In the wake of devastating natural disasters, the path to recovery is often as complex as it is critical. A groundbreaking study led by Dipendra Gautam from the Faculty of Civil and Environmental Engineering at the University of Iceland sheds new light on how communities can rebuild more effectively after earthquakes. Published in the journal ‘Resilient Cities and Structures’ (translated as ‘Resilient Cities and Structures’), the research introduces a novel approach to modeling housing recovery, offering valuable insights for policymakers, urban planners, and the construction industry.
The study focuses on the 2015 Gorkha earthquake sequence in Nepal, which left nearly 800,000 buildings in need of reconstruction. Gautam and his team developed a Stochastic Discrete Event Simulation (SDES) algorithm to model the housing recovery trajectory, taking into account the uncertainties and discrete events in each construction phase. This flexible modeling approach can simulate any housing recovery scenario that follows phased reconstruction, making it a powerful tool for future disaster planning.
“Our algorithm allows us to dissect the uncertainties in housing recovery and assess the reconstruction pace,” Gautam explains. “This is crucial for understanding the long-term impacts of extreme events and for developing strategies to build back better and more resiliently.”
The research compares recovery trajectories from severely hit, crisis hit, and earthquake hit parishes with the actual reconstruction progress. It also evaluates the quality and improvement of reconstructed buildings using seismic fragility functions, comparing them to pre-earthquake constructions. The findings reveal that while the vast majority of reconstructed buildings followed the Build Back Better (BBB) approach, there were missed opportunities to pursue the Build Back Resilient (BBR) approach due to multifaceted challenges ranging from unclear policies to economic constraints.
One of the most compelling aspects of the study is its critical assessment of the Government Assisted Owner Driven (GAOD) versus Owner Driven (OD) recovery framework. The research concludes that an insurance-supported and technically assisted OD approach could be the most suitable model for post-extreme event housing recovery. This finding has significant implications for the energy sector, as resilient housing solutions can reduce the long-term costs associated with disaster recovery and improve energy efficiency in rebuilt communities.
“By understanding the complexities of housing recovery, we can better prepare for future disasters and ensure that our communities are more resilient,” Gautam notes. “This research provides a valuable framework for policymakers and urban planners to make informed decisions that will have lasting impacts on the built environment.”
The study’s innovative use of SDES modeling offers a new lens through which to view post-disaster recovery, highlighting the importance of adaptive and flexible planning. As climate change continues to increase the frequency and severity of natural disasters, the insights from this research will be invaluable for shaping future developments in the field of disaster resilience and urban planning.