A DECISION SUPPORT SYSTEM FOR SUCCESS OF POST-HURRICANE RECONSTRUCTION OF TRANSPORTATION INFRASTRUCTURES

In the aftermath of hurricanes, when reliable transportation systems are vital, the chaotic and complex environment creates multiple uncertainties and risks in the reconstruction of transportation infrastructures. Damaged transport infrastructures decrease the timeliness of emergency responses and recovery procedures, and make it difficult for authorities, who are under excessive pressure and are struggling to find the financial resources to reconstruct them on time and within budget. The aim of this research was to develop a decision support system that would improve the cost and schedule performance, as well as reduce the number and extent of rework in post-hurricane reconstruction of transportation infrastructures. Significant factors that contribute to cost overruns, schedule delays, and the cost of reworks in post-hurricane reconstruction of transportation infrastructures (PRT) were statistically determined in this research. The results demonstrated that 26, 23, and 25 PRTs were statistically significant for cost overruns, schedule delays, and reworks of the mentioned projects, respectively. Three models were developed to predict the cost performance, schedule performance, and cost of reworks, and a stepwise multiple regression method was adopted. The results revealed that seven, nine, and ten PRTs were significant predictors of cost performance, schedule performance, and cost of reworks, respectively. The results demonstrated that frequency of on-site inspection, information management, and safety/environment issues were recorded as influential predictors in all three developed models to predict cost performance, schedule performance, and reworks in post-hurricane reconstruction of transport infrastructures. The extreme bounds analysis (EBA) method proposed by Leamer and Sala-i-Marin was adopted, and the criteria proposed by Sala-i-Martin was used. It was concluded from the results that four, six, and five significant predictors were robustly connected to cost performance, schedule performance, and the cost of reworks of the regression model, respectively. The results revealed that information management was a robust predictor shared between reconstruction cost performance and rework. Moreover, frequency of on-site inspection was the shared robust predictor between reconstruction cost and schedule performance in post-hurricane reconstruction of transportation infrastructures. It is believed that the findings of this research can provide a decision support system to stakeholders, decision makers, and project managers that will improve the success of post-hurricane reconstruction of transportation infrastructures. Additionally, this research provides accurate knowledge and information that will be helpful in effectively allocating limited resources after hurricanes and mitigating schedule delays, cost overruns, and reworks in reconstruction of transportation infrastructures.