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Browsing by Author "van de Lindt, John W., committee member"

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    A streamlined bridge inspection framework utilizing unmanned aerial vehicles (UAVs)
    (Colorado State University. Libraries, 2019) Perry, Brandon J., author; Guo, Yanlin, advisor; Atadero, Rebecca, committee member; van de Lindt, John W., committee member; Beveridge, Ross, committee member
    The lack of quantitative measures and location information for instances of damage results in human-based bridge inspections that are variable and subjective in nature. With bridge owners and managers tasked with making major maintenance/repair decisions with inadequate funding and resources, it is appealing to develop a transparent bridge inspection and evaluation system that integrates field inspection and documentation of damage with quantitative measures and geo-referenced locations in a holistic process. A new, streamlined bridge inspection framework based on unmanned aerial vehicles (UAVs) is proposed to improve the efficiency, cost-effectiveness, and objectivity of these inspections while enhancing the safety of inspectors. Since the current bridge inspection practices use a component based structural rating system, the new UAV-based bridge inspection system should also follow a component-wise damage evaluation system to enable the seamless adoption of this new technology into practice. To provide bridge managers/owners with the streamlined decision-making support, this new system uniquely integrates UAV-based field inspection, automated damage/defect identification, and establishment of an element-wise As-Built Building Information Model (AB-BIM) for the damage documentation in a holistic manner. In this framework, a UAV platform carrying visual sensors first collects data for identifying defects (i.e. cracks, spalling and scaling of concrete). Next, an automated damage detection algorithm is developed to quickly extract quantitative damage information (i.e. type, size, amount, and location) from the data. By using UAV-enabled photogrammetry and unsupervised machine learning techniques, this system can automatically segment the bridge elements (i.e. beam, girders, deck, etc.) from a 3D point-cloud with minimal user input. In the end, the damage information is mapped to the corresponding structural components of the bridge and readily visualized in the AB-BIM. The documented element-wise damage information with quantitative measures in conjunction with the 3D visualization function in the proposed system can provide bridge managers with a transparent condition evaluation and a one-stop decision making support which can greatly ease the planning of repair/maintenance. The feasibility of this approach is demonstrated using a case study of a Colorado bridge.
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    ItemEmbargo
    Development of a prediction model for windborne debris damage assessment of coast communities under hurricanes
    (Colorado State University. Libraries, 2023) Dong, Yue, author; Guo, Yanlin, advisor; van de Lindt, John W., committee member; Ellingwood, Bruce R., committee member; Gao, Xinfeng, committee member
    Urban high-rise building envelopes may suffer severe damage induced by windborne debris during hurricanes. Breaches in urban building envelopes may lead to cascading building content loss due to rain intrusion and building functionality loss for long periods of time, thus disrupting the resilience of urban coastal communities. Such a societal disruption may become worse due to the rapid growth of population and development of economy in coastal communities. Objective of this dissertation is to develop an efficient prediction model for assessing windborne debris damage to building envelopes and apply it to the hurricane scenarios identified through a de-aggregation process to enable risk-informed resilience assessment of coastal communities for windborne debris impact. A set of scenario hurricanes corresponding to a stipulated return period (RP) for resilience assessment of coastal communities for debris impact is systematically identified using the de-aggregation approach proposed in this dissertation. The existing building envelope damage assessment models for urban high/mid-rise buildings often neglect the geometry of building clusters or simply assume a homogeneous configuration, which can introduce errors and uncertainties for urban building clusters with varying geometries and layouts. This dissertation proposes a new fragility modeling approach for urban buildings envelopes, which explicitly considers geometric configurations of urban buildings, to improve the accuracy of risk assessment for urban buildings. Estimating the urban wind field that drives the debris flight is critical for constructing fragilities for debris damage. The traditional tools for simulating urban wind fields, such as wind tunnel tests and Computational Fluid Dynamic (CFD), are usually expensive or time-consuming for complex urban environments and cannot offer an efficient prediction of the urban wind field that is sufficient for risk assessment at a community level. Therefore, an efficient machine learning (ML) based prediction model of wind fields around building clusters is proposed using conditional Generative Adversarial Networks (cGANs). Uncertainty analysis is conducted on the models and parameters involved in this procedure of debris damage assessment. The uncertainty in the final prediction of windborne debris damage is evaluated through uncertainty propagation among models and parameters. Sensitivity analysis with some critical factors is also conducted to identify the dominant contributors to the uncertainty in the estimation of debris damage. In the end, windborne debris damage on building envelopes in virtual communities under synthetic hurricanes scenarios is investigated to illustrate the damage assessment procedure for windborne debris impact at the community level.
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    ItemOpen Access
    Modeling and improving urban human mobility in disaster scenarios
    (Colorado State University. Libraries, 2020) Zou, Qiling, author; Chen, Suren, advisor; Heyliger, Paul, committee member; van de Lindt, John W., committee member; Chong, Edwin K. P., committee member
    Natural and human-made disasters, such as earthquake, tsunami, fire, and terrorist attack, can disrupt the normal daily mobility patterns, posing severe risks to human lives and resulting in tremendous economic losses. Recent disaster events show that insufficient consideration of human mobility behavior may lead to erroneous, ineffective, and costly disaster mitigation and recovery decisions for critical infrastructure, and then the same tragedies may reoccur when facing future disasters. The objective of this dissertation is to develop advanced modeling and decision-making methodologies to investigate the urban human mobility in disaster scenarios. It is expected that the proposed methodologies in this dissertation will help stakeholders and researchers gain a better understanding of emergency human behavior, evaluate the performance of disrupted infrastructure, and devise effective safety management and resilience enhancement strategies. Focusing on the two important mobility modes (i.e., walking and driving) in urban environment, this dissertation (1) develops agent-based crowd simulation models to evaluate the crowd dynamics in complex subway station environment and investigate the interplay among emotion contagion, information diffusion, decision-making process, and egress behavior under a toxic gas incident; (2) develops functionality modeling, interdependency characterization, and decision models to assess and enhance the resilience of transportation networks subject to hazards.
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    Optimal stochastic scheduling of restoration of infrastructure systems from hazards: an approximate dynamic programming approach
    (Colorado State University. Libraries, 2019) Nozhati, Saeed, author; Ellingwood, Bruce R., advisor; Mahmoud, Hussam N., advisor; Chong, Edwin K. P., committee member; van de Lindt, John W., committee member
    This dissertation introduces approximate dynamic programming (ADP) techniques to identify near-optimal recovery strategies following extreme natural hazards. The proposed techniques are intended to support policymakers, community stakeholders, and public or private entities to manage the restoration of critical infrastructure of a community following disasters. The computation of optimal scheduling schemes in this study employs the rollout algorithm, which provides an effective computational tool for optimization problems dealing with real-world large-scale networks and communities. The Markov decision process (MDP)-based optimization approach incorporates different sources of uncertainties to compute the restoration policies. The fusion of the proposed rollout method with metaheuristic algorithms and optimal learning techniques to overcome the computational intractability of large-scale, multi-state communities is probed in detail. Different risk attitudes of policymakers, which include risk-neutral and riskaverse attitudes in community recovery management, are taken into account. The context for the proposed framework is provided by objectives related to minimizing foodinsecurity issues and impacts within a small community in California following an extreme earthquake. Probabilistic food security metrics, including food availability, accessibility, and affordability, are defined and quantified to provide risk-informed decision support to policymakers in the aftermath of an extreme natural hazard. The proposed ADP-based approach then is applied to identify practical policy interventions to hasten the recovery of food systems and reduce the adverse impacts of food insecurity on a community. All proposed methods in this study are applied on a testbed community modeled after Gilroy, California, United States, which is impacted by earthquakes on the San Andreas Fault. Different infrastructure systems, along with their spatial distributions, are modeled as part of the evaluation of the restoration of food security within that community. The methods introduced are completely independent of the initial condition of a community following disasters and type of community (network) simulation. They treat the built environment like a black box, which means the simulation and consideration of any arbitrary network and/or sector of a community do not affect the applicability and quality of the framework. Therefore, the proposed methodologies are believed to be adaptable to any infrastructure systems, hazards, and policymakers' preferences.
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    Performance assessment of simple blast wall systems
    (Colorado State University. Libraries, 2019) Hussein, Assal, author; Mahmoud, Hussam N., advisor; Heyliger, Paul R., advisor; van de Lindt, John W., committee member; McLean, David I., committee member; Mueller, Jennifer L., committee member
    To view the abstract, please see the full text of the document.
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    Quantification of performance, damage, and risk to light wood frame buildings subjected to tornadoes and expansive soils
    (Colorado State University. Libraries, 2017) Maloney, Timothy D., author; Mahmoud, Hussam N., advisor; Ellingwood, Bruce R., advisor; van de Lindt, John W., committee member; Zahran, Sammy, committee member
    Each year, damage to infrastructure caused by the uncorrelated hazards of tornadoes and expansive soils is on the order of billions of dollars. The monetary losses caused by each hazard alone are reason for concern. For tornados, however, the impact can be devastating and extend beyond monetary loss. Furthermore, the presence of expansive soils can exacerbate life-safety concerns during a tornado by limiting construction of underground shelters such as basements. It is not uncommon for communities to be crippled by damage to critical infrastructure such as businesses, homes, utility networks, and emergency facilities. This destruction can limit a community's ability to support its population in the short-term which can lead to significant outmigration that may be difficulty to recover from. The ability of a community to plan for and recover from such hazards is referred to as community resilience. The major goal of this research is to contribute to the development of a set of standards and guidelines for resilient community design. Specifically, this study aims to link the performance of individual building components to building system performance, so that the effect of implementing a change in standard construction techniques (i.e. recommending that homes be constructed with hurricane clips) can be quantified. The work herein focuses on light wood frame residential buildings constructed with methods typical in the American heartland. The research approach taken herein was to develop detailed finite element (FE) models to capture building system performance and individual building component behavior under expansive soil and tornado loading. The level of detail used in the FE models allows the interaction between building components to be captured to a higher degree than previously possible. Knowledge of the demand on building components gained from the FE analysis was then applied to perform statistical analysis to quantify the performance of several building archetypes chosen to represent the residential building portfolio in a typical community located in the US heartland. The performance of the typical archetypes was then analyzed to identify deficient building components and compared to target resilience performance levels provided by research partners at the University of Oklahoma. The effect of implementing various improved construction techniques was then examined in an effort to meet the resilience performance targets. This study revealed that, typically, light wood frame residential construction that is common in tornado prone areas of the U.S. is not sufficient to meet the resiliency goals considered in this study. This is unsurprising considering the historical lack of consideration given to tornado hazards in U.S. design codes and standards. Similarly, it was found that typical masonry block basement wall construction was insufficient to withstand loading from expansive soils without sustaining damage. This is also not surprising because many people in expansive soil prone areas choose to forgo constructing basements due to the likelihood of damage. The study also revealed, however, that resilience target performance levels can be achieved using existing construction techniques. This suggests that resilient community design is a goal that is already within reach at the current state of the art.
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    Reliability-based safety evaluation of traffic on rural highway
    (Colorado State University. Libraries, 2011) Chen, Feng, author; Chen, Suren, advisor; van de Lindt, John W., committee member; Bienkiewicz, Bogusz J., committee member; Sakurai, Hiroshi, committee member
    In the United States as well as other developed countries, road accidents are causing more injuries and casualties than any other natural or man-made hazard. Some vehicles, such as trucks, emergency vehicles and SUVs, often experience increasing risks of single-vehicle accidents under hazardous driving conditions, such as inclement weather and/or complicated topographical conditions. The objective of this research is to establish a reliability-based framework to evaluate the traffic safety through taking account of more realistic adverse driving conditions, such as wind gust, snow-covered or icy road surface, and/or curving. After some background information is introduced in Chapter 1, Chapter 2 covers the development of a mobile mapping technology aiming at collecting site-specific as well as vehicle-specific wind velocity data for traffic safety evaluations. In Chapter 3, an advanced simulation-based single-vehicle accident assessment model considering the coupling effects between vehicles and hazardous driving conditions is developed. In Chapter 4, ten-year accident data involving trucks on rural highway from the Highway Safety Information System (HSIS) is studied to investigate the injury severity of truck drivers by using mixed logit models. Based on the advanced transient dynamic vehicle simulation model, the general framework of a reliability-based assessment model of vehicle safety under adverse driving conditions is finally developed in Chapter 5. In Chapter 6, a case study of I-70 in Colorado to evaluate the traffic safety and operational performance of large trucks is conducted. The integrate study includes individual vehicles for single-vehicle accident risk assessment and the whole traffic on the highway for multi-vehicle accident risk assessment and operational performance evaluation. Finally, conclusions are summarized in Chapter 7.