Example Projects

The student applicants are not bound to work on the projects listed below. These are only a few examples of possible research projects and collaborations through this GAANN opportunity. The students can define their own research and list of advisors.

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 CU Â鶹ӰԺ’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 Mija Hubler and Associate Professor Jeong-Hoon Song

Project Description

The primary mechanism that compromises reinforced concrete infrastructure is moisture ingress. Many of our reinforced concrete structures are now several decades old and have experienced cracking, corrosion, carbonation, freeze-thaw cycles, and other forms of damage. These structures are in a complex, aged state, which results in unique, environmentally sensitive, and nonlinear moisture ingress behavior. Therefore, improving our understanding and predictive capabilities of multiphysics-based moisture ingress in porous cementitious materials is crucial for developing climate-ready infrastructure for the next generation. In this project, we will leverage integrated sensing technologies and data-driven computational methods to capture real-time moisture movement in aged concrete. By applying machine learning and advanced data analytics to sensor data, we aim to enhance predictive models of moisture transport in porous cementitious materials. These models will account for local environmental conditions and structural health data, enabling targeted, region-specific repair strategies. This approach will extend the lifespan of reinforced concrete structures and support the development of climate-resilient infrastructure for the future. 

Project Supervisors

Associate Professor Aditi Bhaskar and Professor Abbie Liel

Project Description

In Colorado and other western U.S. states, two major climate-change related challenges are increasing water scarcity due to declining snowpack and the increasing wildfires impacting urban areas.  One major proposed solution for water scarcity is to reduce urban outdoor water use, which often makes up the majority of urban water use in the western U.S.  Recently for example, more than 30 water agencies in the Colorado River Basin, including Denver Water, committed to remove 30% of non-functional turf and reduce the demand in the basin.  These strategies include large-scale landscape transformations by removing conventional turfgrass and replacing with water-wise landscaping.  One perceived barrier is that these water-wise landscaping are mutually exclusive with fire-wise landscaping.  In this research, we will use modeling of fire simulation and statistical approaches to define the relationship between wise-wise and fire-wise landscapes.  We will also explore the mechanisms used to motivate property owners to comply with these ideas, their success, and their relation to also advancing fire resilience goals. 

Project Supervisors

Associate Professor Mija Hubler and Professor Rajagopalan Balaji

Project Description

The response of concrete infrastructure to increased frequency and intensity of natural hazards depends on the structural health and state of degradation of those structures when subjected to extreme events. Current methods for describing the state of concrete structures in the field are largely quantitative visual inspections for damage. Based on existing knowledge of how concrete ages, we propose to develop a novel approach to quantifying aging of concrete structures. Our proposed novel method is built on adapting ‘paleoclimatology methods’ – i.e. reconstruction of past climates using tree rings and marine sediment cores, also known as ‘paleo proxy’ data. In these, statistical models are developed between climate attributes and proxy data attributes, which are then used to reconstruct past climate. We will collect cores from existing concrete structures, much like the tree and marine cores. Much like tree ring analysis, the layers of chemical and mechanical degradation are compared with variations of local climate data to develop statistical models. These models capture the variability of degradation as a function of time and climate. Evaluating these functions across infrastructure and in different regions will offer insights into the temporal variability of concrete degradation. This will be of immense use in enhancing the skill of the age models, besides, enabling the prediction of degradation and consequently, the infrastructure life under future climate. 

Project Supervisors

Assistant Professor Cristina Torres-Machi and Professor Rajagopalan Balaji

Project Description

To reduce the vulnerability of our transportation system to a changing climate, there is a need to better understand the variability of space-time climatic risks across the country.  This project will identify the suite of climate attributes that impact transportation system resilience, (2) model the space-time variability and consequently the projection of their risks under climate variability and change, (3) translate the climate risks to transportation system vulnerability, and (4) develop climate-informed rubrics and strategies to enhance system resiliency. This research will employ Bayesian Hierarchical Models (BHMs) and Statistical (i.e., Machine) Learning methods to analyze pavements from representative regions across the country that are influenced by diverse climate extremes.

Project Supervisors

Associate Professor Shideh Dashti, Assistant Professor Cristina Torres-Machi, Professor Abbie Liel

Project Description

Climatic extremes (e.g., prolonged drought and flood cycles) affect both shallow and deep soil layers. The influence of these changes on expansive/hydro collapsible soil hazards and damage to surface and buried transportation systems poses a critical knowledge gap in a changing climate. This research will integrate physical experiments (primarily centrifuge testing at CU) with numerical modeling and simulation to investigate and characterize the dynamic thermo-hydraulic-mechanical (THM) behavior of granular deposits and develop reliable predictive models for ground failure and performance of slopes and embankments supporting transportation infrastructure under multi-hazard conditions. We will (1) evaluate the THM response and properties of granular soils under seismic loading at the boundary-value level (using the centrifuge) for different types of road embankments and slopes; (2) validate numerical models with experimental results; (3) prepare more generalizable boundary value problems for predicting slope displacements in road embankments and develop fragility curves; and (4) simulate the effectiveness of possible adaptation strategies to enhance the resilience of the transportation system and derive recommendations on optimal solutions. 

Project Supervisors

Assistant Professor Srikanth S. C. Madabhushi and Professor Abbie Liel

Project Description

Storm surges, wind waves and Tsunamis can have deadly consequences for vulnerable coastal communities around the globe. Climatic changes leading to warmer oceans and elevated sea levels can elevate the frequency and intensity of tropical storms and the loading on coastal structures. At the same time, there is a pressing need to decrease the carbon footprint of civil engineering infrastructure, including coastal defenses, that have typically relied on large volumes of man-made materials such as steel and concrete. In this project, the scope to improve the performance and efficiency of coastal defenses will be researched by exploring composite geo-structural designs that can leverage the strength and stiffness of soil between structural elements. In order to converge coastal, geotechnical and structural engineering a combination of experimental (specifically hyper-gravity centrifuge modeling), numerical and probabilistic methods are envisioned to demonstrate feasibility and reduction in risk associated with composite geo-structural designs. 

Project Supervisors

Professor Moncef Krarti and Professor Abbie Liel

Project Description

In the last few decades, kinetic architecture has been promoted as a new design approach for buildings to be more interactive, responsive, and adaptable to changes in climatic conditions and occupant needs. This design approach, also referred to as dynamic architecture, has been made possible with the development of innovative building materials, flexible construction methods, advanced control and sensing devices, as well as complex computer simulation tools and techniques. Kinetic architecture seeks to incorporate dynamic elements within building envelope systems and facades or to design buildings themselves to move and change form and shape. For this project, optimized structural and thermal analysis approaches of dynamic buildings including rotating structures will be evaluated and applied for selected case studies in various US climates to account for the effects of climate change and extreme events (i.e. consider extreme temperatures and higher wind loads).

Project Supervisors

Professor Amy Javernick-Will and Professor Abbie Liel

Project Description

Society expects school infrastructure to be safe and to protect children. Yet, the infrastructure of schools can be vulnerable to acute hazards such as hurricanes and earthquakes and to chronic hazards such as mold and inadequate ventilation. These hazards can make school infrastructure unsafe and adversely affect children's health, school attendance, learning and future prosperity. These risks disproportionally affect children in lower-income and minority groups. This project studies school infrastructure in Puerto Rico and similar environments.  Working with community groups, we aim to study co-production of infrastructure knowledge with community groups to inform engineering and policy assessments and investigate engineering mitigation and policy solutions. We plan to study this in Puerto Rico and similar contexts. 

Project Supervisors

Professor Amy Javernick-Will and Professor Rajagopalan Balaji

Project Description

The World Database of Happiness (Veenhoven 2022) defines happiness as the subjective enjoyment of an individual’s life as a whole.  The ways engineers design and build infrastructure can affect the happiness and well-being of a population.  The combined impact of social, infrastructural, and environmental conditions can influence citizen happiness.   Within this research, we aim to explore the infrastructural elements that influence citizen happiness and wellbeing within geographical regions/neighborhoods. This could include, for instance, social infrastructure, transportation infrastructure, disaster risk resilience,  public spaces, air quality, water quality, housing affordability, housing quality, etc.  Starting from a literature review, we will identify elements theorized to influence wellbeing, and analyze large, publicly available datasets to determine correlations of infrastructure conditions and happiness by geographical location.  We would then transition to a smaller case study approach in cities/neighborhoods, where we would collect primary data to uncover rich causal conditions associated with happiness and recommend strategies to foster wellbeing. Ultimately, we aim to suggest strategies to enhance well-being with resilience, and recommend strategies for post-disaster rebuilding or upgrading built environments. 

Project Supervisors

Professor Balaji Rajagopalan and Associate Professor Aditi Bhaskar

Project Description

Urban water and environmental infrastructure - such as stormwater drainage, water quality treatment systems, urban trees and green spaces are crucial for a healthy and sustainable society. These infrastructure are also highly vulnerable to climate variability and extremes and are aging and subjected to increasing frequency and magnitude of climate extremes, exacerbated by a warmer future climate. Cities are also facing tough decisions with regards to these infrastructure - retrofit, replace, repair, maintenance amidst tighter budgets. In this project we propose to - (i) understand the signatures of various attributes of climate variability and extremes on the performance  of these urban infrastructures, (ii) model their space-time variability and the impact on the infrastructure vulnerability and (iii) explore potential alternatives in increasing the resiliency and reliability.