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.
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Recent Submissions
Physics-Informed Monocular Estimation and Tracking of Three-Dimensional Object Position Using Known Geometry and Instrumented Observer Motion
Alhendi, Hamad Gh F. A. A. (Virginia Tech, 2026-09-11)
This dissertation presents a physics-informed method for estimating and tracking the three-dimensional position of rigid objects with known geometry using a single monocular camera mounted on an instrumented moving observer. The proposed method uses the object's known dimensions, the observer's recorded motion, and classical estimation techniques, rather than learned models or extensive training data. The proposed method relies on three stated assumptions: (1) the target object is planar, (2) the object has known dimensions and geometry, and (3) the object's principal face remains nearly parallel to the image plane during the vehicle's motion. These assumptions align with the characteristics of geometrically regulated roadside signs (by U.S. Department of Transport) encountered along a vehicle's path and define the proposed method's scope. The proposed method comprises four validated processes. First, a calibrated projection chain links the monocular camera's coordinate system to the vehicle's coordinate system. Second, a sub-pixel edge detector identifies the roadside sign's boundaries at the steepest slope of noise-weighted color-channel responses, providing a confidence for each reading and withholding a result when no high-confidence edge is detected. Third, a consensus-gated adaptation aligns a 3-D wireframe of the roadside sign's true physical size with the detected edges using normalized least-mean-squares updates with per-coordinate clamps, subjecting each fit to fit-quality, retry, and physical-displacement tests before acceptance. Finally, a constant-acceleration motion model integrates the vehicle's recorded motion into the update of every coordinate: as a directly measured velocity on the range coordinate, and as position-derived velocity values on the lateral and vertical coordinates.
The proposed method is evaluated using four instrumented test-track datasets that include three roadside sign types and velocity profiles from nominally constant velocity to a complete brake to standstill. Ground truth is established through the surveyed roadside sign positions and the vehicle's Global Positioning System (GPS)-aided inertial navigation system. The proposed method achieves centimeter-level accuracy in lateral and vertical tracking throughout each dataset, with range errors constrained by the quadratic measurement sensitivity inherent to monocular size-based ranging. All residual systematic errors are characterized and bounded, in one case through the testing and rejection of a competing speed-dependent model. The ego-vehicle velocity fusion yields the largest single accuracy improvement: lateral estimate errors on the most challenging roadside sign decrease by more than five fold, and up to fourteen fold across the roadside signs. The proposed method is compared against two optical-flow alternatives evaluated on identical inputs and hardware, the proposed method's accuracy advantage is an order of magnitude better on the lateral and range coordinates in every one of the twelve roadside sign approaches, at a fraction of the per-frame computation time, completing all estimation steps in a lightweight CPU implementation suited to embedded systems. These results demonstrate that reliable three-dimensional tracking of known roadside signs can be achieved at significantly reduced computational cost when the observer's instrumented motion and the object's known geometry are treated as measurements in their own right. This capability suits low-cost vehicles and embedded platforms, and serves as a template for estimation problems in any domain where the platform already stores part of the required information.
Data-Driven Exploration of Spinodoid Materials for Mechanical and Thermal Properties
Yildiz, Saltuk (Virginia Tech, 2026-09-11)
Spinodoids are a new class of architected materials with smooth, spatially correlated topolo gies, inspired by spinodal decomposition, a phase separation phenomenon. Generated stochas tically through a mathematical formulation based on Gaussian Random Fields (GRFs), they enable an extensive design space covering both periodic and non-periodic architectures. Their smooth, low-curvature geometric features offer enhanced material response and highly tun able anisotropic properties compared to traditional surface- or strut-based architected ma terials, making them promising candidates for engineering applications. This dissertation explores the design spaces of spinodoids for mechanical and thermal properties using nu merical and computationally efficient data-driven frameworks. In Chapter 2, a numerical and data-driven design study is carried out to explore the optimum design parameters of two-dimensional (2D) metal-based porous spinodoids for improved mechanical performance.
In the proposed approach, spinodoids are designed by parameterizing the orientation vectors defined in the GRF formulation. Finite element analysis (FEA) is performed to compute their elastic stress-strain responses, which are compared to those of traditional truss-based lattice structures. The strain energy density function is calculated under tensile and shear deformations, and an optimization problem is formulated to minimize strain energy frac tions. This problem is solved with the pattern search algorithm using the physics-based framework and a deep-learning-based surrogate model. Chapter 3 then presents a numer ical homogenization and design exploration framework for the thermal conductivities of ceramic composite spinodoids. The orientations of the spinodoids are designed to achieve enhanced homogenized thermal conductivity along different directions. For this purpose, their anisotropic thermal conductivities are determined via steady-state thermal conduc tion simulations combined with a volume-averaging method. A physics-based optimization framework is then implemented to perform topology optimization. Exploring the design of these architected materials with conventional physics-based methods becomes computation ally prohibitive when multiphysics properties must be evaluated. To address this, Chapter 4 presents a computationally efficient data-driven framework for exploring and optimizing the mechanical and thermal properties of dual-phase metallic spinodoids. A convolutional neural network (CNN) is developed to predict homogenized elastic and thermal properties by relating images of 2D spinodoids to properties computed via FEA. Next, the CNN model is coupled with a global optimization solver to design dual-phase spinodoids with improved mul tiphysics objectives. Finally, Chapter 5 presents a deep learning-assisted surrogate modeling framework for the inverse design of spinodoids with targeted anisotropic thermal conduc tivity. The GRF-based formulation is extended to three-dimensional (3D) representations of porous spinodoids, which are then modeled as representative volume elements (RVEs) and analyzed via FEA to extract homogenized thermal conductivities in three orthogonal directions. The resulting simulation dataset is used to train a feed-forward neural network (FNN) model that serves as a surrogate for predicting homogenized thermal properties. This surrogate replaces computationally expensive numerical simulations and is embedded within a genetic algorithm (GA) to perform efficient inverse design of spinodoids with prescribed thermal properties.
The Sensory Connotation of Meaning and the Cues That Fit a Brand
Lee, Yeh Jun (Virginia Tech, 2026-09-11)
Brands use names, designs, sounds, and scents to express abstract meanings. This article defines sensory connotation as a meaning's position on attributes such as dark–bright, angular–round, and warm–cool, and uses language models to estimate it at scale. The method uses a compact set of fixed axes that can score new words, phrases, and cue libraries without a new human norming study for every target. We test each claim against evidence that the scoring models did not produce. Human raters placed 80 concepts on the same seven axes and agreed with the instrument at r = .80. A single model predicted held-out human means more closely than an individual rater (mean r = .77 vs. .51), and a four-model consensus reached r = .79. Profile similarity predicted how similar concept pairs felt beyond embeddings, valence, arousal, dominance, and human free association (b = .146, p < .001). Across 6,236 commercial perfumes, names, disclosed notes, and bottle designs showed corresponding profiles across language and pixels. Randomly assigned profile-matched scent descriptions received higher fit ratings than mismatched descriptions. In a preregistered study, profile fit predicted larger stated store-scenting allocations beyond expected pleasantness. Delivered-odor choices showed a similar, though less conclusive, pattern, including a significant preregistered supporting within-name test. A label-blind "blind painter" tests what the profile itself contains by rendering cues after the target label is removed. Together, these results show how sensory connotation can measure meaning, test whether that measure captures shared structure, and rank candidate cues before those cues are built.
Station to Station
Huskison, Kennan Zachary (Virginia Tech, 2026-09-10)
The study will be based around the Appalachian Interstate System and its relationship to people, primarily through the lens of trucks and truck parking. By understanding the site at a regional scale, a local scale, and a site scale, a simple architecture can be developed to impact Appalachia and the conditions studied. This information is used to develop a simple and versatile modular system that can be easily placed across Appalachia as an occupiable human infrastructure for these large vehicles. It connects a hollowed-out economic necessity with community and human need.
Connecting Professional and Educational Communities to Prepare Construction Engineering Students for the Workplace
Yusuf, Anthony Olukayode (Virginia Tech, 2026-09-10)
The construction industry in the United States is one of the country's largest sectors. However, skills gap has been a persistent challenge in the construction industry, characterized by a shortage of personnel who possess both technical competencies and the soft skills necessary for effective performance in complex project environments. This has been a concern for academia and industry leaders as it threatens the continued growth and productivity of the industry. The ripple effect includes additional effort and resources required to train new hires, low employability of fresh graduates, pressure on higher education institutions to produce job-ready graduates, and dissatisfaction among industry employers. To address these skill gaps, greater interaction between instructors and construction practitioners has been identified as critical to imbuing students with professional ways of thinking, enhancing their meaning making ability, develop their professional identity as well as complement theoretical knowledge with practical insights and the development of soft skills. However, instructors' access to practitioners in ways that align with course needs remains limited, and innovative means to improve this connection are still needed. Grounded in Connectivism Learning Theory, this study investigates the potential of a web platform to improve instructors' access to practitioners and contribute to student preparation for the industry. Using a mixed-methods approach, the research first investigated the considerations of instructors and practitioners when collaborating to meet course needs. The findings were then leveraged to inform the graphical user interface design requirements of a web platform intended to connect instructors with practitioners. Subsequently, guided by human factors principles, user-centered design principles, and usability heuristics, a Construction Practitioners and Educators Collaboration (ConPEC) platform was designed and developed. A formative evaluation was conducted with instructors and practitioners, focusing on usability, trust, cognitive load, and intention to use. The outcomes of the evaluation informed the iterative refinement of the platform. The platform was then deployed for public use by instructors and practitioners. Finally, through a summative assessment involving instructors, practitioners, and students, the study examined how the ConPEC platform contributes to improving students' preparation for the workplace. The findings showed that the ConPEC platform enhanced instructor access to practitioners and strengthened instructor–practitioner collaboration to meet course-support needs. This, in turn, contributed to students' professional preparation by facilitating professional identity development, self-efficacy, motivation, confidence, and a deeper integration of theoretical knowledge with practical insights and industry realities. This research demonstrates the efficacy of an innovative, technology-enabled framework for enhancing construction education. The study contributes to Connectivism Learning Theory by illustrating how technology-mediated networks can support professional learning and workforce development, and it provides a scalable approach that can be adopted and adapted in other educational domains.