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Knowledge-Based Risk Assessment Under Uncertainty in Engineering Projects

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Intelligent Data Engineering and Automated Learning – IDEAL 2006 (IDEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4224))

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Abstract

In this paper we describe how an evidential-reasoner can be used as a component of risk assessment of engineering projects using a direct way of reasoning. Guan & Bell (1991) introduced this method by using the mass functions to express rule strengths. Mass functions are also used to express data strengths. The data and rule strengths are combined to get a mass distribution for each rule; i.e., the first half of our reasoning process. Then we combine the prior mass and the evidence from the different rules; i.e., the second half of the reasoning process. Finally, belief intervals are calculated to help in identifying the risks. We apply our evidential-reasoner on an engineering project and the results demonstrate the feasibility and applicability of this system in this environment.

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Authors and Affiliations

  1. The School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, BT7 1NN, Belfast, N.I., UK

    Rashid Hafeez Khokhar, David A. Bell, Jiwen Guan & QingXiang Wu

Authors
  1. Rashid Hafeez Khokhar
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  2. David A. Bell
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  3. Jiwen Guan
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  4. QingXiang Wu
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Editor information

Editors and Affiliations

  1. Escuela Politécnica Superior, GICAP Research Group, Universidad de Burgo, Calle Francisco de Vitoria S/N, Edifico C, Campus Vena, 09006, Burgos, Spain

    Emilio Corchado

  2. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  3. Department of Information Systems and Computation, Technical University of Valencia, Camino de Vera, Valencia, Spain

    Vicente Botti

  4. University of West Scotland, PA1 2BE, Paisley, Scotland

    Colin Fyfe

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© 2006 Springer-Verlag Berlin Heidelberg

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Khokhar, R.H., Bell, D.A., Guan, J., Wu, Q. (2006). Knowledge-Based Risk Assessment Under Uncertainty in Engineering Projects. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_154

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  • DOI: https://doi.org/10.1007/11875581_154

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45485-4

  • Online ISBN: 978-3-540-45487-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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