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Climate change is exerting a profound influence on natural hazards, resulting in increased frequency, intensity, and altered patterns of extreme weather events. These changes pose significant risks to vulnerable populations worldwide. Consequently, it is imperative to adopt hazard mitigation policies to address the impacts of climate change on natural hazards and communities. The adoption of such policies is a complex and dynamic process that requires a thorough understanding of the key factors influencing policy adoption. The United States has experienced a rise in the severity and frequency of floods, necessitating the implementation of comprehensive flood mitigation policies. These policies aim to protect vulnerable communities, safeguard critical infrastructure, and reduce the economic and human costs associated with these natural disasters. Among the various flood mitigation strategies, floodplain property buyout programs have garnered attention. However, there is limited research that examines the factors influencing the adoption of buyout programs at the local government level from a government perspective. This dissertation provides a comprehensive analysis of the adoption process of floodplain property buyout programs at the local level in the United States. The study employs a mixed methods approach to examine the mechanism behind policy adoption and identify the key factors that influence this process. Chapter 1 lays the foundation for the research by defining relevant terms and outlining the characteristics of floodplain property buyout programs in the U.S. Chapter 2 presents a theoretical framework that enhances our understanding of hazard mitigation policy adoption at the local level. The framework is exemplified through case studies of property buyout programs in North Carolina and New Jersey. The case studies conducted in these states offer compelling evidence that supports the proposed framework, which encompasses five-factor categories: hazard problem, social context, institutional capacity, cross-sector collaboration, and policy diffusion. Notably, institutional capacity plays a crucial role in buyout adoption, encompassing individual, organizational, and system capacity. These factors influence the uptake of buyouts and contribute to their success or failure. This exercise gives us valuable insights into the buyout decision making process and suggests avenues for research in the subsequent chapters. Chapter 3 conducts a quantitative analysis to validate the hazard mitigation policy adoption framework. Specifically, it focuses on investigating the factors that influence the adoption of Federal Emergency Management Agency (FEMA) property buyout programs by local governments in Virginia counties. Utilizing logistic regression models and a survey dataset collected from local floodplain managers in the Commonwealth of Virginia, the study reveals that floodplain managers' perception of repetitive flood loss and economic spillovers in neighboring areas significantly impact the adoption of buyout programs. In Chapter 4, we conduct a qualitative approach to delve into the decision-making dynamics in the adoption of floodplain property buyout programs from a government perspective in Virginia. Through semi-structured interviews with 12 experts representing various stakeholders involved in floodplain management, this study demonstrates the variations in the adoption processes among different local governments. The findings underscore the importance of leadership, community population size, floodplain managers' perception of repetitive flood loss, organizational staff capacity, and tax revenue considerations in shaping buyout decisions. It highlights the need for local leadership commitment, empowerment of floodplain managers, and comprehensive approaches to address challenges faced by small communities. The research provides practical guidance to enhance flood risk management practices and promote resilient and sustainable communities. In conclusion, this dissertation contributes to the understanding of hazard mitigation policy adoption at the local level by proposing a theoretical framework and providing empirical evidence through case studies, surveys, and interviews. The findings emphasize the importance of various factors, such as hazard problem, social context, institutional capacity, and policy diffusion, in shaping buyout policy adoption. The implications of this research extend to policymakers, practitioners, and researchers, providing insights into the motivations, obstacles, and strategies surrounding the adoption and implementation of hazard mitigation policies. By considering these factors and employing comprehensive approaches, communities can enhance their resilience and effectively mitigate the impacts of natural hazards. / Doctor of Philosophy / Climate change is causing significant changes in natural hazards, leading to more frequent and intense extreme weather events. These changes pose risks to vulnerable populations worldwide. To address these risks, it is crucial to adopt policies that mitigate the impacts of climate change on natural hazards and communities. This dissertation focuses on the adoption of such policies at the local level in the United States. The study examines the factors that influence the adoption of floodplain property buyout programs, which aim to protect communities and critical infrastructure from the impacts of floods. The research employs a combination of quantitative and qualitative methods to understand the adoption process and identify key factors that shape policy decisions. By studying case studies in North Carolina, New Jersey, and Virginia, the research provides insights into the motivations and obstacles surrounding the adoption of hazard mitigation policies. The findings emphasize the need for strong local leadership, considering community population size, addressing flood loss perceptions, building organizational capacity, and considering tax revenue implications. The research offers practical guidance for policymakers, practitioners, and researchers in enhancing flood risk management practices and promoting resilient and sustainable communities. By addressing the identified factors and adopting comprehensive approaches, communities can improve their resilience to natural hazards. The implications extend to policymakers, practitioners, and researchers, providing valuable insights into the adoption of hazard mitigation policies.

<p>The U.S. construction sector ranks second in fatal occupational injuries in 2021 among other sectors. Although many research efforts have been conducted for decades to improve safety at construction jobsites, fatal occupational injuries did not reduce to the desired level. Specifically, previous studies argued that still more than 70% of hazards often remained unrecognized by construction workers even after receiving safety training. In addition to the enforced safety regulations, the Organizational Safety and Health Administrator (OSHA) has mandated safety training for construction workers to train them regarding potential hazards and risks at jobsites while mainly focusing on a general overview of the hazards and preventive measures.</p> <p>However, in the last decade, it was extensively argued that workers’ low performance in hazard identification may not only be related to their hazard knowledge and more related to the cognitive processing of information to identify and perceive the cues in a construction environment to remain situationally aware (i.e., cognitive failures). Therefore, there is a critical need to identify a new approach for customizing training construction workers to address the lack of knowledge and cognitive failures that workers may experience. Thus, this thesis aimed to develop multi-modal personalized safety training to reduce human errors and construction workers' unsafe behaviors by improving their hazard identification abilities.</p> <p>To do so, workers’ hazard identification skills were assessed through subjective and objective non-invasive psychophysiological metrics (e.g., visual attention, emotional responses) in an immersive 360° virtual environment and customized training for them. The effectiveness of the developed personalized training was tested and validated, and the findings indicate considerable improvements in subjects’ hazard identification performance after receiving this customized training.</p> <p>This thesis contributed to the body of knowledge and practice by proposing an advanced personalized safety training framework that automatically translates workers' subjective test results and objective psychophysiological responses into customized training recommendations. The outcomes lay the necessary foundations for building tailored training regimens to improve construction worker safety using comprehensive cognitive analysis and effective intervention strategies. The developed personalized safety training will not only improve workers' hazard identification performance but will also save construction companies time delays and cost overruns by eliminating the need for a repetitive retraining of the workforce.</p>

The design earthquake selection problem is fundamentally probabilistic. Disaggregation of a probabilistic model of the seismic hazard offers a rational and objective approach that can identify the most likely earthquake scenario(s) contributing to hazard. An ensemble of time series can be selected on the basis of the modal earthquakes derived from the disaggregation. This gives a useful time-domain realization of the seismic hazard, to the extent that a single motion parameter captures the important time-domain characteristics. A possible limitation to this approach arises because most currently available motion prediction models for peak ground motion or oscillator response are essentially independent of duration, and modal events derived using the peak motions for the analysis may not represent the optimal characterization of the hazard. The elastic input energy spectrum is an alternative to the elastic response spectrum for these types of analyses. The input energy combines the elements of amplitude and duration into a single parameter description of the ground motion that can be readily incorporated into standard probabilistic seismic hazard analysis methodology. This use of the elastic input energy spectrum is examined. Regression analysis is performed using strong motion data from Western North America and consistent data processing procedures for both the absolute input energy equivalent velocity, (Vea), and the elastic pseudo-relative velocity response (PSV) in the frequency range 0.5 to 10 Hz. The results show that the two parameters can be successfully fit with identical functional forms. The dependence of Vea and PSV upon (NEHRP) site classification is virtually identical. The variance of Vea is uniformly less than that of PSV, indicating that Vea can be predicted with slightly less uncertainty as a function of magnitude, distance and site classification. The effects of site class are important at frequencies less than a few Hertz. The regression modeling does not resolve significant effects due to site class at frequencies greater than approximately 5 Hz. Disaggregation of general seismic hazard models using Vea indicates that the modal magnitudes for the higher frequency oscillators tend to be larger, and vary less with oscillator frequency, than those derived using PSV. Insofar as the elastic input energy may be a better parameter for quantifying the damage potential of ground motion, its use in probabilistic seismic hazard analysis could provide an improved means for selecting earthquake scenarios and establishing design earthquakes for many types of engineering analyses. / Ph. D.

Estimating local site effects and modifying the uncertainty in ground motion predictions are two indispensable parts of partially non-ergodic site-specific PSHA. Local site effects can be estimated using site response simulations or recorded ground motions at the site. When such predictions are available, the aleatory variability of ground motions used in PSHA can be changed to the single station sigma value. However, in these cases, the epistemic uncertainty in predicting site effects must be incorporated into the hazard analyses. This research focuses on the challenges specific to conducting partially non-ergodic site-specific PSHA using recorded ground motions or site response analysis. The main challenge in estimating local site effects using recorded data is whether ground motions collected in a relatively short time can be used to estimate site effects for long return period events. We first develop a database for recorded ground motions at the KiK-net array to investigate this question and use this database to develop a predictive model for the Fourier Amplitude Spectra of ground motions. The ground motion model (GMM) residuals are used to investigate the stability of site terms across different tectonic regimes. We observe that empirical site terms are stable across different tectonic regimes. This observation allows the use of ground motions from any tectonic regime (whether they belong to the tectonic regime that controls the hazard or not) to estimate local site effects. Moreover, in Fourier amplitude, site effects are not dependent on event magnitude and source to site distance; therefore, estimates of site effects from low magnitude events can be easily extrapolated to larger events. The Fourier amplitude GMM developed in this study adds to the library of Fourier amplitude models to be used in future partially non-ergodic site-specific PSHAs. In practice, one of the most common tools for simulating wave propagation is 1-D site response analysis. Two central assumptions in 1-D site response analysis are that the soil profile is comprised of horizontal soil layers of infinite extent and that the vertically propagating SH-waves control the horizontal component of ground motion. SH-waves tend to propagate vertically near the surface because as earthquake waves hit softer layers traveling from the source to the site, they refract until the path becomes steeply inclined. The validity of both assumptions in 1-D site response depends on the geological setting at the site and the geology between the earthquake source and the site, raising the question of which sites are suitable for 1-D site response analysis and what the model error in 1-D site response analysis is. We use the GMM developed for FAS to estimate observed and empirical site terms. The empirical site effects are then compared with the theoretical site effects to determine whether sites are amenable to 1-D site response analyses, and to quantify the model error in the analyses. / Doctor of Philosophy / It is impossible to predict future earthquake-induced ground motions due to randomness in the process and a lack of knowledge. In fact, there are significant uncertainties not only in predicting the location, time, and magnitude of a future earthquake but also in predicting the intensity of ground motion induced by a given future earthquake. Therefore, assessing the safety of the human environment against earthquake hazards requires a method that considers all sources of uncertainties. To this end, Earthquake Engineers have developed Probabilistic Seismic Hazard Analysis(PSHA) framework. Structural engineers use the results of PSHA to design a new structure or assess the safety of an existing building. The accuracy of PSHA estimations leads to designs that are both safe and cost-efficient. The distribution of possible ground motions induced by a given earthquake scenario significantly controls the result of PSHA. This distribution should consider the effect of source, source to site path, and local site effects. This research focuses on improving PSHA results by estimating local site effects using recorded ground motions or simulating wave propagation in the site. In estimating local site effects using recorded data, the local site effect observed in ground motions collected in a relatively short time window is used to estimate hazards from all scenarios. However, the collected ground motions usually belong to frequent low magnitude events that are different from large magnitude events that control the hazard. This difference requires either using a measure of local site effect that is independent of the magnitude and distance of the earthquake or considering the effect of magnitude and distance on the local site effect estimate. Moreover, since frequent events sample different sources and paths than large events, we need to make sure the local site effect is consistent across different sources and paths. This research develops Ground Motion Models(GMMs) for Fourier amplitude, a linear function of ground motion times series, using Japanese ground motions. The ratio of Fourier amplitude at the surface over bedrock is a measure of local site effect that is not dependant on magnitude and distance. The model is then used to see if the trade-off between source and site effect and path and site effect is significant or not. In practice, one of the most common tools for simulating wave propagation is 1-D site response analysis. Two central assumptions in 1-D site response analysis are that the soil profile comprises horizontal soil layers of infinite extent and that the vertically propagating horizontal shear waves (SH-waves) control the horizontal component of ground motion. SH-waves tend to propagate vertically near the surface because as earthquake waves hit softer layers traveling from the source to the site, they refract until the path becomes vertically inclined. The validity of both assumptions in 1-D site response depends on the geological setting at the site and the geology between the earthquake source and the site, raising the question of which sites are suitable for 1-D site response analysis and what the model error in 1-D site response analysis is. We use the GMM developed for FAS to estimate empirical local site effects. The empirical site effects are then compared with the theoretical site effects to determine whether sites are amenable to 1-D site response analyses and quantify the model error in the analyses.

Deep wastewater injection-induced seismicity has led to over a thousand magnitude (Mw) > 3 earthquakes and four Mw>5 earthquakes in Oklahoma, Texas, and Kansas (OTK) over the last ten years. Liquefaction observed following the 3 September 2016, Mw5.8 Pawnee, OK, induced earthquake raises concerns regarding the liquefaction risk posed by future induced earthquakes. The stress-based simplified liquefaction evaluation procedure is widely used to evaluate liquefaction potential. However, empirical aspects of this procedure were primarily developed for tectonic earthquakes in active shallow-crustal tectonic regimes (e.g., California). Consequently, due to differences in ground motion characteristics and regional geology, the depth-stress reduction factor (rd) and Magnitude Scaling Factor (MSF) relationships used in these variants may be unsuitable for use with induced earthquakes in OTK. This is because both rd, which accounts for the non-rigid soil profile response, and MSF, which accounts for shaking duration, are affected by ground motion and soil profile characteristics. The objective of this research is to develop and test a new liquefaction triggering model for use in assessing the regional liquefaction hazard in OTK from injection-induced earthquakes. This model incorporates induced seismicity-specific rd and MSF relationships. To assess model efficacy, the liquefaction potential is evaluated for several sites impacted by the 2016 Pawnee earthquake using the model developed herein, as well as several models commonly used to evaluate liquefaction potential for tectonic earthquakes. Estimates are then compared with field observations of liquefaction made following the Pawnee event. This analysis shows that, at most sites, the induced seismicity-specific model more accurately predicts liquefaction severity than do models developed for tectonic earthquakes, which tend to over-predict liquefaction severity. The liquefaction triggering model developed herein is also used to assess the minimum magnitude (Mmin) of induced earthquakes capable of triggering liquefaction. For sites capable of supporting structures, it is shown that Mmin = 5.0 is sufficient to fully capture liquefaction hazard from induced events in OTK. However, for extremely liquefaction-susceptible soil profiles that are potentially relevant to other infrastructure (e.g., pipelines and levees), consideration of Mmin as low as 4.0 may be required. / Doctor of Philosophy / Seismic activity caused by deep wastewater injection has caused over a thousand magnitude (Mw) > 3 earthquakes and four Mw>5 earthquakes in Oklahoma, Texas, and Kansas (OTK) over the last ten years. These events are referred to as induced earthquakes. Liquefaction observed following the 3 September 2016, Mw5.8 Pawnee, OK, induced earthquake raises concerns regarding the liquefaction risk posed by future induced earthquakes. The stress-based simplified liquefaction evaluation procedure is widely used to evaluate liquefaction potential. However, to date, variants of this procedure were developed primarily for natural, tectonic earthquakes in active seismic areas such as California. Due to differences between induced and tectonic earthquakes as well as regional geology, existing variants of the simplified procedure may be unsuitable for use with induced earthquakes in OTK. The objective of this research is to develop and test a new liquefaction triggering model for use in assessing the regional liquefaction hazard in OTK from injection-induced earthquakes. The model was developed using regional induced earthquake ground motion recordings and soil profiles. To assess model accuracy, liquefaction potential is assessed at several sites impacted by the 2016 Pawnee earthquake using the new model, as well as several models commonly used to evaluate liquefaction potential for tectonic earthquakes. Estimates are then compared with field observations of liquefaction made following the Pawnee event. This analysis shows that, at most sites, the induced seismicity-specific model more accurately predicts liquefaction severity than do models developed for tectonic earthquakes, which tend to over-predict liquefaction severity. The liquefaction triggering model developed herein is used to assess the minimum magnitude (Mmin) of induced earthquakes capable of triggering liquefaction. For sites capable of supporting structures, it is shown that Mmin = 5.0 is sufficient to fully capture liquefaction hazard from induced events in OTK. However, for extremely liquefaction-susceptible soil profiles potentially relevant to other infrastructure (e.g., pipelines and levees), Mmin as low as 4.0 may be required.

This paper presents a novel approach for designing and optimizing maintenance service contracts through the application of mechanism design theory. When offering a contract to its customer, the maintenance service provider seeks to specify contract terms - such as price, service features and incentives - that maximize the provider's profit, satisfy customer needs, allocate risks effectively and mitigate moral hazards. Optimal contract design has to account for asymmetric information and uncertainties associated with customer characteristics and behaviors. We illustrate our mechanism design approach by applying it to the contract design challenge of a gas turbine manufacturer, which also provides maintenance services for its aircraft engines. In our solution approach, we compute an optimal set of contracts. The entire set is presented to the customer and is designed such that the customer will accept one of the contract alternatives without negotiations. In addition to eliminating the costs and delays associated with negotiations, this approach also reveals the customer's private information to the service provider, which the provider can use to its benefit in maintenance management and future contract renewals. Furthermore, we design and incorporate win-win incentive mechanisms into the contracts, which reward the customer for actions that reduces maintenance costs. We present a deterministic and a stochastic mechanism design model, the latter accounting for uncertainties associated with customer actions, engine performance, and maintenance costs during the contract execution phase. / Master of Science

Reliable and robust methods of extreme value based hurricane surge prediction, such as the Joint Probability Method (JPM), are critical in the coastal engineering profession. The JPM has become the preferred surge hazard assessment method in the United States; however, it has a high computational cost: one location can require hundreds of simulated storms, and more than ten thousand computational hours to complete. Optimal sampling methods that use physics based surge response functions (SRFs), can reduce the required number of simulations. This study extends the development of SRFs to bay interior locations at Panama City, Florida. Mean SRF root-mean-square (RMS) errors for open coast and bay interior locations were 0.34 m and 0.37 m, respectively; comparable to expected ADCIRC model errors (~0.3 m—0.5 m). Average uncertainty increases from open coast and bay SRFs were 10% and 12%, respectively. Long-term climate trends, such as rising sea levels, introduce nonstationarity into the simulated and historical surge datasets. A common approach to estimating total flood elevations is to take the sum of projected sea-level rise (SLR) and present day surge (static approach); however, this does not account for dynamic SLR effects on surge generation. This study demonstrates that SLR has a significant dynamic effect on surge in the Panama City area, and that total flood elevations, with respect to changes in SLR, are poorly characterized as static increases. A simple adjustment relating total flood elevation to present day conditions is proposed. Uncertainty contributions from these SLR adjustments are shown to be reasonable for surge hazard assessments. / Master of Science

Injury is the leading cause of death and disabilityamong American children, and most injuries to children aged 1-5 years occur at home. Factors associated with increased risk for unintentional injury to young children include an overcrowded home environment, low SES, and living with a mother who is young, less educated, more emotionally overwhelmed, and less protective in her supervision, attributes characteristic of many adolescent mothers. Previous research suggests that teaching parents basic child behavior management techniques can reduce child dangerous behavior (Mathews, et al., 1987; Powers &amp; Chapieski, 1986), but these interventions have not addressed the lack of knowledge about child development common among adolescent parents. The current study employed a multiple-baseline design across subjects to assess the impact of a 6-week in home educational and behavioral treatment. Observable home hazards, supervisory skill, maternal efficacy, parenting stress, and cognitive readiness to parent were examined in four adolescent mothers (aged 16-19), in addition to the dangerous behavior of their children (aged 16-24 months). Mothers received education about child development and sensitive parenting, as well as training in home safety and child behavior management. Child dangerous behavior and maternal supervisory skill and positive behavior were assessed in weekly 20-minute videotaped mother-child interactions. Pre- and post- measures of parenting stress, cognitive readiness to parent, and maternal efficacy related to parenting and child safety were assessed by self-reports. Treatment resulted in improvements in mother positive behavior and knowledge of child development and in reductions of parenting stress, observable home hazards, and child dangerous behavior. A 2-month follow-up revealed some minimal maintenance of treatment gains suggesting additional booster sessions are needed for longer-term gains of reduced injury risk. / Ph. D.