A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
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 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.
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 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.
Construction cost estimation of reinforced and prestressed concrete bridges using machine learning
Miljan Kovačević (author) / Nenad Ivanišević (author) / Predrag Petronijević (author) / Vladimir Despotović (author)
2021
Article (Journal)
Electronic Resource
Unknown
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