Dr. Mostafa Reisi Gahrooei, Assistant Professor in the Department of Industrial and Systems Engineering, has received a National Science Foundation (NSF) grant to fund the project, “Collaborative Research: Multi-Agent Adaptive Data Collection for Automated Post-Disaster Rapid Damage Assessment.”
This project aims to develop a novel framework for data collection that is able to constantly analyze observations to dynamically and adaptively determine the trajectories of data collector agents to maximize information gain in the shortest possible time. This framework can be applied to scenarios like the aftermath of a disaster, when efforts to identify building damage is critical and time-sensitive.
In the immediate aftermath of a disaster, reconnaissance efforts to identify building damage severity and distribution are critical for search and rescue and other time-sensitive decisions. However, existing data collection and analytical processes are less responsive to unforeseen and unexpected circumstances. Therefore, this project develops a novel adaptive data collection framework that constantly analyzes the most recent observations to determine and update the trajectory of data collector agents toward areas with the greatest potential for information gain. It enables these agents to collect reliable data under severe time and resource constraints. The outcomes of this project set the stage for automated damage assessment systems to improve the resilience of built environments and citizens in hazard-prone regions. A set of educational and outreach efforts are envisioned for broadly disseminating the research findings and integrating them into undergraduate and graduate courses.
The adaptive data collection system is built on a novel hierarchical Bayesian framework for modeling disaster damage levels and Bayesian optimization for adaptive destination identification and trajectory planning. This method first relies on a pre-disaster preliminary probabilistic model of physical damage levels for different types of structures at the census tract level of granularity using a priori information and spatial attributes available to the public. It then creates and constantly updates distributions of physical damage levels across census tracts by integrating with the collected data from visited zones. Next, it dynamically and adaptively determines the trajectories for multiple agents to maximize information gain in the shortest possible time. The Bayesian probabilistic models could be transferable to other complex problems such as environmental pollution assessment.