Prioritizing Construction Risks Using Fuzzy AHP in Brazilian Public Enterprises

Leandro Modesto Prates Beltrão · Michele Tereza Marques Carvalho

59 citations0/0 files storedJournal of Construction Engineering and Management

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Construction Project Management and Performance Multi-Criteria Decision Making BIM and Construction Integration

Abstract

Risks capable of impacting project objectives are intrinsic to the construction industry. Effective risk analysis has key importance to professionals concerned with construction goals, especially in public enterprises, given the amount of money invested and the demand for modern infrastructure. Therefore, this study aims to identify the main public construction risks and present a practical fuzzy analytic hierarchy process (AHP) model to prioritize these risks in Brazilian public enterprises. The model is divided into three steps: (1) risk identification and development of the risk breakdown structure, (2) pairwise comparisons, and (3) risk prioritization. Throughout the case study, 54 risks were identified, which were organized into eight categories. The major risk in the Brazilian scenario was “difficulty in environmental licensing.” Although this study focused on a specific context, its methodology provides a reliable and practical framework that can be adjusted to various construction projects worldwide to assess risks. Additionally, the identified risks can provide sound guidance to practitioners globally, because their elicitation was based on studies concerning different countries.

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References

1965Fuzzy sets 1990How to make a decision: The analytic hierarchy process 1996A Guide to the project management body of knowledge 1985Fuzzy hierarchical analysis 2002Underestimating Costs in Public Works Projects: Error or Lie?1994Highlights and critical points in the theory and application of the Analytic Hierarchy Process 2004Fuzzy MCDM approach for planning and design tenders selection in public office buildings 2014Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies

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