No abstract

Numerous studies have generated cost estimating relationships (CERs) for transportation projects via data analysis. Some studies collected data from databases, while others sourced data from conventional paper-based formats. When cost data were not in a consistent format, many studies failed to discuss the streamlining of pattern recognition. This work adopts a standard procedure for identifying CERs for transportation projects. A pavement maintenance and rehabilitation project type was selected as a case study for extracting data and concealed prediction rules. Linear and log-linear statistical approaches were employed to create optimal models. The resulting optimum estimation models via knowledge discovery in databases process can be then integrated into an expert system to facilitate information management and generate preliminary budgets for transportation agencies.

Many studies have generated cost estimating relationships (CERs) for transportation projects via data analysis. Some studies collected data from databases, while others sourced data from conventional paper-based formats. When cost data were not in a consistent format, many studies failed to discuss the streamlining of pattern recognition. This study adopts a standard procedure for identifying CERs for transportation projects. A pavement maintenance and rehabilitation project type was selected as a case study for extracting data and concealed prediction rules. Linear and log-linear statistical approaches were employed to create optimal models. The resulting optimum estimation models via knowledge discovery in databases process can be then integrated into an expert system to facilitate information management and generate preliminary budgets for transportation agencies.