The combination of natural hazards, climate change, and the COVID-19 pandemic has demonstrated the importance of community resilience. Community resilience is a manifestation of the human trait of adaptation. A resilient community is able to withstand and recover from hazardous events with minimal disruption to its way of life. As humans, we seek to use our ability to engineer to adapt to the threat of natural hazards. Although achieving resilience is technically challenging and expensive, communities must strive to accomplish the highest level of resilience attainable with the engineering and financial resources available.
The science behind resilience engineering involves many disciplines, each dedicated to a subset of the overall problem. Complex issues lie at the intersection of these subsets, but interdisciplinary research is difficult to achieve because researchers in various disciplines frame problems and perform research from different perspectives and along distinct pathways. However, as computational models are well established in each discipline, computation is a natural language that links the disciplines together.
Last fall, the Michigan Institute for Computational Discovery and Engineering and the department of Civil and Environmental Engineering brought together established leaders and some of the most innovative rising scholars in the computational hazards research, to present and discuss different computational approaches used in modeling, assessing, and defining standards for community resilience. The speakers included representatives from leading research centers in the field: keynote speaker, Terri McAllister, from the National Institute of Standards and Technology (NIST); John van de Lindt (Colorado State University) co-director of the NIST-funded Center of Excellence (CoE) for Risk-Based Community Resilience Planning; Gregory Deierlein (Stanford University) from the SimCenter, which represents a consortium of universities on the U.S. West Coast; Sherif El-Tawil (University of Michigan) from ICoR, and Wael El-Dakhakhni (McMaster University) from INTERFACE. They were joined
by other leaders in the fields including Tasos Sextos from Bristol University, UK, Xinzheng Lu, head of the Institute of Disaster Prevention and Mitigation of Tsinghua University; Hiba Baroud from Vanderbilt University, and Seth Guikema from the University of Michigan. The speakers highlighted their Centers’ or research groups’ capabilities and contributions, then reconvened for a panel discussion to address questions from the audience of nearly 250 participants from 30 countries, across six continents. The event also included a hands-on workshop that highlighted the Simple Run-Time Infrastructure software toolkit (SRTI). The SRTI is a free, open-source solution developed at the University of Michigan. It enables researchers to connect computer programs and simulators written in different languages, share data during execution, and design hybrid systems using disparate simulator modules, with a primary goal of being user friendly. The applications within this workshop demonstrated how one tool can be used to bring together multiple computational dialects to create a single language in the context of natural hazards research. The SRTI software toolkit is a result of the work of Dr. Sherif El-Tawil’s research group at the University of Michigan, supported by the National Science Foundation’s Office of Advanced Cyberinfrastructure (OAC) under grant CRISP TYPE II – 1638186. (icor.engin.umich.edu).
The range of techniques and principles that were detailed at this workshop can be applied to the current COVID-19 crisis. The pandemic is a perfect example that demonstrates that investing in mitigating risk reduces the cost, both human and material, of a hazard, and that even hazards with such a low probability of occurrence require enough investment to make ourselves resilient to it. The pandemic also illustrates that computational hazards research is a rich field with many opportunities at the intersection of the various disciplines. One of the most interesting ideas there is to explore is how to fuse sensor data – from the field – with simulations data, to achieve models that can help predict in real time the effect of a natural hazard.