The cost of bridge construction is influenced by numerous internal and external factors, making it very difficult to approximate. Years prior to a bridge project letting, state highway agencies (SHAs) must set a reliable budget for the proposed project, but the information available to them is very limited. Developing reliable cost estimates for bridge projects during the early pre-construction phases is very important and challenging for SHAs. This study employed a non-parametric regression analysis technique—multivariate adaptive regression splines (MARS)—to model the conceptual cost of bridge projects. This novel approach does not require detailed construction documents and does not require strict assumptions to be valid for the developed model or to be reliable for the model predictions. MARS was applied to the empirical data gathered from a Florida Department of Transportation database. The 10-fold cross-validation method was employed in this study to assess model performance. The criteria to gauge overall model fit, generalizability, and prediction error were evaluated. The results revealed that the developed model consistently performed well, based on Cross-Validated R-square (CVRSq), Generalized Cross-Validation (GSV), Generalized R-square (GRSq), and max error.
