Cost Contingency is a financially important amount added to the base estimate for covering unforeseen uncertainties and risks in construction projects, including construction difficulties, design changes during construction, and inaccuracies in the estimating process. An accurate cost contingency is critical to construction project participants having a significant impact on project financial successes and other organizational activities. This study proposes a new approach to predicting the owner’s cost contingency on transportation construction projects using particle swarm optimization (PSO), a population-based stochastic optimization technique inspired by the social behavior of flocking birds or schooling fish. Through a comparison of performance with an artificial neural network (ANN) based approach using historical data from Florida Department of Transportation (FDOT) construction projects, the findings indicated PSO more accurately predicts the owner’s cost contingency.
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