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
Physics-Informed Machine Learning for Predicting Rubber–Surface Friction from Dynamic Friction Tester Measurements: Load–Speed Interaction, Load, Roughness, and Material Response
Hamedi, Sajad (Virginia Tech, 2026-09-11)
The interaction between tires and the road is one of the main factors determining how safely and effectively a vehicle can operate. Predicting this friction, however, is far from straightforward because it depends on several variables, such as structural and material properties of tire, road roughness, tire slip, applied load, temperature, and velocity, that interact in complex and nonlinear ways. Existing models can handle simplified conditions but often fall short when applied to real
driving situations. This thesis addresses the problem through physics-informed machine learning. Approximately 600 steady-state friction observations produced by a Dynamic Friction Tester (DFT), collected by Su [8] at the CenTiRe Laboratory at Virginia Tech, were assembled and used to evaluate a range of machine learning techniques. Eight rubber compounds were tested across three standardized abrasive surfaces spanning coarse to fine texture. Random Forest, Support Vector Regression, Artificial Neural Network, and a Hybrid Stacking Ensemble were trained and compared against the measured friction coefficients. Because the measurements were made on abrasive papers rather than on pavement, the friction coefficients span a wider and higher range than a tire encounters in service; the models are therefore calibrated to a DFT-on-abrasive regime rather than to a road regime. The central contribution of this work lies in how the learning problem is posed rather than in the volume of data analyzed. Raw experimental inputs such as sliding velocity and viscoelastic loss modulus are coupled through the excitation frequency relationship 𝜔 ≈ 𝑞𝑉s described in Persson’s viscoelastic friction theory [2,59–61]. The feature basis was therefore re-expressed through two
mechanism-guided interaction terms: Fn×Vs, a power-dimensional load–speed interaction, and Ts×E″, a strength–damping coupling formed from the compound’s maximum tensile strength and viscoelastic loss modulus. The physical interpretation of the first term is deliberately limited: Fn×Vs is not the actual frictional power delivered at the interface, which is 𝐹f𝑉s= 𝜇𝐹n𝑉s. Su [8]
likewise expresses sliding work using friction force, 𝑊 = 𝐹f𝑠. The second term is also an engineered interaction rather than a derived physical quantity; its units, Pa², are not those of energy or dissipation. Models trained on this reparameterized feature set achieved high accuracy while producing feature-importance rankings that are interpretable in tribological terms. Overall, the study shows that a feature basis built from tribological reasoning yields accurate friction predictions whose importance structure is interpretable in mechanistic terms. A matched raw-feature control was subsequently trained on {Dp, Ts, Fn, Vs, E″, grit} under the same split, preprocessing and hyperparameters, and all four architectures were re-evaluated under leave-one-compound-out validation. The control shows that the composite terms are not universally more accurate: across twenty repeated splits they give the Random Forest a small but reproducible gain (ΔR² = +0.005, p = 0.024), while the raw basis is better for SVR and ANN and indistinguishable for the Hybrid Ensemble. Compound-holdout validation shows that the Random Forest predicts entirely unseen formulations with pooled R² = 0.921, including R² = 0.858 when trained on compounds A–G and tested on the withheld compound H. The claim advanced here is therefore one of interpretability, physical consistency and demonstrated material generalization rather than of universal predictive superiority. The framework is a step toward friction models that can be audited against theory as well as against data.
Surgery Markets in Trans-it: Construction of the Gender Affirming Surgery Market
Frommer, Stephen Lyndon (Virginia Tech, 2026-11-04)
Gender affirming surgeries are often denied, delayed, or covered only under paternalistic and pathologizing conditions. Meanwhile, comparable procedures such as breast reconstruction after mastectomy for cis women are covered due to constructions of the natural and normative. This dissertation asks how trans* people value gender affirming surgeries, what role they play in the valuation process, and how recognizing their agency in that process actively improves care. Reading trans* medicine through both biopolitics and economic sociology, it argues that the value of gender affirming care is not inherent but constructed — through the moral and cultural work of the courts, clinics, insurers, and markets that have long treated trans* embodiment as a disorder to correct. Drawing on 76 U.S. court cases (1975–2016), analysis of medical and institutional documents, and interviews with 13 trans* masculine people who pursued top surgery, this project sets out to show that biopower and economic valuation reproduce one another, and that trans* people themselves must define any just account of gender affirming care through support and connection, not correction and cure.
2026 Schnabel Engineering Lecture: Bayesian Thinking in Geo-Engineering
Baecher, Gregory B. (Virginia Tech, 2026-10-13)
The statistics you took in college is mostly worthless in geotechnical practice, but that is probably not a surprise. ‘Classical’ statistics doesn’t address the uncertainties that geotechnical engineers actually deal with, which are mostly epistemic: limited knowledge, changed site conditions, approximate models, too few data, and engineering judgment. The good news is that Bayesian Thinking provides a method for dealing with these epistemic uncertainties, and most geotechnical engineers are intuitively Bayesian so the approach is easily applied in practice. Project examples arising in site characterization, dam safety, systems risk analysis, and reliability will be discussed, along with the emblematic observational approach of Terzaghi which is a purely Bayesian concept.
Visual Accessibility Simulation: Pedagogical framework, computational methods, and implementation of a browser-based design tool
Tural, Mehmedalp (2026-10-30)
The design of interior environments influences the visual information available to people with low vision and, consequently, how they perceive, interpret, and navigate their surroundings. Understanding these relationships is important in interior design education, where students can examine how their decisions about spatial configuration, materials, and lighting may affect visual accessibility. Visual Accessibility Simulation is a browser-based educational platform developed with support from Virginia Tech's TLOS Technology-Enhanced Learning Grant. It combines explanatory resources, sample photographs, and an interactive simulator that accepts photographs and design renderings. An Empathetic Design Cycle connects image preparation and simulation with observation, documentation, discussion, and design revision. Browser access allows students to revisit these activities independently and at their own pace, alongside instructor-led studio work. This report presents the platform's pedagogical foundation, computational pipeline, and implementation. The methods include image resizing, field-of-view estimation, angular scaling, Gaussian and two-band acuity approximations, color adjustments, and condition-associated masks. The equations describe the browser's image transformations and distinguish them from the contrast-sensitivity methods that informed development. The report also identifies limits associated with image inputs, working resolution, parameter interpretation, and image-fixed representations of field loss. Examples from the website's sample library illustrate how students can connect changes in visual information to specific design questions. The platform supports reflective design inquiry rather than clinical assessment or photometric measurement. It gives students a way to examine visual information in their projects, explain why particular features matter, and justify proposed design responses
Assessing Healthy and Traditional Foods in Virginia Tribal Communities: Barriers and Facilitators from Lived Experiences
Powell, Tia (Virginia Tech, 2026-11-03)
Indigenous communities experience disproportionate rates of food insecurity, nutrition-related chronic diseases, and barriers to accessing both healthy and traditional foods. While these disparities have been well documented nationally, limited research has explored the unique experiences of Virginia’s Indigenous communities and how Virginia Cooperative Extension can support those communities. The purpose of this qualitative study was to examine the barriers and facilitators to accessing healthy and traditional foods through the lived experiences and stories of Indigenous community members in Virginia. Data collected through semi-structured interviews and a focus group discussion were analyzed using thematic analysis to identify common experiences, patterns, and emerging themes related to barriers and facilitators to access healthy and traditional foods. Participants identified barriers when accessing healthy and traditional foods, including rural food deserts, limited transportation, rising food costs, aging and physical limitations, historical loss of land, reduced opportunities for hunting, fishing, gardening, and food preservation, and the erosion of traditional ecological knowledge across generations. Participants also highlighted significant community strengths and facilitators to health and traditional food access, including strong family and community relationships, cultural revitalization efforts, community gardens, food forests, traditional food education, seed saving, food preservation, youth engagement, and collaborative partnerships with organizations such as Virginia Cooperative Extension. The findings demonstrate that Indigenous food sovereignty extends beyond food security to encompass cultural identity, self-determination, stewardship of ancestral knowledge, and community well-being. Based on the findings, recommendations are provided for culturally responsive nutrition education, community-engaged Cooperative Extension programming, and future partnerships that support Indigenous food sovereignty and improve access to healthy and traditional foods. By centering Indigenous voices and lived experiences, this research contributes to a growing body of knowledge that can inform public health practice, Extension programming, and policies that honor tribal priorities while promoting health equity.