Skip to main content
SpringerLink
Log in

Application of Artificial Neural Network to Fire Safety Engineering

  • Chapter
Handbook on Decision Making

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 4))

Abstract

Artificial neural networks (ANN) have been widely adopted as decision support systems in different engineering applications. Recently, ANN has been employed to determine the occurrence of catastrophic fire and to predict the fire and smoke developments. Intelligent approach becomes an alternative ways to evaluate the fire safety of a building instead of the traditional numerical approaches which require extensive computer storage and lengthy computation. Since fire data is usually noise corrupted in nature, a few ANN models are particularly developed for this application.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Specht, D.F.: A General Regression Neural Network. IEEE Transactions on Neural Network 2(6), 568–576 (1991)

    Article  Google Scholar 

  2. Rzempoluck, E.J.: Neural Network Classification of EEG During Camouflaged Object Identification. International Journal of Medical Informatics 44, 169–175 (1997)

    Article  Google Scholar 

  3. Schaffner, C., Schroder, D.: An Application of General Regression Neural Network to Nonlinear Adaptive Control. In: Fifth European Conference on Power Electronics and Applications, vol. 4, pp. 219–224 (1993)

    Google Scholar 

  4. Hyun, B.G., Nam, K.: Faults Diagnoses of Rotating Machines by Using Neural Nets: GRNN and BPN. In: Proceedings of the 1995 IEEE IECON 21st International Conference on Industrial Electronics, Control, and Instrumentation, vol. 2, pp. 1456–1461 (1995)

    Google Scholar 

  5. Leung, M.T., Chen, A.S., Daouk, H.: Forecasting Exchange Rates Using General Regression Neural Networks. Computers & Operations Research 27, 1093–1110 (2000)

    Article  MATH  Google Scholar 

  6. Parzen, E.: On Estimation of a Probability Density Function and Mode. Ann. Math. Statist. 33, 1065–1076 (1962)

    Article  MATH  MathSciNet  Google Scholar 

  7. Grossberg, S.: Adaptive Pattern Recognition and Universal Recoding II: Feedback, Expectation, Olfaction and Illusions. Biological Cybernetics 23, 187–202 (1976)

    Article  MATH  MathSciNet  Google Scholar 

  8. Kosko, B.: Fuzzy Entropy and Conditioning. Inform. Sci. 40, 165–174 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  9. Brezmes, J., Llobet, E., Vilanova, X., Orts, J., Saiz, G., Correig, X.: Correlation Between Electronic Nose Signals and Fruit Quality Indicators on Shelf-life Measurements with Pinklady Apples. Sensors and Actuators B 80, 41–50 (2001)

    Article  Google Scholar 

  10. Araujo, R., Almeida, T.D.: Learning Sensor-based Navigation of a Real Mobile Robot in Unknown Worlds. IEEE Transaction on Systems, Man and Cybernetics – Part B 29(2), 164–178 (1999)

    Article  Google Scholar 

  11. Araujo, R., Almeida, A.T.D.: Fuzzy ART Based Approach for Real-time Map Building. In: Proceedings of the 5th International Workshop on Advance Motion Control, AMC 1998-Coimbra, pp. 623–628 (1998)

    Google Scholar 

  12. Cinque, L., Foresti, G.L., Gumina, A., Levialdi, S.: A modified Fuzzy ART for image segmentation. In: Proceedings of the 11th International Conference on Image Analysis and Processing, pp. 102–107 (2001)

    Google Scholar 

  13. Park, S.: Neural networks and customer grouping in e-commerce: a framework using fuzzy ART. In: Proceedings of Academia/Industry Working Conference on Research Challenges, pp. 331–336 (2000)

    Google Scholar 

  14. Lee, E.W.M., Lim, C.P., Yuen, R.K.K., Lo, S.M.: A hybrid neural network model for noisy data regression. IEEE Transactions on Systems, Man and Cybernetics – Part B 34(20), 951–960 (2004)

    Article  Google Scholar 

  15. Yuen, R.K.K., Lee, E.W.M., Lim, C.P., Cheng, G.W.Y.: Fusion of GRNN and FA for online noisy data regression. Neural Processing Letters 19(3), 227–241 (2004)

    Article  Google Scholar 

  16. Kohonen, T.: The Self-Organizing Map. Proceedings of the IEEE 78(9), 1464–1480 (1990)

    Article  Google Scholar 

  17. Bezdek, J.B.: A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms. IEEE Trans. Pattern Anal. Machine Intell. PAMI 2, 1–8 (1980)

    Article  MATH  Google Scholar 

  18. Carpenter, G.A., Grossberg, S., David, B.R.: Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System. Neural Networks 4, 759–771 (1991)

    Article  Google Scholar 

  19. Lim, C.P., Harrison, R.F.: An Incremental Adaptive Network for On-line Supervised Learning and Probability Estimation. Neural Networks 10(5), 925–939 (1997)

    Article  Google Scholar 

  20. Tomandl, D., Schober, A.: A Modified General Regression Neural Network (MGRNN) with New, Efficient Training Algorithms as a Robust ‘Black Box’ Tool for Data Analysis. Neural Network 14, 1023–1034 (2001)

    Article  Google Scholar 

  21. Specht, D.F.: Enhancements to Probabilistic Neural Network. In: International Joint Conference on Neural Network, IJCNN, vol. 1, pp. 761–768 (1992)

    Google Scholar 

  22. Burrascano, P.: Learning Vector Quantization for the Probabilistic Neural Network. IEEE Transaction on Neural Networks (2), 458–462 (1991)

    Google Scholar 

  23. Tseng, M.L.: Integrating Neural Networks with Influence Diagrams for Multiple Sensor Diagnostic Systems. Ph.D. Dissertation, University of California, Berkeley (1991)

    Google Scholar 

  24. Traven, H.G.C.: A Neural Network Approach to Statistical Pattern Classification by ‘Semiparametric’ Estimation of Probability Density Functions. IEEE Transactions on Neural Network 2, 366–377 (1991)

    Article  Google Scholar 

  25. Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1998)

    Google Scholar 

  26. Efron, B.: Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics 7, 1–26 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  27. Efron, B.: Nonparametric Standard Errors and Confidence Intervals. The Canadian Journal of Statistics 9, 139–172 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  28. Williamson, J.R.: Gaussian ARTMAP: A Neural Network for Fast Incremental Learning of Noisy Multidimensional Maps. Neural Networks 9(5), 881–897 (1996)

    Article  Google Scholar 

  29. Carney, J., Cunningham, P.: Tuning diversity in bagged ensembles. International Journal of Neural Systems 10(4), 267–279 (2000)

    MATH  Google Scholar 

  30. Breiman, L.: Bagging Predictors. Technical Report no. 421, Department of Statistic, University of California at Berkeley, California (1994)

    Google Scholar 

  31. Friedman, J.: Multivariate Adaptive Regression Splines (with discussion). Annals of Statistics 19, 1–141 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  32. Weigend, A.S., Gersehnfield, N.A.: Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley, Reading (1994)

    Google Scholar 

  33. Singh, S.: Noise Impact on Time-series Forecasting Using an Intelligent Pattern Matching Technique. Pattern Recognition 32, 1389–1398 (1999)

    Article  Google Scholar 

  34. Weiss, S.M., Kulikowski, C.A.: Computer Systems That Learn: The Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. Morgan Kaufmann Publishers Inc., San Mateo (1991)

    Google Scholar 

  35. Beard, A.N.: Fire Models and Design. Fire Safety Journal 28, 117–138 (1997)

    Article  Google Scholar 

  36. Yeoh, G.H., Yuen, R.K.K., Lee, E.W.M., Cheung, S.C.P.: Fire and Smoke Distribution in Two-room Compartment Structure. International Journal of Numerical Methods for Heat and Fluid Flow 12(2-3), 178–194 (2002)

    MATH  Google Scholar 

  37. Yeoh, G.H., Yuen, R.K.K., Chen, D.H., Kwok, W.K.: Combustion and Heat Transfer in Compartment Fires. Numerical Heat Transfer Part A-Applications 42(1-2), 153–172 (2002)

    Article  Google Scholar 

  38. Yeoh, G.H., Yuen, R.K.K., Lo, S.M., Chen, D.H.: On Numerical Comparison of Enclosure Fire in a Multi-compartment Building. Fire Safety Journal 38, 85–94 (2003)

    Article  Google Scholar 

  39. Chen, Y., Sathyamoorthy, S., Serio, M.A.: New Fire Detection System Using FT-IR Spectroscopy and Artificial Neural Network. NISTIR6242. In: NIST annual conference fire research Gaithersburg, MD (November 1998)

    Google Scholar 

  40. Ishii, H., Ono, T., Yamauchi, Y., Ohtani, S.: Fire Detection System by Multi-layered Neural Network with Delay Circuit. In: Fire Safety Science – Proceedings of the fourth international symposium on Fire Safety Science, pp. 761–772 (1994)

    Google Scholar 

  41. Milke, J.A., Mcavoy, T.J.: Analysis of Signature Patterns for Discriminating Fire Detection with Multiple Sensors. Fire Technology 31(2), 120–136 (1995)

    Article  Google Scholar 

  42. Okayama, Y.: A Primitive Study of a Fire Detection Method Controlled By Artificial Neural Net. Fire Safety Journal 17(6), 535–553 (1991)

    Article  Google Scholar 

  43. Pfister, G.: Multisensor / Multicriteria Fire Detection: A New Trend Rapidly Becomes State of Art. Fire Technology 33(2), 115–139 (1997)

    Article  Google Scholar 

  44. Lee, W.M., Yuen, K.K., Lo, S.M., Lam, K.C.: Prediction of Sprinkler Actuation Time Using the Artificial Neural Networks. Journal of Building Surveying 2(1), 10–13 (2000)

    Google Scholar 

  45. Lee, E.W.M., Yuen, R.K.K., Lo, S.M., Lam, K.C.: Application of Fuzzy ARTMAP for Prediction of Flashover in Compartmental Fire. In: Proceedings of International conference on Construction, Hong Kong, June 19-21, pp. 301–311 (2001)

    Google Scholar 

  46. Lee, E.W.M., Yuen, R.K.K., Lo, S.M., Lam, K.C.: Probabilistic Inference with Maximum Entropy for Prediction of Flashover in Single Compartment Fire. Advanced Engineering Informatics 16(3), 179–191 (2002)

    Article  Google Scholar 

  47. Lee, E.W.M., Yuen, R.K.K., Lo, S.M., Lam, K.C., Yeoh, G.H.: A Novel Artificial Neural Network Fire Model for Prediction of Thermal Interface Location in Single Compartment Fire. Fire Safety Journal 39(1), 67–87 (2004)

    Article  Google Scholar 

  48. Yuen, R.K.K., Lee, E.W.M., Lo, S.M., Yeoh, G.H.: Prediction of Temperature and Velocity Profiles in a Single Compartment. Fire by an Improved Neural Network Analysis 41(6), 478–485 (2006)

    Google Scholar 

  49. Lee, E.W.M., Lee, Y.Y., Lim, C.P., Tang, C.Y.: Application of a Noisy Data Classification Technique to Determine the Occurrence of Flashover in Compartment Fires. Advanced Engineering Informatics 20(2), 213–222 (2006)

    Article  Google Scholar 

  50. Lee, E.W.M., Lau, P.C., Yuen, K.K.Y.: Application of Artificial Neural Network to Building Compartment Design for Fire Safety. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 265–274. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  51. Lee, E.W.M.: Application of an Intelligent Model Developed from Experimental Data to Building Design for Fire Safety. Stochastic Environmental Research and Risk Assessment 23(4), 493–506 (2009)

    Article  Google Scholar 

  52. Steckler, K.D., Quintiere, J.D., Rinkinen, W.J.: Flow induced by fire in a compartment. NBSIR 82-2520, National Bureau of Standards, Washington D.C. (1982)

    Google Scholar 

  53. McGranttan, K.B., Baum, H.R., Rehm, R.G., Hamins, A., Forney, G.P.: Fire Dynamics Simulator – Technical Reference Guide NISTIR 6467. National Institute of Standards and Technology, Gaithersburg, MD (2000)

    Google Scholar 

  54. Wang, H.Y., Coutin, M., Most, J.M.: Large-eddy-simulation of Buoyancy-driven Fire Propagation Behind a Pyrolysis Zone Along the Vertical Wall. Fire Safety Journal 37, 259–285 (2002)

    Article  Google Scholar 

  55. McCaffrey, B.J., Quintiere, J.G., Harkleroad, M.F.: Estimating Room Temperatures and the Likelihood of Flashover Using Fire Test Data Correlations. Fire Technology 17, 98–119 (1982)

    Article  Google Scholar 

  56. Drysdale, D.: An Introduction to Fire Dynamics. John Wiley and Sons, Chichester (1996)

    Google Scholar 

  57. Karlsson, B., Quintiere, J.: Enclosure Fire Dynamics. CRC Press LLC, Boca Raton (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Building and Construction, City University of Hong Kong, Hong Kong Special Administrative Region, Tat Chee Avenue, Kowloon Tong, People of Republic of China

    Eric Wai Ming Lee

Authors
  1. Eric Wai Ming Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of South Australia, Adelaide, Australia

    Lakhmi C. Jain

  2. University of Science Malaysia, Malaysia

    Chee Peng Lim

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Lee, E.W.M. (2010). Application of Artificial Neural Network to Fire Safety Engineering. In: Jain, L.C., Lim, C.P. (eds) Handbook on Decision Making. Intelligent Systems Reference Library, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13639-9_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13639-9_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13638-2

  • Online ISBN: 978-3-642-13639-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation