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Project Supervisors

Professors Moncef Krarti and Yida Zhang

Project Description

The proposed project will evaluate geotechnical and thermal performance as well as sustainability and resiliency effectiveness for thermo-active foundations (TAFs) as alternative heating and cooling systems for buildings. The project will quantify the properties governing the thermo-hydraulic-mechanical response of soils (including clay, silt, and sand) under the stress-state associated with TAFs. Through detailed modeling analysis, the project will optimize the design and operation of the TAFs under various climatic conditions and building applications. Specifically, the proposed project aims (i) to evaluate the long-term structural and thermal performance of TAFs, (ii) to investigate suitable building types and climates for TAF applications, and (iii) to assess both energy efficiency and resiliency performance of TAF systems and ultimately develop a set of design and operation guidelines.

Project Supervisors

Associate Professor Shideh Dashti, Assistant Research Professor Brad Wham and Assistant Professor Srikanth Madabhushi

Project Description

This research integrates centrifuge, numerical, and statistical modeling to characterize the seismic performance of continuous, large-diameter, water distribution pipelines buried in non-uniform liquefiable sites. Failure of lifelines (e.g., drinking or wastewater pipelines) can have dramatic and cascading effects on disaster response and recovery. Service continuity of pipeline systems, or the ability to quickly repair them after a disaster, is critical. Due to their extensive geographic distribution, many pipelines need to be installed in geotechnically hazardous ground (e.g., liquefiable soils that naturally have non-uniform properties and geometry). The seismic performance of these critical lifelines has been inadequate in prior case histories. The primary objectives of this research are to: 1) evaluate mechanistically and systematically the 3-D permanent ground deformation patterns and simultaneous transient demand imposed on continuous pipelines buried in non-uniform and gently-sloped liquefiable soils via centrifuge modeling; 2) develop well-calibrated and validated numerical tools, (b) prepare analytical guidelines for evaluating key performance parameters, and (c) create a statistically-designed numerical database of pipeline performance; and 3) develop physics-informed and probabilistic predictive models of seismic performance of large-diameter pipelines buried in liquefiable soils, accounting for total uncertainty.

Project Supervisors

Professor Amir Behzadan and Associate Professor Jeong-Hoon Song

Project Description

Evacuation analysis is essential for reducing strain on infrastructure during significant distress events such as major disasters. A well-organized and timely evacuation provides equitable support for vulnerable populations and allows for continued refinement of strategies based on lessons learned from past experiences. This project seeks to improve the predictability of mass evacuations, ultimately enhancing community resilience and equity. Addressing this challenge requires a multidisciplinary approach, integrating expertise in data science, human behavior, and advanced computational modeling to simulate community interactions and decision-making during distress events. These insights will inform the development of more effective and adaptive evacuation strategies that respond dynamically to real-time conditions and behavior. Throughout the project, students will gain hands-on experience with cutting-edge technologies in data analysis, machine learning, and computational modeling, all aimed at creating more robust and adaptive evacuation strategies. Additionally, students will have the opportunity to collaborate with ºÚÁϳԹÏ’s leading researchers in infrastructure engineering and human behavioral science, while actively engaging with other GAANN project teams.

Project Supervisors

Associate Professor Jeong-Hoon Song and Professor Rajagopalan Balaji

Project Description

Understanding the ENSO (El Niño-Southern Oscillation) model is crucial for climate prediction, as it provides insights into one of the most significant drivers of global weather variability. ENSO’s dynamic changes substantially influence extreme weather events worldwide. This project seeks to advance the ENSO model to mitigate the socio-economic impacts of climate variability and improve decision-making in climate-sensitive civil engineering sectors. Given the multidisciplinary nature of this challenge, the project is positioned at the intersection of engineering science, data science, and climate research, combining computational modeling and data analysis to investigate the dynamics of the ENSO model. Through their involvement, students will acquire advanced knowledge and expertise in computational modeling of dynamic systems, stochastic data analysis, and machine learning techniques, all directed toward enhancing the predictive capabilities of the ENSO model. Furthermore, students will have the opportunity to collaborate with leading research institutes, such as the National Center for Atmospheric Research (NCAR), and engage with other GAANN project teams.

Project Supervisors

Associate Professor