Stander Symposium Projects

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  Between Authority and Service: The LeBar Sisters

  Andrew McNeely

  American evangelicals have long engendered strict codes of gender hierarchy in churches and their related institutions. Twentieth-century evangelical education, however, discloses unique ironies women educators embodied in their reliance on these designated gender roles. Placed in liminal positions between authority and service, women educators transgressed boundaries between the “maleness” of given authority and the subservient nature proper to womanhood. This research explores this phenomenon by examining the educational careers of Mary and Lois LeBar, both of whom taught at Wheaton College from 1945 to 1975. Instead of rejecting gender hierarchy, they used their distinct gender roles as women tasked to serve others by appropriating progressive educational pedagogy that proved amenable to their agency as evangelical women educators. For the LeBars, this was as theologically imperative as it was pedagogically effective.

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  Beyond Barriers: A Study of Cultural Identity and Belonging Among Underrepresented Engineering Students

  Sama Ahmed

  Representation in engineering is essential for promoting diversity, inclusion, and student success. This study focuses on the cultural identity and sense of belonging among underrepresented engineering students, particularly those enrolled in the Multi-Ethnic Engineering Program (MEP) at the University of Dayton (UD). Data for this study emerges from semi-structured interviews of 10 students enrolled in MEP. It investigates the barriers these students faced, including limited resources, gender disparities, and negative stereotypes, with the goal of illuminating the challenges that affect their academic and professional goals. Building on existing research about identity and belonging in STEM fields, this study aims to pinpoint support systems that can enhance student retention and success. By delving into the experiences of these multi-ethnic engineering students, the research offers valuable insights into initiatives that foster inclusivity, such as mentorship programs and necessary institutional changes. Understanding the lived experiences of these students is crucial for advancing efforts to create a more equitable and supportive environment in engineering education.

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  Beyond Boundaries: Tracing the Threads of Systemic Inequity in Education, Housing, and Mental Healthcare - A Focus on Redlining in Dayton and its Impact on Youth Mental Health Today

  Alexis Taylor

  Health disparities resulting from redlining have been widely documented, yet limited research explores its impact on children's access to mental healthcare. This quantitative correlational study examines the relationship between residential segregation and access to mental health services among school-aged youth. Specifically, the study investigates how redlining influences health outcomes, school district funding, and the availability of mental healthcare resources. By highlighting the systemic barriers created by historical and ongoing segregation, this research aims to illuminate the disparities in youth mental health access and their broader implications. To assess this correlation, the study will analyze historical redlining data alongside current measures of mental healthcare accessibility in affected neighborhoods. Key factors include the proximity of mental health facilities, insurance coverage, and the presence of mental health professionals within school districts. The findings of this study will contribute to the growing discourse on systemic racism in healthcare and inform policy interventions aimed at reducing mental health disparities among marginalized youth.

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  Beyond the Red Line: Building Equitable Communities

  Maya Dalal, Peter Grant, Henry Hipp, Allison Vandergriff

  Historical redlining has created marginalized communities in the 21st century, including inside the classroom walls. Students from diverse populations bring their different funds of knowledge into the classroom, but historical redlining has created barriers to accessible resources and opportunities. Beyond the Red Line: Building Equitable Communities challenges students to create a forward-looking vision for equitable communities. Students will choose their preferred medium for their project. Aspects of students' designs will include innovative solutions to housing, economic mobility, and access to resources. It advocates for at-risk communities and prioritizes reimagining urban spaces where all students can access appropriate resources, opportunities, and a sense of belonging. Students will collaborate to create a neighborhood or community that promotes social justice and advocacy for their peers in their community.

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  Bio-Inspired Rotating Empennage (BIRE) - Desktop Model

  Ryan Rotsching

  A concept fighter aircraft is being investigated by the Air Force Research Labs that eliminates the vertical tail and uses a bio inspired rotating empennage (BIRE). The motion of the empennage is intended to mimic the agile flight displayed by birds of prey. To assist in communicating the mechanical concept, a desk-top demonstration model was created. Each part in the model is constructed primarily of additively manufactured (AM) components, allowing each component to be custom designed and swiftly manufactured to maximize functionality and accuracy. These components were based on the existing structure of the baseline F-16 and modeled in SolidWorks. The project involved research into different AM techniques and most appropriate process for each component. Two different variants are being constructed: 1) a simple internal structure demonstrating the functions of the BIRE, and 2) a topographically optimized solution. The results of these models allow visualization of the functionality and viability of the BIRE concept.

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  Bird Family Recognition

  Soham Chousalkar, Kasturi Avinash Jamale, Jayanth Merakanapalli

  In this research, we present a novel deep learning-based approach for bird detection and classification. Using YouTube videos as a data source, we train a model capable of accurately identifying bird species in diverse environments. Our dataset consists of 20 bird species, each categorized into two subclasses: parent and chick. Leveraging YOLO models, our system effectively detects and classifies birds under varying environmental conditions. The proposed method demonstrates high classification accuracy, contributing to advancements in automated bird identification. This work has significant applications in ecological monitoring and conservation efforts, aiding researchers in tracking and studying avian populations.

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  Breaking the Cycle Through Transformative Pedagogy: Exploring Reflection, Critical Thinking, and Structural Barriers to Social Deviance

  Destiny Rivera, Asja Steele

  Over one million people are incarcerated in the United States, with 62% of those released rearrested within three years. In Ohio alone, nearly one in three individuals returns to state prison within three years of release. Recent studies suggest that educational and vocational programming in correctional institutions can decrease recidivism rates while increasing post-release employment opportunities and wages. Education represents a significant opportunity for meaningful second chances. However, there is limited pedagogical research examining thestructural challenges incarcerated individuals face that either hinder or facilitate reentry into their communities. This qualitative study explores how transformative pedagogy in correctional settings fosters critical awareness of systemic barriers to rehabilitation. Using a phenomenological approach, we analyze reflective essays written by incarcerated students enrolled in educational programs across various correctional institutions in Ohio. By centering student voices, the study highlights the role of transformative learning in empoweringincarcerated individuals to recognize and challenge the social conditions influencing their behavior. Preliminary findings will be discussed, and the study contributes to broader discussions on prison education as a tool for disrupting cycles of deviance and fostering long-term socialchange.

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  Breaking the Silence: Addressing Harassment and Assault at the University of Dayton

  Taylor Gallagher, Katerina Metheney, Alexandria Moore, Lianna Shakoor

  In this class project for SOC 324: Communities & Crime, we worked to identify a crime problem or a plan to promote safety that is pertinent to our own community. We applied relevant social science theories in order to understand the problem of harassment and assault at the University of Dayton. Based on these theoretical explanations, we offer strategies to address the crime problem and to enhance community safety, as well as an action plan to implement our recommended solutions.

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  Bridging the Gap Between High School and College Sense of Belonging

  Julie Huber

  Students' sense of belonging in high school and students' sense of belonging in college have both been studied separately, but not much information has been recorded about the connection between the two. This study seeks to understand this relationship and look at other factors such as personality, athletics, and family life, and their role in this relationship. I conducted a short anonymous survey and concluded the study with follow up interviews to gain a more personal insight into what students' thoughts are on the connection between belonging in college and high school. The sample consisted of 82 undergraduate students currently attending the University of Dayton. Overall, I find a moderate positive association between belonging in high school and college that is influenced by multiple personal and social factors. These findings highlight the importance of considering high school experiences in shaping students’ perception of belonging and community in college.

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  Bridging the Gap in Industrial Energy Modeling through Lean Energy Analysis

  Sean Kapp, Gavin Mchale

  HVAC systems account for a significant portion of the energy consumption within the industrial sector. Specifically within chemical manufacturing facilities, fume hoods contribute heavily to HVAC energy use by continuously exhausting conditioned air. While advanced energy modeling techniques exist, small and medium-sized manufacturers (SMMs) often lack the resources and data required to implement complex machine learning-based solutions. An inability to collect useful information on energy patterns throughout the year can be a large obstacle for these facilities in deciding which changes will have the largest benefit for the company. Depending on the complexity of the energy model, predictions can be made based on a variety of factors. Changes in outdoor temperature plays a primary role in the variation of monthly energy usage. This study presents Lean Energy Analysis (LEA) as a practical and effective approach for assessing weather-dependent energy consumption in manufacturing facilities. LEA utilizes energy billing data to model energy-weather dependence through a piecewise linear changepoint analysis, enabling manufacturers to identify inefficiencies and predict energy savings from efficiency measures. A comparative analysis between LEA and the Random Forest (RF) machine learning model was conducted to validate the accuracy and utility of LEA for energy modeling. The results demonstrate that while RF models can provide strong predictive accuracy, they lack transparency and requires many features to be robust. In contrast, LEA effectively identifies independent energy usage, changepoint temperatures, and weather-dependent slopes as distinct, physically meaningful quantities, offering actionable insights for energy optimization without the need for extensive sensor networks. A case study is conducted at a chemical manufacturing facility where excessive fume hood usage was identified as a major contributor to HVAC energy waste. By applying LEA, the research team quantified the energy savings potential of lowering fume hood doors when not in use. Implementing this measure resulted in an annual reduction of 191,259 kWh in electricity and 729 MMBtu in natural gas, leading to cost savings of $18,851 and a carbon footprint reduction of 129 metric tons. This study highlights the advantages of LEA for SMMs seeking to optimize energy efficiency without the cost and complexity of high-tech energy modeling solutions. By leveraging historical data, LEA provides a low-cost, data-driven framework for energy assessment and sustainability improvements in industrial facilities.

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  Bridging the Gap: The Role of Hybrid Learning in Modern Legal Education

  Averee Richardson

  Hybrid legal education is reshaping the accessibility and effectiveness of law school, making it possible for students who might otherwise be unable to pursue a J.D. to enter the profession. The University of Dayton School of Law (UDSL) is at the forefront of this transformation with its ABA-accredited Hybrid J.D. program, which provides flexibility while maintaining academic rigor and strong student outcomes. This presentation explores how UDSL’s hybrid model benefits students—particularly rural, first-generation law students, and students who need to work full-time while attending law school. The program’s structure combines synchronous, asynchronous, and in-person intensives, allowing for meaningful engagement and experiential learning without requiring students to relocate or leave their careers. The result is a diverse and dynamic cohort of students who enrich the legal profession with unique perspectives and experiences. The program’s success is reflected in its high Bar passage rates and strong post-graduate employment outcomes, demonstrating that hybrid legal education can produce highly qualified and well-prepared attorneys. Despite being remote for much of their education, Hybrid J.D. students at UDSL actively participate in extracurricular opportunities, including Moot Court, Law Review, and student leadership roles. They also build strong cohort connections through virtual collaboration, social media, and in-person opportunities. UDSL’s model serves as a case study for the future of hybrid legal education, proving that flexibility and excellence can coexist. By widening access to legal education, hybrid programs are not only benefiting students but also strengthening the legal profession as a whole.

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  Bridging Theory and Practice: Advising Skills in the Career Services Functional Area

  Danny Bean

  Higher education and student affairs professionals must cultivate strong advising skills if they wish to maximize their marketability and become the best advocates for students that they can be. In this brief presentation, I will connect student development and organizational leadership theory to my internship with the University of Dayton Career Services office, specifically focusing on the bridge between academic study, vocation, and future career path.

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  Bugs Under the Sun: the Influence of Solar Infrastructure on Insect Communities

  Claire Qua

  As renewable energy is growing in its popularity for both industrial and residential use it is my goal to help fill the gaps in knowledge regarding the influence of solar infrastructure on insect communities. Insects are not only crucial pollinators for the flowers we love, but also the agriculture we depend on. Additionally, a particular emphasis was placed during this study on Orthopterans (specifically grasshoppers) as they are prolific in nature at these sites as well as in their extensive predatory behavior. Sweep net samples were collected at Topaz Solar Farm in San Margarita California within the two studied microhabitats (array aisles, and intentionally planted reference areas). Each sample was sorted morphologically to order and subsequently counted to measure abundance and order richness. Statistical analysis using including T-Tests, GLMM, and NMDS were conducted when appropriate to the common orders found. There was a statistical significance indicating a greater overall insect abundance in the reference area affirming my hypothesis. Of the studied orders no additional differences were seen aside from block location of Dipterans and microhabitat preference in Hemipterans. In regard to Orthopteran size, stage of development and pink morph prevalence results are forthcoming as data collection is underway. I anticipate a difference in tibia and overall length as well as mass indicating larger individuals within the reference area. The results exemplify the differences between microhabitats demonstrating the effectiveness of reference areas in mitigating possible land degradation that may occur when developing a solar farm. Solar prairies are a beneficial collaboration of renewable energy and ecological stewardship and are a significant step in the right direction.

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  Building a Dataset for American Sign Language Recognition

  Soham Chousalkar

  Sign language is a crucial means of communication for the Deaf and Hard of Hearing (DHH) community. However, the language barrier between ASL users and non-signers is a relevant factor. The purpose of this research is to create a real-time American Sign Language (ASL) to English translation system using computer vision and deep learning techniques. The ultimate objective is to bridge the gap by enabling seamless communication with the assistance of an AI-based translation model.The system adopts a deep learning-based approach, leveraging Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to handle ASL hand gestures and facial expressions from video input collected using a normal camera. Pre-trained models, i.e., MediaPipe Hands and OpenPose, are integrated to attain effective feature extraction. The Transformer-based model is integrated to interpret visual input into its corresponding English text with effective translation accuracy and contextual understanding.The database includes publicly available ASL gesture datasets and specially recorded sequences to improve recognition across different hand shapes, orientations, and illuminations. The system is trained to recognize isolated signs, sentence structures, and nuances such as finger spelling and co-articulated gestures. Natural Language Processing (NLP) techniques also refine the generated text for syntactic correctness.It aims to provide an intuitive application that delivers real-time feedback as text and speech output. The potential contributions include enhanced accessibility for ASL users, contributing to educational resources for learning sign language, and assisting the advancement of gesture-based AI modeling. Future includes extending the system to support different sign languages and implementing it on mobile and augmented reality platforms for usability by general users.This research falls into the agenda of innovation and inclusiveness, and it suggests an applicable AI-powered solution to make everyday communication for the DHH community better.

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  Building Bridges to Math Success: Exploring Children’s Flexible Attention to Numerical and Spatial Magnitudes

  Deja Richardson

  The study will analyze how children in early childhood engage with number books and how they influence mathematical abilities such as flexible attention to magnitudes (FAM) and executive functioning. It is hypothesized that the number of books consisting of mathematical language will lead to an increase in math ability. We will also explore the correlations between children’s engagement with the books and their outcomes. It is suggested that number books will engage children using mathematical language and real-world settings. This study will be a pretest-intervention-posttest design, where 40 participants between the ages of three and five years old recruited from daycare centers and preschools in the Dayton area will complete 6 one-on-one sessions with an experimenter in their school. The pretest will consist of the following assessments: Woodcock-Johnson IV Tests of Early Cognitive and Academic Development (WJ ECAD) Number Sense, WJ ECAD Picture Vocabulary, Give-N, and Minnesota Executive Function Scale (MEFS). The intervention will involve participants, who will be randomly assigned to one of two conditions, completing 4 reading sessions with an experimenter. The mixed condition involves number books with questions about size and number language, while the control condition will have questions about special colors. Each condition has two book settings, a farm, and a restaurant. The post-test will include the FAM Task and WJ ECAD Number Sense assessments. The study will analyze results from the book trials and assessments used in the pre and post-tests using ANCOVA analyses that control for pre-test and demographic covariates.

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  Can Sweat Equate to Smarts? Physical Activity and Its Connection to Academic Achievement

  Jackson Lucas

  Physical activity is an important part of an overall healthy life. Outside of obvious physical benefits, it has a plethora of other advantages. From this literature review, I share the connection between physical activity and academic achievement for middle and high school students.

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  Channel Assignment Problem

  Tom Jacob

  This is a project for MTH 466, Graph Theory and Combinatorics. A graph is a mathematical object that consists of two sets, a set of vertices and a set of edges. An edge joins two vertices and depicts a relationship between those vertices. By considering the vertices on a connected graph G of order n to be transmitters and the colors of the vertices to be the channels assigned to the transmitters, we can construct a model that represents the Channel Assignment Problem. This problem deals with the task of efficiently allocating channels to transmitters. Concepts such as radio k-coloring and radio labeling were inspired by the Channel Assignment Problem. By stipulating a minimum permitted distance rule, labeling vertices based on the assignment of their colors, and considering two vertices to be adjacent if they are sufficiently close to each other, it is possible to organize a network of channels that does not overlap each other. The Channel Assignment Problem has origins in assigning channels to FM radio stations through prevention of interference by keeping separation between stations based on signal power, height of their antennas, and frequency.

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  Characterization of a Drosophila CRC 3-hit model using genetic approaches and impact of inhibitors on tumor growth

  Sydney Anderson

  Colorectal cancer (CRC) is the second leading cause of cancer deaths in the United States, resulting in the deaths of over 50,000 people every year (American Cancer Society, 2023). The similarities shared between mammal and Drosophila melanogaster anatomy within the intestinal tract make Drosophila a great model for studying colorectal cancer. This study will investigate tumor characteristics of a Drosophila CRC model generated by modulating three genes within the key Hippo pathway to create a 3-hit model: p53, RasV12, and APC. The gene combination in the 3-hit model closely emulates how CRC presents in humans and is therefore important to study. This study will (a) characterize the tumors in the guts of Drosophila for invasion, metastasis, and other phenotypes such as blockage of the intestinal tract. This study will also (b) investigate the impact of different pathway inhibitors as single or combination therapies on tumor size and metastasis. The results of this study will expand the discipline’s knowledge of CRC tumor characteristics, and metastasis to investigate the effects of new single or combination therapies on CRC.

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  Characterizing Limitations and Biases of Diffuse Reflectance-Based Technologies for Disease Detection and Prognosis to Facilitate More Equitable and Inclusive Healthcare Outcomes

  Rana Dey

  Diffuse reflectance-based technologies have shown potential to significantly advance disease detection and prognosis in dermatology and other clinical applications. However, these technologies also have the potential to exhibit biases, particularly against individuals with darker skin tones, which can lead to disparities in effectiveness of diagnosis and treatment if the sources of the bias are not properly identified and corrected. For instance, patients with darker skin who are diagnosed with melanoma typically receive the diagnosis at a later stage than their white counterparts. Similarly, current hypoxia assessment methods, including pulse oximetry, have demonstrated reduced accuracy in measured blood oxygenation values for individuals with darker skin, contributing to potential misdiagnosis and inadequate treatment. This study aims to investigate and address these types of disparities through computational modeling of light-tissue interaction, as well as the design of tissue-mimicking materials for experimental analysis. This research will inform the development of more inclusive diagnostic technologies, ultimately improving accuracy and facilitating equitable healthcare outcomes.

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  Characterizing the Broadband Frequency Response of Pressure-Sensitive Paint

  Charles Strunc

  Pressure-Sensitive Paint (PSP) is a valuable tool for measuring pressure distributions in aerodynamic testing, but its effectiveness depends on its response time to pressure fluctuations. This research investigates the frequency response of PSP using a custom-built resonance tube designed to generate controlled pressure oscillations across a wide frequency range. The tube exploits the resonant properties of an air column to amplify pressure fluctuations produced by a speaker system, theoretically enabling precise characterization of PSP behavior at frequencies from 100 Hz up to 60 kHz. PSP pressure readings are compared to a high-precision transducer to quantify phase lag and signal attenuation, providing insight into the operational limits of different PSP formulations. The goal of extending frequency response characterization beyond the typical 10 kHz threshold offers a more comprehensive understanding of PSP performance at high frequencies. The resonance tube developed in this work establishes a permanent experimental setup for future PSP testing and optimization, supporting advancements in high-speed aerodynamic pressure measurements.

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  Chronic health conditions in relation to student belonging

  Sophia Mayer

  The relationship between chronic health conditions on college campuses and student belonging is uncertain. This population of students remains underrecognized and understudied. This study addressed this relationship via an online, anonymous thirteen-question survey. What was discovered was that academic support should be approached differently and/or changed to better support students with chronic conditions. The findings reflect gaps in the campus’ current academic support for students who choose to use academic resources. Low levels of belonging were found within the results. This could be improved upon through developing and restructuring the current systems and programs.

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  ClarityMD

  Vinit Jain, Pratham Yadav

  Managing multiple chronic conditions often requires patients to consult different specialists, leading to fragmented care where critical health information may not be effectively communicated between providers. This communication gap poses significant risks, as treatments prescribed by one physician might inadvertently conflict with a patient’s coexisting conditions or medications. To address this challenge, we present a patient-centric digital platform designed to streamline communication between patients and healthcare providers. The application leverages artificial intelligence (AI) to analyze uploaded medical records, identify potential conflicts (e.g., drug interactions, contradictory therapies), and generate personalized checklists of topics for patients to discuss during clinical visits. By automating the synthesis of complex medical data, the tool reduces reliance on error-prone manual note-taking, ensuring patients and doctors prioritize critical health concerns. Feedback from interviews with 4 physicians, highlighted widespread recognition of this issue. Clinicians emphasized that inconsistent information sharing between specialists and patients often complicates care coordination, and they endorsed the application’s potential to bridge these gaps. Doctors noted that AI-generated checklists could standardize patient-provider communication, reducing oversights during consultations and mitigating risks of conflicting treatments.The platform’s second phase introduces an AI-driven visualization engine that dynamically selects optimal data representations (e.g., graphs, timelines) based on the patient’s medical history and current health metrics. This feature aims to minimize cognitive overload by presenting information in formats tailored to enhance comprehension for both patients and providers, allowing more time to focus on treatment plans. Our research underscores the transformative potential of AI in addressing systemic communication challenges in multi-specialty care. By integrating predictive analytics with clinician-informed design, the platform enhances patient safety and fosters collaborative decision-making. Future work will explore scalability across healthcare systems and the impact of adaptive visualizations on treatment adherence. This dual-phase approach positions technology as a catalyst for cohesive, efficient, and patient-centered healthcare ecosystems.

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  Climate Change Impacts on The Central American Lenca Tribe

  Yeimi Bartolon Perez, Joseph Guagenti, Norah Hess, Maya Pelshaw

  The Lenca people are the largest indigenous population in Honduras, with around two thousand villages and around 116,000 people; they also represent a large population in eastern El Salvador of around 37,000 people. They inhabit remote, mountainous regions with limited access to infrastructure and economic opportunities. Their livelihoods primarily depend on agriculture, weaving, and pottery. Recent developments, such as hydroelectric projects and mining operations, have infringed upon their ancestral lands, leading to significant environmental and cultural challenges. For the residents of a northern village, Guapinol, their problems began in 2014, when the Honduran government granted a mining concession with the Carlos Escaleras National Park (Global Witness). Consequences such as unreliable source of drinking water due to the mine, intimidation, and arrests for those who dared to defend their environment against the authorities began to rise. Looking towards the future, the Lenca people have resolutely decided that there is no other option but to fight for their rights and the protection of their land, a movement that can be seen to this day.

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  Clinical Translation of Virtual Reality Motion Capture for Upper Extremity Therapy Using Machine Learning

  Skyler Barclay

  Virtual reality (VR) has become popular in research due to its ability to present clear and customizable tasks. Infrared (IR) motion capture allows for the collection of full kinematic data, however the cost may not be feasible for most clinics. Vive trackers allow for integrated wearables and VR therapy at a lower cost. Our current VR motion capture system requires segment definitions from the IR motion capture system in order to build an accurate skeletal model. The aim for this study is to output kinematics from the raw VR motion capture data using a Bidirectional Long-Short-Term-Memory (BLSTM) algorithm trained with joint kinematics calculated from the IR motion capture system.IR and VR motion capture of the upper extremity was collected simultaneously, in Nexus and Brekel respectively, while participants played customized levels in Beat Saber. The participants were instructed to slice through the virtual blocks with a saber in the directed position, orientation, and correct arm. To determine if shoulder, elbow, and wrist joint kinematics can be predicted using raw VR motion capture data a person specific BLSTM algorithm (n=3) was trained in Python on IR joint kinematics from the first visit (lookback = 100) and tested on the participants’ second visit data.The BLSTM results found an average error of ±10° for the joints. Collecting known joint angle poses, filtering the input data, and fine tuning the algorithm hyperparameters should decrease the error further. This means one baseline IR capture could make it possible for clinics to predict upper extremity joint kinematics of a patient during these customizable Beat Saber therapy games, and possibly other motions, with only the VR equipment, Brekel, and Python. Additionally, the use of VR therapy allows for individualized and fun therapies with quantitative results to track progress.

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  Collecting road conditions with virtual travel

  Shani Patel

  The primary objective of this research project is to develop an automated system for collecting road condition images through virtual travel, eliminating the need for vehicle-mounted dashcams. Instead of manual driving, our approach leverages Google Street View within Google Maps to gather visual data. An automated program running on a virtual machine systematically navigates designated routes, retrieving high-resolution images. This method offers key advantages: a virtual machine operates continuously without fatigue, ensuring efficiency and consistency. By automating data collection, we enhance the accuracy and timeliness of road condition assessments, benefiting transportation agencies, urban planners, and researchers focused on infrastructure maintenance and development.