Modelling enablers of the post-disaster recovery of road infrastructure using ISM and fuzzy MICMAC analysis

Abstract

The road infrastructure is damaged after a disaster, resulting in the area being disconnected from other locations. Thus, road clearing, and recovery operations are on priority during the emergency recovery operations to restore access to the disaster-affected area. Therefore, an investigation was conducted on the disaster in Uttarakhand (a Northern state in India) that occurred during February 2021, to advance our understanding of the enablers of the emergency recovery operations for road infrastructure. For this purpose, the study undertakes a comprehensive review of contemporary literature and discussion with the practitioners and academicians involved in this field of study. The study uses interpretive structural modeling (ISM) and Fuzzy MICMAC analysis to identify and develop a hierarchical model of enablers to improve the post-disaster recovery of road infrastructure. The study identifies eleven enablers to the post-disaster recovery of road infrastructure, and interrelationships among these enablers are derived using the ISM model. The enablers include a focus on preparedness, prevention and mitigation phase, engagement of local communities, engagement of local contractors, support from local government, developing policies with more focus on disaster preparedness, allocation of funds, investment in technology, more utilization of technology, developing trust among stakeholders, promoting information sharing among stakeholders and foster coordination and collaboration among stakeholders. Further, the result indicates that driver variables, including the focus on preparedness, prevention and mitigation phase, engagement of local communities, engagement of local contractors, support from local government, and developing policies with more focus on disaster preparedness, are critical to improving the post-disaster recovery of road infrastructure.