Journal of Failure Analysis and Prevention

, Volume 19, Issue 2, pp 496–503 | Cite as

PCA-I and AHP Methods: Unavoidable Arguments in Accident Scenario Classification

  • Hefaidh Hadef
  • Mébarek DjebabraEmail author
Technical Article---Peer-Reviewed


Risk mapping is the foundation of the risk prevention strategy. It allows for the understanding of all factors that may affect the activities. It is a collective decision based on negotiation between the actors. Argument-based negotiation accelerates the negotiation process and converges it toward a final and common decision. It is in this context that this paper aims to illustrate the contribution of the improved PCA and AHP tools, considered as arguments, to the classification of major accident risks.


Risks Cartography Accident Scenario Classification 



  1. 1.
    American Institute of Chemical Engineers (AICE), Guidelines for Hazard Evaluation Procedures, 2nd. edn. (American Institute of Chemical Engineers (AICE), New York, 1992)Google Scholar
  2. 2.
    E. Villeneuves, Hybridization of the Cognitive and Static Experience Feedbacks for Risks Assessment. PhD thesis presented at the University of Toulouse, France (2012)Google Scholar
  3. 3.
    American Institute of Chemical Engineers (AICE), Tools for Making Acute Risk Decisions (American Institute of Chemical Engineers (AICE), New York, 1995)Google Scholar
  4. 4.
    M. Merad, D. Guionnet, L. Rouïl, Controversy of public policy analytics in terms of risks prevention: the case of air pollution, in Citizen Science International Workshop. 14–15. Université Paris Dauphine (2015)Google Scholar
  5. 5.
    S.B. da Cunha, A review of quantitative risk assessment of onshore pipelines. J. Loss Prev. Process Ind. 44(11), 282–298 (2016)CrossRefGoogle Scholar
  6. 6.
    M. Gul, A.F. Guneri, A fuzzy multi criteria risk assessment based on decision matrix technique: a case study for aluminum industry. J. Loss Prev. Process Ind. 40(3), 89–100 (2016)CrossRefGoogle Scholar
  7. 7.
    H. Hadef, M. Djebabra, L. Sedrat, M. Taghelabet, Contribution to the evaluation of safety barriers performance. World J. Sci. Technol. Sustain. Dev. 16(1), 56–68 (2019)CrossRefGoogle Scholar
  8. 8.
    D.-G. Bona, A. Forcina, Analytic critical flow method (ACFM): a reliability allocation method based on analytic hierarchy process. J Fail. Anal. Preven. 17(6), 1149–1163 (2017)CrossRefGoogle Scholar
  9. 9.
    D. Krus, K. Grantham, A step toward risk mitigation during conceptual product design: component selection for risk reduction. J Fail. Anal. Preven. 11(4), 432–445 (2011)CrossRefGoogle Scholar
  10. 10.
    G. Decrop, Risque et accident nucléaire: l’introuvable négociation sociale? Nat. Sci. Soc. 5(4), 60–64 (1997)CrossRefGoogle Scholar
  11. 11.
    L. Fang, B. Xiao, H. Yu, Q. You, A stable systemic risk ranking in China’s banking sector: based on principal component analysis. Phys. A 492, 1997–2009 (2018)CrossRefGoogle Scholar
  12. 12.
    L.-L. Palese, A random version of principal component analysis in data clustering. Comput. Biol. Chem. 73(4), 57–64 (2018)CrossRefGoogle Scholar
  13. 13.
    T.-G. Penkova, Principal component analysis and cluster analysis for evaluating the natural and anthropogenic territory safety. Proc. Comput. Sci. 112, 99–108 (2017)CrossRefGoogle Scholar
  14. 14.
    A. Gupta, A. Barbu, Parameterized principal component analysis. Pattern Recogn. 78, 215–227 (2018)CrossRefGoogle Scholar
  15. 15.
    M.-B. Christopher, Pattern Recognition and Machine Learning (Information Science and Statistics) (Springer, Berlin, 2006)Google Scholar
  16. 16.
    S.-C. Ng, Principal component analysis to reduce dimension on digital image. Procardia Comput. Sci. 111, 113–119 (2017)CrossRefGoogle Scholar
  17. 17.
    T.-L. Saaty, Multicriteria Decision Making: The Analytic Hierarchy Process (RWS, New York, 1995)Google Scholar
  18. 18.
    M. Yucesan, K. Gökhan, Risk evaluation and prevention in hydropower plant operations: a model based on Pythagorean fuzzy AHP. Energy Policy 126, 343–351 (2019)CrossRefGoogle Scholar
  19. 19.
    M. Yazdi, Hybrid probabilistic risk assessment using fuzzy FTA and fuzzy AHP in a process industry. J. Fail. Anal. Preven. 17(4), 756–764 (2017)CrossRefGoogle Scholar
  20. 20.
    U. Kokangül, P. Viye, D. Cansu, A new approximation for risk assessment using the AHP and Fine Kinney methodologies. Saf. Sci. 91, 24–32 (2017)CrossRefGoogle Scholar
  21. 21.
    D. Qingxing, C. Orrin, An orders-of-magnitude AHP supply chain risk assessment framework. Int. J. Prod. Econ. 182, 144–156 (2016)CrossRefGoogle Scholar
  22. 22.
    O. Taylan, O. Abdallah, B. Reda, M.S. Abdulaal, M.-R. Kabli, Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Appl. Soft Comput. 17, 105–116 (2014)CrossRefGoogle Scholar
  23. 23.
    M. Dagdeviren, I. Yüksel, Developing a fuzzy analytic hierarchy process (AHP) model for behavior-based safety management. Inf. Sci. 178, 1717–1723 (2008)CrossRefGoogle Scholar
  24. 24.
    F. Tuysuz, C. Kahraman, Project risk evaluation using a fuzzy analytic hierarchy process: an application to information technology projects. Int. J. Intell. Syst. 21, 229–284 (2006)CrossRefGoogle Scholar
  25. 25.
    E. Triantaphyllou, S.-H. Mann, Using the analytic hierarchy process for decision making in engineering applications: Some challenges. Int. J. Ind. Eng. Appl. Pract. 2)1, 35–44 (1995)Google Scholar
  26. 26.
    P.-T. Harker, The Art and Science of Decision Making: The Analytic Hierarchy Process (Springer, Berlin, 1996)Google Scholar
  27. 27.
    F. Schätter, O. Hansen, M. Wiens, F. Schultmann, A decision support methodology for a disaster-caused business continuity management. Decis. Support Syst. 118, 10–20 (2019)CrossRefGoogle Scholar
  28. 28.
    X. Yang, S. Haugen, Classification of risk to support decision-making in hazardous processes. Saf. Sci. 80, 115–126 (2015)CrossRefGoogle Scholar
  29. 29.
    H. Martensen, K. Diependaele, S. Daniels, W-V. Berghe, E. Papadimitriou, G. Yannis, I. Van Schagen, W. Weijermars, W. Wijnen, A. Filtness, R. Talbot, P. Thomas, K. Machata, E. Breuss, S. Kaiser, T. Hermitte, R. Thomson, R. Elvik, The European road safety decision support system on risks and measures, Accid. Anal. Prev. (2018)Google Scholar
  30. 30.
    Morteza Yazdani, Pascale Zarate, Adama Coulibaly, Edmundas Kazimieras Zavadskas, A group decision making support system in logistics and supply chain management. Expert Syst. Appl. 88, 376–392 (2017)CrossRefGoogle Scholar

Copyright information

© ASM International 2019

Authors and Affiliations

  1. 1.LRPI LaboratoryApplied Engineering Department, Institute of Technology, University of OuarglaOuarglaAlgeria
  2. 2.LRPI LaboratoryUniversity of Mostefa Benboulaid-Batna 2, Health and Safety InstituteFesdis-Batna, BatnaAlgeria

Personalised recommendations