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Browsing by Author "Guo, Yanlin, committee member"

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    Fully integrated network of networks
    (Colorado State University. Libraries, 2022) LaMar, Suzanna, author; Jayasumana, Anura, advisor; Cale, Jim, committee member; Guo, Yanlin, committee member; Simske, Steve, committee member
    There are many different facets to developing a fully integrated network of networks system that can facilitate seamless information exchange between nodes within a complex network topology. As an example, individual link resiliency, enhanced waveform capabilities, spectral and spatial diversity are all critical features in providing communications that can enable connectivity and interoperability for a fully networked system extending into multiple domains (ground, surface, air, and space). Steps taken toward achieving such an architecture are introduced with emerging millimeter wave (mmW) and high-band antenna technologies that can be integrated with future tactical multifunction software defined radios (SDRs) to enable information distribution between vital networked participants, including 5th generation aircraft. Small, lightweight mmW and high-band antenna designs that will enable small unit tactical operations to persist under electronic warfare conditions will be discussed. These small units are typically fielded with multiple communications radios but are limited in function and do not enable rapid communication on the move, or high-capacity data transfers at the halt. Additionally, a revolutionary cognitive antenna (CA) is introduced where artificial intelligence (AI) techniques are proposed to aid in improving antenna functions, support self-healing attributes, and promote autonomous communication operations. A CA designed for future spacecraft (S/C) communications systems that is environmentally perceptive will be presented where it can sense and transmit radio frequency (RF) signals and cooperate with a cognitive radio (CR) to modify waveform and beam pattern characteristics for enhanced resiliency and communications. As an extrapolation to interoperability and information exchanges, data must be always secured. Common communications payload security architectures are presented as a basis for offering data protection to not only the system itself, but also to networks that are part of the larger enterprise solution. Similarly, machine learning methods are proposed to combat malicious cyber-attacks within an enterprise security space-based communications architecture to offer a more resilient, protective adaptive framework. Additionally, the machine learning algorithms seek to provide a viable solution for identifying, classifying, and detecting possible intrusions in a highly dynamic environment. Machine learning is also applied to networking strategies to predict congestion before it happens; thereby, preventing bottlenecks within the network. This is especially important for critical, high-value information. A CONgestion Aware Intent-based Routing (CONAIR) architecture that facilitates faster and more reliable data exchanges between end users is proposed. The CONAIR architecture leverages platform and mission information to derive quality of service (QoS) metrics that can be used to support network route optimizations by using a network controller (NC) with machine learning to predict future network behaviors. Finally, the CA, multifunction SDRs and NC subsystems are integrated into a robust architecture on unmanned aerial vehicles (UAVs) to form collaborative cognitive communications systems that are responsive to stressing operating conditions. Through collaborative behaviors and interactions, communications can be optimized. These discriminating technologies support the continued ambition for maturating military communications systems to benefit cooperative interactions and information exchanges between various users in multi-hop, complex networks.
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    High-resolution multi-hazard approach to quantify hurricane-induced risk for coastal and inland communities
    (Colorado State University. Libraries, 2021) Nofal, Omar M., author; van de Lindt, John W., advisor; Cutler, Harvey, committee member; Mahmoud, Hussam N., committee member; Guo, Yanlin, committee member
    Hurricanes are devastating natural hazards that often cause damage to coastal and in-land communities as a result of their loadings which include storm surge, waves, wind, and rainfall and riverine flooding, often in combination. Modeling these hazards individually and their effects on buildings is a complex process in that each loading component within the hazard behaves differently affecting either the building envelope, the structural system, or the interior contents. For coastal communities, realistic modeling of hurricane effects requires a multi-hazard approach that considers the combined effects of wind, surge, and waves. Previous studies have focused primarily on modeling these hazards individually with less focus on the multi-hazard impact on the whole building system made up of the combination of structure and its interior contents. For inland communities, high-resolution hydrologic and hydrodynamic models are required to develop high-fidelity flood hazard maps that account for the different hazard characteristics (e.g., flood depth, velocity, duration, etc.). The current flood damage assessment standards are still using stage-damage functions to account for flood damage to buildings. These functions include inherent uncertainties in the damage assessment with significant limitations on their applications. Additionally, the analysis resolution used in these previous studies did not allow hurricane risk assessment through at the building component level (e.g., interior content, structural, and non-structural components). To address these research gaps, a high-resolution flood risk model was developed for inland communities using robust probabilistic flood fragility functions developed for a portfolio of 15 building archetypes that can model the flood vulnerability at the community-level. For coastal communities, a regional-level multi-hazard hurricane risk analysis methodology is proposed to account for the combined impacts of wind-surge-wave loadings driven by hurricanes for both the building system and its interior contents. Fragility functions are used to describe building vulnerability to the multiple loadings driven by hurricanes, and a new convolutional vulnerability approach was developed to combine wind and wave/surge fragilities. The models developed in this dissertation were included in an open-source Interdependent Networked Community Resilience Modeling Environment (IN-CORE) to allow researchers/users to systematically use these models in different types of engineering, social, and economic analyses. The analysis resolution used in the hazard, exposure, and vulnerability models allowed investigation of different levels of mitigation measures including component-, building-, and community-level mitigation strategies. The proposed hurricane risk models for coastal and in-land communities were then applied to a number of case studies to demonstrate the ability of the developed methods to predict damage at the building level across a large spatial domain of small and large communities. The main contribution of these efforts is the development of generalized fragility-based flood vulnerability functions that were applied to a suit of building archetypes and are extendable to be used for other buildings/facilities. These fragilities were then combined with another suite of existing wind fragilities and other storm surge-wave fragility functions to account for the impact of the hurricane-induced hazards on coastal communities. These models enable a better understanding of the damages caused by hurricanes for coastal and in-land communities, thereby setting initial post-impact conditions for community resilience assessment and investigation of recovery policy alternatives.
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    Physical-socio-economic systems integration for community resilience-informed decision-making and policy selection
    (Colorado State University. Libraries, 2022) Wang, Wanting, author; van de Lindt, John W., advisor; Mahmoud, Hussam, committee member; Guo, Yanlin, committee member; Cutler, Harvey, committee member
    Natural hazards are damaging communities with cascading catastrophic economic and social consequences at an increasing rate due to climate change and land use policies. Comprehensive community resilience assessment and improvement requires the analyst to develop a model of interacting physical infrastructure systems with socio-economic systems to measure outcomes that result from specific decisions (policies) made. There is limited research in this area currently because of the complexity associated with combining physics-based and data-driven socio-economic models. This dissertation proposes a series of multi-disciplinary community resilience assessment models (e.g., multi-disciplinary disruption assessment and multi-disciplinary recovery assessment) subjected to an illustrative natural hazard across physical infrastructure and socio-economic systems. As illustrative examples, all the proposed methodologies were applied to the Joplin, Missouri, testbed subjected to tornado hazard but are generalizable. The goal is to enable community leaders and stakeholders to better understand the community-wide impacts of a scenario beyond physical damage and further empower them to develop and support short-term and long-term policies and strategies that improve community resilience prior to events. Advancements in multi-disciplinary community resilience modeling can help accelerate the development of building codes and standards to meet the requirements of community-wide resilience goals of the broader built environment, consistent with the performance objectives of individual buildings throughout their service lives.
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    Resilience-based seismic design based on time-to-functionality for tall mass timber buildings
    (Colorado State University. Libraries, 2023) Furley, Jace, author; van de Lindt, John, advisor; Arneson, Erin, committee member; Guo, Yanlin, committee member; Mahmoud, Hussam, committee member
    Mass timber has existed for years as a structural material; however, only in the last decade or so has progress been made in North America on the adoption of mass timber for moderate to high seismic regions. During this time, there has been significant research effort and resources allocated to demonstrating various mass timber products as suitable for seismic applications, in particular as seismic force resisting systems (SFRS). However, during the research process, the potential suitability of mass timber for mid-rise or tall buildings was identified, and research efforts into the applicability of mass timber for taller buildings in seismic regions have been increasing in the past several years. Along with the growing interest in mass timber for tall buildings, a larger more general push for resilient buildings and communities has also been prevalent, providing the opportunity to design mass timber SFRS for tall buildings that not only meet current performance standards, but also have the potential to contribute to resilience-based design and ultimately community resilience. This research presented in this dissertation develops and applies the time-to-functionality fragility (TTF) methodology to provide resilience-based design guidance for tall mass timber buildings. The new TTF methodology incorporates many of the considerations of previous performance-based methodologies (such as FEMA P-58) and resilience methods (such as the REDi rating system) into a multi-layer direct Monte Carlo simulation to estimate various recovery levels. This method was then applied to a two-story test specimen utilizing a new mass timber SFRS (a cross laminated timber [CLT] rocking wall), developed as a part of the Natural Hazards Equipment Research Infrastructure (NHERI) TallWood project, to demonstrate the resilience capabilities of the system. While the CLT rocking wall SFRS demonstrated excellent resilience capabilities, a dearth of data in mass timber (in terms of resilience considerations) were identified both as a part of the TTF methodology development and as a part of NHERI TallWood. To address some this lack of data, nail laminated timber (NLT) and dowel laminated timber (DLT) diaphragms were tested using quasi-static reversed cyclic loading, determining the lateral capacity of these systems as well as identifying damage states to better incorporate them into the TTF methodology. With the resilience of the CLT rocking wall system demonstrated, and several of the identified research data gaps addressed, the TTF methodology was applied to the two-story, six-story, and ten-story archetypes utilizing the CLT rocking wall system and varying the different structural components to create a database of TTF performance. A total of 243 SFRS designs were considered, and this database was leveraged using the developed resilience-based design guidance to estimate the TTF performance of two ten-story design examples. The research presented here demonstrates that it is possible to design tall mass timber buildings with resilience considerations, and that there are mass timber SFRS suitable for resilient design. While the findings focus on mass timber, the methodology itself is not limited to mass timber. The design guidance presented herein represents the first step towards a more prescriptive solution for TTF performance, with the potential for the incorporation of other structural systems and materials beyond the CLT rocking wall. In addition, there is a significant push to codify functionality, often termed "functional recovery", into U.S. design codes in the next 10 years. The TTF methodology directly considers functionality as a part of the method and this research and research like it will provide the foundation for the codification effort.
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    Visible & thermal imaging and deep learning based approach for automated robust detection of potholes to prioritize highway maintenance
    (Colorado State University. Libraries, 2023) Chen, Wei-Hsiang, author; Jia, Gaofeng, advisor; Guo, Yanlin, committee member; Chen, Haonan, committee member
    Potholes are a primary pavement distress that can compromise safety and cause expensive damage claims. Potholes are results of deterioration of pavements due to aging, weather and traffic overloads and are common problems across the U.S. Potholes are even more common in the Mountain Plains region due to the snow and freeze/thaw effect. Identifying and repairing potholes is one critical aspect of highway maintenance. Accurate, robust and fast detection of potholes is critical to enabling timely and cost-effective pavement maintenance. Recently, there has been growing interest and research in using machine learning techniques for pothole detection using different views of visible images. However, quality of potholes detection using only visible images may be significantly compromised due to poor lighting, weather conditions, low contract to surrounding pavement. On the other hand, thermal images are more robust to lighting and weather conditions. Although thermal images may lack the texture details of visible images, they can offer additional unique features compared to visible images, e.g., temperature difference between pothole and surrounding pavement, which can be potentially used for pothole detection. However, so far, the great potential and effectiveness of integrating both visible and thermal images as well as using fused images to enable accurate and robust pothole detection have not been investigated. This research aims to develop an automated deep learning based pothole detection and mapping tool for highway maintenance using the fusion of visible and thermal images. First, a unique and valuable database of geotagged and labeled trios of visible, thermal and fused images is established for training pothole detection algorithms. This is done through collecting pothole images using a low-cost FLIR ONE thermal camera connected to a smart phone. These data are used to train the machine learning algorithms for pothole detection. To establish an accurate pothole detection algorithm, we proposed and compared the performances of three machine learning algorithms, i.e., Anisotropic Diffusion Fusion (ADF) + Mask R-CNN, RTFNet, and RTFNet with Enhancement Parameters (EPs). These algorithms differ in how the visible and thermal images are fused and used for pothole detection. We achieved the best F1-score of 93.7% in the daytime scenario by the RTFNet method and 90.9% in the nighttime scenario by the RTFNet with EPs method. To best leverage the information from the thermal images, in the end we developed a Bright-Dark detector to determine the lighting conditions of candidate testing images, and then feed the images to the respective algorithms for pothole detection. For images with potholes detected, we also developed a mapping tool to map the location of the pothole using GPS information of the images. In the end, the trained overall algorithm is packaged as a tool with graphical user interface (GUI) to facilitate its adoption by highway maintenance team. As more images are collected, the overall algorithm can be continuously improved to further increase the pothole detection accuracy.