Our Projects

Recent earthquakes have demonstrated that code-conforming modern reinforced concrete (RC) buildings (i.e., post-1970s) can satisfy life safety performance objectives. However, the accumulated damage in these modern buildings raised concerns about their performance in any future events; contributing to widespread demolition and long-term closure of damaged buildings. The economic and environmental impacts associated with the demolition and long-term closure of modern buildings led to societal demands for improved design procedures to limit damage and shorten recovery time after earthquakes. We propose a repairability-based design approach for structural systems. The proposed approach targets recovery-based performance objectives through component deformation design limits that are defined to ensure that structural components are repairable (i.e., the components have sufficient residual capacity to withstand future events without requiring safety-critical repair) after design-level events. 

​

Our built environment is often subjected to multiple hazards’ concurrent and/or sequential impacts. Modeling multi-hazard interactions (i.e., possible interrelationships and interdependencies of hazard events with their associated frequencies and severities and potential impacts on a specific location or region) and simulating multi-hazard scenarios (i.e., realizations of possible multi-hazard interactions) to design and assess civil infrastructure is crucial. We have developed a probabilistic framework for simulating multi-hazard scenarios for civil infrastructure design and risk assessment. The framework efficiently characterizes the interrelationships and interdependencies between primary and secondary hazards using a series of occurrence models for the primary and secondary hazards and simulation-based approaches to generate the arrival times and features (e.g., severity) of all considered hazards over a defined space-time interval. Our proposed framework can help decision-makers design and test efficient disaster mitigation and management policies.

​

Understanding the impact of disasters to civil infrastructure systems can guide strategic pre-disaster preparedness and mitigation and post-disaster recovery planning. We have developed a resilience quantification framework for simulating post-disaster recovery of residential buildings, schools, bridge networks, and electric power and water networks. Our framework integrates statistical and heuristic methods (e.g., stochastic network analysis, greedy algorithm, probabilistic critical path analysis, multicriteria decision-making analysis) to simulate recovery trajectories under various technical, environmental, socioeconomic, political, and cultural conditions. Our models account for intra-system and inter-system interdependencies. We have applied our framework to both developed and developing countries. Our models can be applied to both single (e.g,, earthquakes, flood, hurricane, tsunami) and multiple hazard events.

Several design and detailing deficiencies have contributed to the poor performance of older and modern reinforced concrete buildings in past earthquakes. As a step towards risk mitigation, seismic assessment procedures continue to be developed to assess the response of existing structures at the component and global levels. Such procedures are aimed towards identifying seismic vulnerable buildings that need to be prioritized for intervention. In certain cases, existing codified procedures can be too conservative, resulting in unwarranted retrofit or building closures. Conservative seismic assessment procedures pose a significant financial burden on the global economy and may have an unintended negative impact on community resilience.

Over the last few years, we have developed seismic assessment procedures to replace a number of conservative procedures in the New Zealand and American Seismic Assessment Standards. A distinct feature of our proposed approaches is that they are mechanics-based. Mechanics-based models do not suffer from extrapolation and data overfitting problems associated with purely empirical models.

​