Aggregation-Based Framework for Construction Risk Assessment with Heterogeneous Groups of Experts

  • Author(s) / Creator(s)
  • Construction companies continuously seek to improve risk analysis techniques to determine the contingency of projects. Construction risk assessment relies on a group decision-making (GDM) process, in which a heterogeneous group of experts provides their opinions to determine the probabilities and impacts of project risks. In this paper, risk probabilities and impacts are expressed as linguistic terms, which are then represented by fuzzy sets to account for the uncertainty in these assessments. Current GDM processes help experts to obtain collective agreement through the use of a consensus-reaching process, which has several limitations, such as being a time-consuming procedure. The main contributions of this paper are to introduce a list of criteria and a set of metrics to evaluate risk-assessment expertise. Additionally, this paper discusses the development of a method for weighting the importance of experts’ opinions according to their expertise levels. This research will also serve to improve GDM processes in construction risk assessment by introducing a structured framework that combines assessments from a heterogeneous group of experts through aggregation.

  • Date created
    2019-01-01
  • Subjects / Keywords
  • Type of Item
    Article (Draft / Submitted)
  • DOI
    https://doi.org/10.7939/r3-q4eh-bj63
  • License
    © 2019 American Society of Civil Engineers This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://doi.org/10.1061/(ASCE)CO.1943-7862.0001614
  • Language
  • Citation for previous publication
    • Monzer, N., Lourenzutti, R., Fayek, A. Robinson, & Siraj, N. B. (2019). An aggregation-based framework for construction risk assessment with heterogeneous groups of experts. Journal of Construction Engineering and Management, 145(3): 04019003. 10 pp. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001614.