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Building information modeling (BIM) can be recognized as the most promising technology that was introduced to the construction sector in the recent past. The current BIM-based safety risk assessment methods ignore data uncertainty. Hence, this study integrates BIM with fuzzy set theory for safety risk prediction. A Fuzzy Inference System (FIS) was developed based on the causality of safety incidents. The FIS extracts construction project data from BIM models while automatically assessing each potential safety hazard and project's total risk. The proposed method was demonstrated by using a building project in which the safety risk was predicted as low. Safety records of the above project revealed that no safety incidents were recorded in this project. The proposed method can indicate the safety risks level at various stages of a construction project and help to enhance the safety of construction workers on a construction site. [ABSTRACT FROM AUTHOR]

Construction project proposal evaluations received much attention during the recent years due to increased awareness of sustainability, value for money, and transparency. The bid evaluation matrix is the commonly used method in the construction industry for project proposal evaluation. The subjective judgments used for evaluation criteria are associated with significant uncertainty. Moreover, the transformation of qualitative evaluations raises significant data uncertainty. Attempts have been made by previous researchers to incorporate uncertainties associated with project bid evaluations, but the ability of those methods to account for "true uncertainty" is questionable. The objective of this paper is to develop a fuzzy logic-based evaluation framework for project proposal evaluation. This use of type-2 fuzzy numbers sets the proposed approach apart from the current body of knowledge. An algorithm using type-2 fuzzy numbers was developed to define input uncertainty and parameter weights. The developed method was demonstrated using a building construction project case study. The proposed approach enables quantifying qualitative evaluations more comprehensively from a scientific basis, forming a generic proposal evaluation method applicable to various industry sectors. [ABSTRACT FROM AUTHOR]