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A DST-based approach for construction project risk analysis

  • Special Issue Paper
  • Published:
Journal of the Operational Research Society

Abstract

Despite its huge potential in risk analysis, the Dempster–Shafer Theory of Evidence (DST) has not received enough attention in construction management. This paper presents a DST-based approach for structuring personal experience and professional judgment when assessing construction project risk. DST was innovatively used to tackle the problem of lacking sufficient information through enabling analysts to provide incomplete assessments. Risk cost is used as a common scale for measuring risk impact on the various project objectives, and the Evidential Reasoning algorithm is suggested as a novel alternative for aggregating individual assessments. A spreadsheet-based decision support system (DSS) was devised to facilitate the proposed approach. Four case studies were conducted to examine the approach's viability. Senior managers in four British construction companies tried the DSS and gave very promising feedback. The paper concludes that the proposed methodology may contribute to bridging the gap between theory and practice of construction risk assessment.

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Authors and Affiliations

  1. The University of Bedfordshire Business School, Bedford, UK

    A Taroun

  2. The University of Manchester, Manchester, UK

    J-B Yang

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  1. A Taroun
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Appendix

Appendix

In a hierarchy of two levels, the aggregation of the distributed assessments of l criteria is conducted according to the following steps:

  1. 1)

    Assign importance weights ωi (i=1, …, l) to decision criteria. The weights must be normalised, so that 0⩽ωi⩽1 and ∑i=0lωi=1.

  2. 2)

    Transform the degrees of belief into basic probability assignments by multiplying them with the importance weights:

    mn, i represents the probability mass assigned to the evaluation grade H n when considering the criterion C i , N is the number of assessment grades.

  3. 3)

    Calculate the probability mass assigned to the whole frame of discernment, a set of N evaluation grades, on every criterion by the following formula:

    This probability mass can be split into two parts: • caused by the weight of the criterion Ci and • i=1, …, l caused by the incompleteness of the assessment

  4. 4)

    Aggregate the probability assignments by means of the following equations:

  5. 5)

    Aggregate the probability masses assigned to the whole frame of discernment:

  6. 6)

    Transform the aggregated probability masses into an aggregated belief structure in the shape of S(Cj(Ai))=(Hn, βn, i(Ai)). Such a transformation requires calculating the belief degrees β n (n=1, …, N) using the following equation:

  7. 7)

    Calculate the aggregated degree of ignorance β H :

The aggregation result is an overall belief structure. In this belief structure, the overall degrees of belief β n together with the degree of ignorance β H always sum to unity which is perfectly logical.

In order to compare different alternatives, the overall assessment should be transformed from its distributed form into a representative score. Yang (2001) proposed calculating an expected utility for every alternative u(S(Aj))as follows:

Hence, by agreeing upon the utility value of every evaluation grade, the expected utility values of the competing alternatives can be estimated. In the case of incomplete assessment, the degree of ignorance can be utilised in order to generate a utility interval with upper and lower levels (Yang, 2001) as follows:

where H1 is the assessment grade with the lowest utility value; H N the assessment grade with the maximum utility value.

These two utility levels can be easily averaged in order to rank the alternatives accordingly. However, using the average utility for comparison is not always reliable. According to Yang (2001), alternative A1 is preferred to alternative A2 if: umin(A1)>umax(A2) and they are indifferent if umin(A1)=umin(A2).

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Taroun, A., Yang, JB. A DST-based approach for construction project risk analysis. J Oper Res Soc 64, 1221–1230 (2013). https://doi.org/10.1057/jors.2013.38

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  • DOI: https://doi.org/10.1057/jors.2013.38

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