VTechWorks
VTechWorks provides global access to Virginia Tech scholarship, including journal articles, books, theses, dissertations, conference papers, slide presentations, technical reports, working papers, administrative documents, videos, images, and more by faculty, students, and staff. Faculty can deposit items to VTechWorks from Elements, including journal articles covered by the University open access policy. Email vtechworks@vt.edu for help.
Open Access Policy
Virginia Tech's open access policy enables researchers to deposit the accepted version of scholarly articles with no embargo.
Theses and Dissertations
Virginia Tech was first in the world to require ETDs in 1997, and continues to add scans of older theses and dissertations.
Open Textbooks
More than 50 freely available and openly licensed textbooks are among our most downloaded items.
Communities in VTechWorks
Select a community to browse its collections.
Recent Submissions
Unprecedented Candidate, Uncertain Coverage: Media Framing of Trump and January 6th
Barco, Michael William (Virginia Tech, 2025-12-18)
This thesis examines how U.S. news outlets framed the January 6, 2021 attack on the U.S. Capitol during the year surrounding Donald Trump's 2024 presidential candidacy. The study analyzes over 40,000 articles from fifteen outlets across nonpartisan-centrist, Democratic-favoring, and Republican-favoring categories, coding articles that mentioned both Trump and January 6th for use of the term "insurrection." Results show that nonpartisan-centrist outlets steadily reduced their use of "insurrection" between May 2022 and April 2023, declining by more than half across the period. Democratic outlets also showed a modest decline, while Republican outlets remained inconsistent due to low baseline use and smaller sample sizes. Alternative framings such as "protest" were rare and did not replace "insurrection." The findings suggest that language softened gradually rather than shifting at a single point, influenced by newsroom caution, public polling that showed Trump as a likely frontrunner, and professional pressures to maintain neutrality.
Restoring the Chocó: Growth and Survival of Native Chocoan Trees Using Different Propagation Methods
Aparicio, Sebastian (Virginia Tech, 2025-12-18)
The Chocó region is one of the most biodiverse places on the planet and has been identified as a priority area for conservation due to its high levels of endemism and diversity, currently threatened by the ongoing loss of forest cover. In this context, establishing baseline data on the survival and growth rates of native species can contribute to the design of effective ecological restoration strategies in the region.
With this objective, we evaluated the establishment capacity of 28 native species propagated from large cuttings. The results showed that survival depended primarily on species identity, suggesting that propagation capacity by this method is associated with intrinsic adaptations. Although the use of large cuttings is a more economical method than using seedlings, there are certain limitations, such as the fact that not all species can be propagated by this method or the requirement of a high number of donors in order not to affect genetic variability.
Additionally, the survival and growth rates of 23 native species (seven of them endemic to the Ecuadorian Chocó) were documented in a restoration experiment based on the applied nucleation strategy. The results showed high variability among species, while edaphic factors such as more acidic, less dense soils with gentle slopes and eastward exposure favored growth and survival. Pioneer species such as Castilla elastica, Ficus tonduzii, and Cecropia sp. promoted the development of highly diverse plantations, possibly by acting as nurse species. Finally, plantations with a higher density of islands (each composed of 25 individuals) showed increased growth rates, which could reflect competitive or facilitative interactions between individuals.
This thesis presents key insights into the performance of various propagation methods and provides a scientific foundation to inform future forest restoration efforts in the Ecuadorian Chocó.
The Residual Strength of Liquefied Soil
Gallus, Erika (Virginia Tech, 2025-12-18)
Flow (or static) liquefaction is one of the most detrimental forms of ground failure. To determine flow slide potential, the residual strength of liquefied soil is needed. However, this is an extremely difficult parameter to estimate for soil deposits due to spatial variability of soil properties, potential for the formation of water films, the intermixing of soils, and the potential for partial drainage during flow liquefaction. Thus, the current state of practice for estimating the residual strength of liquefied soil (Sr) is via back calculations using case histories. However, the complexity of flow slides makes case histories difficult to interpret, and combined with the limited number of case histories, it inherently implies large uncertainties in the derived empirical relationships. Although such empirical relationships define the current state-of-practice for estimating Sr, laboratory studies and fundamental soil mechanics can provide insights and/or can be used to guide the form of the empirical relationships. For example, one issue that is an active area of debate is whether Sr normalizes by initial vertical effective stress (σ'vo). Olson and Stark (2002) present an empirical relationship estimating residual undrained strength ratio (i.e., Sr/σ'vo) as a function of normalized cone penetration test tip resistance whereas Kramer and Wang (2015) showed that Sr does not scale linearly with σ'vo. Hence, the objective of this study is to develop a more mechanistic understanding of the residual shear strength of liquefied soils based on fundamental soil mechanics and laboratory studies. Specifically, an expression for Sr/σ'vo is derived in terms of the effective angle of internal friction for residual strength, ϕ'r, and Skempton's pore water pressure coefficient for residual conditions, �r. Data from published laboratory studies are used to develop correlations for estimating both ϕ'r and A r. The results show that ϕ'r is relatively constant for a range of sands, and the variability in its value does not significantly affect the computed value of Sr/σ'vo. Additionally, the results show that �r correlates with the state parameter (ψ) for initial conditions and depends on whether the soil grains crush. Additionally, the value of �r significantly affects the computed value of Sr/σ'vo. The derived laboratory-data-based ψ - �r relationship is used in conjunction with an empirical relationship relating ψ and normalized cone penetration test (CPT) tip resistance (Qtn) to develop a relationship relating Sr/σ'vo to Qtn, which is then compared to similar relationships derived from back-analysis of case histories. The comparison shows that the proposed correlation most closely resembles Robertson's (2010) correlation once adjustments are made to the relationship between Q tn and ψ.
Enhancing Software Maintenance: A Research Investigation on Current Practices, Potential Improvements, and Procedure Automation
Kabir, Md Mahir Asef (Virginia Tech, 2025-12-18)
Software maintenance is one of the most important phases of the Software Development Life Cycle, as it prevents unexpected development issues and ensures long-term reliability. Proper maintenance reduces cost and protects software from security and run-time problems. However, software maintenance is widely recognized as a challenging and resource-demanding phase of the life cycle. Developers frequently encounter difficulties such as managing dependency updates, addressing metadata inconsistencies, and adapting to evolving project requirements. These recurring issues motivate the need for better insights into maintenance practices and the exploration of automated techniques that can improve reliability and efficiency. To address these challenges, this dissertation presents (1) an examination of current maintenance practices by developers, (2) an investigation of the feasibility of using Large Language Models for software maintenance, and (3) the development of a new tool to automatically detect bugs and support automated maintenance. First, we identified security-related best practices for JavaScript developers and examined how well they are followed in open-source projects. Our empirical study of 841 applications revealed frequent violations and showed that developers often ignore best practices due to perceived irrelevance or distrust in tools. These findings highlight limitations in human-driven maintenance and motivate the exploration of automated assistance. Next, we evaluated how Large Language Model (LLM) tools such as ChatGPT perform in maintenance tasks compared to human developers. By analyzing their performance in technical QandA and software revision, we found that ChatGPT provided better answers for 97 out of 130 Stack Overflow questions and successfully revised software for 22 out of 48 maintenance requests. While promising, our results indicate that LLMs struggle with context-specific precision. Finally, we developed a domain-specific language (DSL) and language engine tool (MECHECK) to detect metadata-related bugs in Java programs. We defined 15 rules from Spring and JUnit documentation and evaluated MECHECK using two datasets of 115 enterprise applications. MECHECK detected bugs with 100% precision, 96% recall, and 98% F-score, and identified 152 real-world bugs, 49 of which were confirmed fixes. These results demonstrate that MECHECK helps ensure the correct use of metadata and advances automated software maintenance. In summary, this research provides insight into software maintenance, its challenges, and how the process can be improved from understanding developer behavior, to leveraging AI assistance, to creating automated detection tools.
Modeling the Food-Water Nexus: A Spatio-temporal Accounting of Agricultural Land and Water Use in the United States
Lamsal, Gambhir (Virginia Tech, 2025-12-18)
Agriculture dominates both land and water use in the United States, and plays a pivotal role in both national food security and global agricultural trade. Yet, this critical system faces growing pressures from groundwater depletion, climate change, and competing demands for water across sectors. Addressing these challenges requires an integrated understanding of how croplands and water use have evolved through time, and how more efficient management can support agricultural production without expanding water withdrawals. This dissertation developed a unified, high-resolution framework linking agricultural land use, crop water consumption, and on-farm management to evaluate opportunities for sustainable intensification within the U.S. food–water nexus.
This dissertation first created HarvestGRID, a gridded dataset of irrigated and rainfed harvested areas for 30 major crops from 1981 to 2019. Existing datasets often face a trade-off between spatial detail and temporal coverage. Remote sensing provides fine spatial resolution but limited historical depth, while administrative records extend further back in time but have coarse spatial resolution and contain missing values. HarvestGRID bridges this divide by combining USDA survey and census records with satellite-based land-use products to create spatially explicit and temporally consistent maps of harvested area. The dataset captured long-term agricultural shifts, revealing that while the national extent of irrigated cropland has remained relatively stable, irrigation has declined in water-scarce western states and expanded in more humid eastern regions, reflecting adaptive responses to changing water availability.
Building on this spatial foundation, this dissertation then created MIrAg-US (Modeled Irrigated Agriculture of the United States), which provided the first multi-decadal, monthly record of crop water consumption for the same 30 crops using process-based crop growth models. U.S. irrigated croplands consumed an average of approximately 154 cubic kilometers of water annually, with about 70 percent derived from blue water sources (irrigation). Alfalfa and corn together accounted for nearly 40 percent of this total, underscoring the dominance of a few key crops in national water demand. Modelled estimates from MIrAg-US were rigorously evaluated against multiple independent data sources, including government water-use records, previously published model estimates, and remotely sensed evapotranspiration datasets. The comparisons demonstrated generally strong agreement, although alignment varied by region and crop type, reflecting both differences in modeling frameworks and input data.
Finally, we utilized the modelling framework developed in previous chapters and evaluated the potential for increasing U.S. food production through improved on-farm water management, explicitly accounting for the rebound effect i.e. the reinvestment of saved water into expanded cultivation. Using AquaCrop-OS simulations, we quantified the water savings achievable through the adoption of high-efficiency irrigation technologies (sprinkler and drip) and organic mulching across 13 major irrigated crops, and modeled the reallocation of this saved water within the same watershed. Nationally, these practices could save up to 27.4 billion cubic meters of irrigation water annually (30% of current total applied irrigation), and reallocation of this water could expand irrigated croplands by as much as 6.2 million hectares, primarily by converting rainfed cropland. This expansion would increase national crop production by approximately 21 million metric tons per year (an 8.9% gain), valued at $4.7 billion annually.
Together, these studies create a cohesive empirical and modeling foundation for understanding agricultural water sustainability in the United States. Beyond documenting past change, this work establishes a pathway that links crop modeling and human decision-making to guide data-driven strategies for managing water and food systems under a changing climate.