Construction cost estimation of reinforced and prestressed concrete bridges using machine learning

Seven state-of-the-art machine learning techniques for estimation of construction costs reinforced-concrete and prestressed concrete bridges are investigated in this paper, including artificial neural networks (ANN) ensembles ANNs, regression tree (random forests, boosted bagged trees), support vector (SVR) method, Gaussian process (GPR). A database design characteristics 181 prestressed-concrete is created model training evaluation.