Browsing by Author "Ham, Youngjib"

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Collaborative Research: Improving Undergraduate Education in Civil & Building Engineering through Student-centric Cyber-Physical Systems and Real-world Problems
Construction Science; https://hdl.handle.net/20.500.14641/233; National Science Foundation
This project aims to serve the national interest in high quality undergraduate engineering education. It will do so by integrating smartphone-based digital visualization technology into Civil and Building Engineering curricula. The project predicts that this addition will improve student engagement in the engineering classroom. Education research shows that active learning increases student motivation and learning . However, active learning experiences are challenging to incorporate into civil and building engineering, because these fields address problems that are large-scale or not available locally. Additionally, students often view civil and building engineering negatively as "low-tech", making it more challenging to interest potential majors. This project proposes a novel approach to increase enrollment, retention, and success in civil and building engineering degree programs: the use of low-cost technologies to enhance the student-learning experience. Specifically, real-world problems in civil and building engineering will be brought into undergraduate classrooms through interactive digital visualizations of civil and building engineering systems. Students will be able to control the systems through affordable sensing technologies, such as smartphones, cameras, and Internet of Things sensors. The resulting educational tools, which the project calls student-centric cyber-physical systems, are expected to promote active learning as students interact with these systems in real-time. These tools will also introduce students to innovative sensors for smart infrastructure, which may stimulate additional interest and increase student recruitment and retention. Through design-based implementation research, this exploratory project will develop student-centric cyber-physical systems for building science and structural analysis, and address the following questions: 1) How do students' interest, motivation, and engagement vary? 2) How does student-centric cyber-physical systems affect student ability to sense and predict solutions to problems? 3) How does exposure to sensing technology and data science aspects of student-centric cyber-physical systems affect students? perceptions of civil and building engineering, particularly the low/high tech nature of the field? The developed student-centric cyber-physical systems tools will be tested and assessed at two large public research universities. The project plans to generate theory to guide the transition for next-generation student-centric cyber-physical systems tools across multiple STEM fields. Lessons learned in the context of civil and building engineering can be shared with other fields, potentially leading to a broad national impact. The student-centric cyber-physical systems tools will also be used in outreach efforts at K-12 public schools. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.
I-Corps: Artificial Intelligence-Driven Disaster Risk Prediction and Assessment
Construction Science; TAMU; https://hdl.handle.net/20.500.14641/233; National Science Foundation
The broader impact/commercial potential of this I-Corps project is the development of a rapid and automated scene understanding technology to intelligently evaluate existing conditions of vulnerability and assess potential disaster risks more proactively and effectively. This technology may help make informed decisions regarding steps to reduce the impacts of disasters. Developing and implementing the technology to reduce disaster risk requires identifying and assessing the sources of potential risk effectively, but a lack of a rapid and automated tools makes practitioners rely on manual inspection. The proposed innovation is to make the current disaster risk assessment process more intelligent and efficient by reducing time-consuming and labor-intensive manual inspection. If the project is completed successfully, the proposed innovation will enhance scientific and technological understanding of rapid and automated visual sensing and analytics for disaster risk prediction and assessment. This I-Corps project is based on the development of the integrated analysis of low-level image features together with high-level semantic models, which enables robust scene understanding and risk prediction in complex environments. By leveraging large-scale data from multimodal visual sensors (e.g., drones, security cameras, etc.), the technology automatically encodes the context of potential disaster risk into machine vision algorithms to identify the elements at risk on video recordings and assess the degree of vulnerability and the elements at risk in complex environments. The system generates site-specific managerial information in an automated manner, which may enhance risk-informed decision-making for disaster mitigation and preparedness, thereby reducing both the level of risk and the impacts of disasters effectively. The proposed innovation may also provide the foundation for further vision-based reasoning for disaster management. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
I-Corps: Artificial Intelligence-Driven Disaster Risk Prediction and Assessment
Construction Science; TAMU; https://hdl.handle.net/20.500.14641/233; National Science Foundation
The broader impact/commercial potential of this I-Corps project is the development of a rapid and automated scene understanding technology to intelligently evaluate existing conditions of vulnerability and assess potential disaster risks more proactively and effectively. This technology may help make informed decisions regarding steps to reduce the impacts of disasters. Developing and implementing the technology to reduce disaster risk requires identifying and assessing the sources of potential risk effectively, but a lack of a rapid and automated tools makes practitioners rely on manual inspection. The proposed innovation is to make the current disaster risk assessment process more intelligent and efficient by reducing time-consuming and labor-intensive manual inspection. If the project is completed successfully, the proposed innovation will enhance scientific and technological understanding of rapid and automated visual sensing and analytics for disaster risk prediction and assessment. This I-Corps project is based on the development of the integrated analysis of low-level image features together with high-level semantic models, which enables robust scene understanding and risk prediction in complex environments. By leveraging large-scale data from multimodal visual sensors (e.g., drones, security cameras, etc.), the technology automatically encodes the context of potential disaster risk into machine vision algorithms to identify the elements at risk on video recordings and assess the degree of vulnerability and the elements at risk in complex environments. The system generates site-specific managerial information in an automated manner, which may enhance risk-informed decision-making for disaster mitigation and preparedness, thereby reducing both the level of risk and the impacts of disasters effectively. The proposed innovation may also provide the foundation for further vision-based reasoning for disaster management. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria
Uncovering Potential Risks of Wind-induced Cascading Damages to Construction Projects and Neighboring Communities
Construction Science; TAMU; https://hdl.handle.net/20.500.14641/233; National Science Foundation
Unstructured construction sites, including incomplete structures and unsecured resources (e.g., materials, equipment, and temporary facilities), are among the most vulnerable environments to windstorms such as hurricanes. Wind-induced damages to construction sites cause substantial losses, disruption, and considerable schedule delays, and thus negatively impact the efficiency of construction projects. Moreover, wind-induced damage caused by equipment, materials or structural elements, for example, originating from construction sites negatively affect neighboring communities, triggering structural damage, serious injuries, and casualties, as well as economic losses. This project will create and validate a new streamlined Imaging-to-Simulation framework to prevent wind hazard events from causing catastrophic damage to construction projects and neighboring communities. This project will benefit our society as it will significantly enhance current windstorm preparedness and mitigation plans, which ultimately promote public safety, property loss reduction, insurance cost reduction, and induce a culture of preparedness for disasters. This multidisciplinary research will help broaden participation of a new generation of young people in the Science, Technology, Engineering and Math (STEM) fields through integrated research and pedagogical activities. Using knowledge on potential at-risk construction resources obtained through experimental testing of extreme wind events, this project will partially or fully automatically model the current state of construction sites through machine vision techniques using multimodal visual data obtained from construction workers and camera-equipped unmanned aerial vehicles. To perform multi-physics simulation of multiple discrete objects in unstructured construction sites, an impulse-based discrete element method will be conceptualized. This method explicitly accounts for impulse-based dynamics by realizing efficient computing, which enable balancing between significant speed-up and reasonable simulation fidelity. The approach can describe the collective motion of mutually interacting components over time. Component-based vulnerability and impact analysis with 3D Building or Civil Information Models (BIM/CIMs) will then be conducted to generate fundamental and highly specific knowledge on wind-induced damage mechanisms. Finally, the entire system will be validated in real-world construction projects and within a 12-fan Wall of Wind facility that can generate up to hurricane category 5 wind speeds.