MAINS LAB
Primary Research Areas
Despite the extensive research in this area over the recent years, measuring resilience and predicting the intricate performance of complex structures and infrastructure systems exposed to natural hazards remain challenging to date. MAINS Lab's research efforts respond to the conventional challenges by bridging the two domains of Structural Engineering and Applied Machine Learning. In particular, a central goal of this research group is the development of computationally effective vulnerability assessment tools for two primary areas explained below.
Performance-based Vulnerability Assessment of Structures subjected to Catastrophic Events
MAINS Lab researchers provide a deeper understanding of structural behavior by improving the prediction accuracy of damage. The overarching goal of this research area is to enhance the robustness of structures to natural and manmade hazards by developing a more reliable structural damage estimation and predicting the effects of hazards on the performance of structures.
We propose computational frameworks to develop probabilistic seismic demand models and generate fragility models using advanced numerical modeling and analysis methods in combination with probabilistic techniques. For this purpose, MAINS Lab researchers develop probabilistic frameworks for the analytical performance-based evaluation of various structural systems (e.g., bridges, timber buildings, towers, earth structures such as dams, etc.) under natural disasters such as earthquakes, floods, aging effects, extreme winds, tornadoes, coastal inundation, and climate changes. The outcomes pave the path toward increasing awareness of disaster-related risks and developing an effective post-disaster recovery strategy.
Multi-hazard Infrastructure Resilience Assessment
Researchers at MAINS Lab aim to improve the sustainability of interconnected infrastructures and address questions in risk assessment, pre-disaster mitigation, and post-disaster efforts of civil infrastructures and lifeline systems such as transportation, telecommunication, water, gas, and electric supply subjected to natural hazards. For this purpose, we perform probabilistic post-event recovery, develop propagation and restoration models, and quantify the probabilistic resilience of infrastructure to detect the influential components on the overall functionality of the system. We propose an effective resilient network that can assist policy decisions to prepare for future disasters, mitigate the exposure of infrastructure, and prioritize disaster risk management investments.
