pauls J (1999) A personal perspective on research, consulting and codes/standards development in fire-related human behaviour, 1969+1999, with an emphasis on space and time factors. Fire Mater 23:265–272. doi:10.1002/(SICI)1099-1018(199911/12)23:6<265::AID-FAM698>3.0.CO;2-OGoogle Scholar
  14. 14.
    Lo SM, Lam KC, Yuen KK, Fang ZA (2001) Pre-evacuation behavioural study for the people in a high-rise residential building under fire situations. Int J Eng Performance-Based Fire Codes 2(4):143–152Google Scholar
  15. 15.
    Zhao CM, Lo SM, Liu M, Zhang SP (in press) A post-fire survey on the pre-evacuation human behavior. Fire Technol (available online)Google Scholar
  16. 16.
    McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133. doi:10.1007/BF02478259 MATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Rosenblatt F (1958) The perception: a probabilistic model for information storage and organization in the brain. Psychol Rev 65:386–408. doi:10.1037/h0042519 CrossRefMathSciNetGoogle Scholar
  18. 18.
    Schüürmann G, Müller E (1994) Back propagation neural networks—recognition vs. prediction capability. Environ Toxicol Chem 13(5):743–747. doi:10.1897/1552-8618(1994)13[743:BNNVPC]2.0.CO;2 CrossRefGoogle Scholar
  19. 19.
    Fraser-Mitchell JN (1999) Modelling human hehaviour within the fire risk assessment tool CRISP. Fire Mater 23:349–355. doi:10.1002/(SICI)1099-1018(199911/12)23:6<349::AID-FAM710>3.0.CO;2-3Google Scholar
  20. 20.
    Zadeh LA (1965) Fuzzy sets. Inf Contr 8:338–353. doi:10.1016/S0019-9958(65)90241-X MATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Zadeh LA (1973) Outline of new approaches to analysis of complex systems and decision process. IEEE Trans Syst Man Cybern 3(1):28–44MATHMathSciNetGoogle Scholar
  22. 22.
    Zadeh LA (1987) A fuzzy-algorithmic approach to the definition of complex or imprecise concepts. Fuzzy sets and application: selected papers by L. A. Zadeh. John Wiley & Sons, Inc., Philadelphia, PA, pp 147–192Google Scholar
  23. 23.
    Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15:116–132MATHGoogle Scholar
  24. 24.
    Jang RJS (1993) ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans Syst Man Cybern 23(3):665–685. doi:10.1109/21.256541 CrossRefMathSciNetGoogle Scholar
  25. 25.
    Rosnow RL (1993) Rosenthal R beginning behavioral research. Maxwell Macmillian Canada, Inc., TorontoGoogle Scholar
  26. 26.
    Tong D, Canter D (1985) The decision to evacuate: a study of the motivations which contribute to evacuation in the event of fire. Fire Safe J 9:257–265. doi:10.1016/0379-7112(85)90036-0 CrossRefGoogle Scholar
  27. 27.
    Sekizawa A, Ebihara M, Notake H, Kubota K, Nakano M, Ohmiya Y et al (1999) Occupants: behaviour in response to the high-rise apartments fire in Hiroshima City. Fire Mater 23:297–303. doi:10.1002/(SICI)1099-1018(199911/12)23:6<297::AID-FAM702>3.0.CO;2-2Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • S. M. Lo
    • 1
  • M. Liu
    • 1
  • P. H. Zhang
    • 2
  • Richard K. K. Yuen
    • 1
  1. 1.Department of Building & ConstructionCity University of Hong KongKowloon TongHong Kong
  2. 2.Faculty of City and Environmental EngineeringShenyang Jianzhu UniversityShenyangPeople’s Republic of China

Personalised recommendations