Browsing by Author "Ham, Youngjib"

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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.