Accommodating Uncertainty in Forecast Generation of Artificial Intelligence Tools in Construction Projects

Plagued by considerable uncertainty, construction management requires an outside view to increase the reliability of reference class forecasting (RCF). Accordingly, artificial intelligence (AI) tools are used in this approach to reduce such uncertainty in RCF. More explicitly, the outside view adopts the performance of past similar projects to predict that of the future project. As widely acknowledged in the literature, the similarity between the selected reference and the new project is of paramount importance. Despite this, the previous literature did not show how to measure the extent of the similarity to the new project. This study was conducted to fill this important gap by proposing a measure of similarity. The proposed measure can be used to quantify how previous projects were distributed. In the proposed method, eight real-life construction projects were analyzed using AI tools to select a highly comparable reference to the project.