Neural Computation
Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Web-based CBR system applied to early cost budgeting for pavement maintenance project
Expert Systems with Applications: An International Journal
Neural networks for cost estimation of shell and tube heat exchangers
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
GAMLSS and neural networks in combat simulation metamodelling: A case study
Expert Systems with Applications: An International Journal
Intelligent Systems Research in the Construction Industry
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Modeling of construction costs is a challenging task, as it requires representation of complex relations between factors and project costs with sparse and noisy data. In this paper, neural networks with bootstrap prediction intervals are presented for range estimation of construction costs. In the integrated approach, neural networks are used for modeling the mapping function between the factors and costs, and bootstrap method is used to quantify the level of variability included in the estimated costs. The integrated method is applied to range estimation of building projects. Two techniques; elimination of the input variables, and Bayesian regularization were implemented to improve generalization capabilities of the neural network models. The proposed modeling approach enables identification of parsimonious mapping function between the factors and cost and, provides a tool to quantify the prediction variability of the neural network models. Hence, the integrated approach presents a robust and pragmatic alternative for conceptual estimation of costs.