Early Bill

Accurate cost estimation in the preliminary stages of project development is critical for making informed planning decisions. However, such early estimates are typically restricted by limited information. In this article, the widely recognized intelligence of feed-forward artificial neural networks (FFANNs) is used to process actual data from 68 concrete road bridges and provide a surrogate model for the accurate estimation of the bill-of-quantities (BoQ). Specifically, two FFANNs are trained to estimate the superstructure and piers concrete and steel-based on the construction method and the bridge dimensions. As the relevant metrics demonstrate, the FFANNs capture very well the complex interrelations in the data set and produce highly accurate estimates. Furthermore, their generalization capability is superior to the capability of respective linear regression models. As the data used to train the FFANNs are normally available early in the project lifecycle, the proposed model enables early, yet accurate cost estimates to be obtained