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

Abstract

U ovom radu istraženo je sedam najnovijih postupaka strojnog učenja za procjenu troškova izgradnje armiranobetonskih i prednapetih betonskih mostova, uključujući umjetne neuronske mreže (ANN) i ansamble ANN, ansamble regresijskih stabala (eng. random forests, boosted and bagged regresijska stabla), metodu potpornih vektora za regresiju (SVR) i Gausov regresijski proces (GPR). Stvorena je i baza podataka o troškovima izgradnje i projektnim karakteristikama za 181 armiranobetonski i prednapeti betonski most za treniranje i ocjenu modela.Seven state-of-the-art machine learning techniques for estimation of construction costs of reinforced-concrete and prestressed concrete bridges are investigated in this paper, including artificial neural networks (ANN) and ensembles of ANNs, regression tree ensembles (random forests, boosted and bagged regression trees), support vector regression (SVR) method, and Gaussian process regression (GPR). A database of construction costs and design characteristics for 181 reinforced-concrete and prestressed-concrete bridges is created for model training and evaluation