Descriptive data analysis.

This study aimed to develop predictive models that could be used to estimate the cost and schedule performance of reconstruction of transportation infrastructure damaged by hurricanes and to determine the predictors that are robustly connected to the developed models. Stepwise multiple linear regression and extreme bound analysis (EBA) were used to develop the models and determine the robust and fragile predictors, respectively. The results demonstrated that seven cost performance predictors and nine schedule performance predictors accounted for Adjusted R-Squared of 92.4% and 99.2%, respectively. The results of the EBA revealed that four cost and seven performance predictors were robustly connected to the developed cost and schedule performance predictive models. It was concluded that increases in laborers’ wages, the number of inspections, information and data management, and addressing safety and environmental issues prior to a project’s execution were predictors of both the cost and schedule performance of reconstruction projects. The outcomes of this study provide knowledge and information that will be helpful to decision-makers who are responsible for mitigating delays and cost overruns, and effectively allocating their limited resources available following a disaster.