Over fifty small sinkholes (~1 meter in depth and width) were found in conjunction with structural damage to homes in an area south of Cleveland, TX. The local geology lacks carbonate and evaporite deposits associated with normal sinkhole development through dissolution. The morphology and distribution of sinkholes, and the geologic setting of the site are consistent with piping erosion. However, the site lacked the significant hydraulic gradient or exit points for sediment associated with traditional piping erosion. In areas of sinkholes, geophysical measurements of apparent electrical conductivity delineated anomalously high conductivity levels that are interpreted as a brine release from a nearby oil-field waste injection well. The contaminated areas have sodium adsorption ratios (SAR) as high as 19, compared to background levels of 3. Sodium has been shown to cause dispersion of soil colloids, allowing for sediment transport at very low velocities. Thus, subsurface erosion of dispersed sediment could be possible without significant hydraulic gradients. This hypothesis is backed by the observation of the depletion of colloidal particles within the E-horizon of sinkholes. However, there is a lack of precedence of waste brines initiating colloid dispersion. Also, sodium dispersion is not thought to be an important process in piping erosion in humid settings such as this one. Therefore, laboratory experiments on samples from the site area, designed to simulate field conditions, were conducted to measure dispersion verses pH, SAR and electrical conductivity (EC). Analysis of the experimental data with neural networks showed that an increase in SAR did increase dispersion. A dispersion prediction map, constructed with the trained neural network and calibrated geophysical data, showed correlation between sinkhole locations and increased predicted dispersion. This research indicates that a contaminant high in sodium content has caused colloidal dispersion, which may have allowed nontraditional subsurface erosion to occur in an area lacking a significant hydraulic gradient.

The probabilistic safety assessment of engineering systems involving high-consequence low-probability events is stochastic in nature due to uncertainties inherent in time to an event. The event could be a failure, repair, maintenance or degradation associated with system ageing. Accurate reliability prediction accounting for these uncertainties is a precursor to considerably good risk assessment model. Stochastic Markov reliability models have been constructed to quantify basic events in a static fault tree analysis as part of the safety assessment process. The models assume that a system transits through various states and that the time spent in a state is statistically random. The system failure probability estimates of these models assuming constant transition rate are extensively utilized in the industry to obtain failure frequency of catastrophic events. An example is core damage frequency in a nuclear power plant where the initiating event is loss of cooling system. However, the assumption of constant state transition rates for analysis of safety critical systems is debatable due to the fact that these rates do not properly account for variability in the time to an event. An ill-consequence of such an assumption is conservative reliability prediction leading to addition of unnecessary redundancies in modified versions of prototype designs, excess spare inventory and an expensive maintenance policy with shorter maintenance intervals. The reason for this discrepancy is that a constant transition rate is always associated with an exponential distribution for the time spent in a state. The subject matter of this thesis is to develop sophisticated mathematical models to improve predictive capabilities that accurately represent reliability of an engineering system. The generalization of the Markov process called the semi-Markov process is a well known stochastic process, yet it is not well explored in the reliability analysis of nuclear power plant systems. The continuous-time, discrete-state semi-Markov process model is a stochastic process model that describes the state transitions through a system of integral equations which can be solved using the trapezoidal rule. The primary objective is to determine the probability of being in each state. This process model ensures that time spent in the states can be represented by a suitable non-exponential distribution thus capturing the variability in the time to event. When exponential distribution is assumed for all the state transitions, the model reduces to the standard Markov model. This thesis illustrates the proposed concepts using basic examples and then develops advanced case studies for nuclear cooling systems, piping systems, digital instrumentation and control (I&C) systems, fire modelling and system maintenance. The first case study on nuclear component cooling water system (NCCW) shows that the proposed technique can be used to solve a fault tree involving redundant repairable components to yield initiating event probability quantifying the loss of cooling system. The time-to-failure of the pump train is assumed to be a Weibull distribution and the resulting system failure probability is validated using a Monte Carlo simulation of the corresponding reliability block diagram. Nuclear piping systems develop flaws, leaks and ruptures due to various underlying damage mechanisms. This thesis presents a general model for evaluating rupture frequencies of such repairable piping systems. The proposed model is able to incorporate the effect of aging related degradation of piping systems. Time dependent rupture frequencies are computed and the influence of inspection intervals on the piping rupture probability is investigated. There is an increasing interest worldwide in the installation of digital instrumentation and control systems in nuclear power plants. The main feedwater valve (MFV) controller system is used for regulating the water level in a steam generator. An existing Markov model in the literature is extended to a semi-Markov model to accurately predict the controller system reliability. The proposed model considers variability in the time to output from the computer to the controller with intrinsic software and mechanical failures. State-of-the-art time-to-flashover fire models used in the nuclear industry are either based on conservative analytical equations or computationally intensive simulation models. The proposed semi-Markov based case study describes an innovative fire growth model that allows prediction of fire development and containment including time to flashover. The model considers variability in time when transiting from one stage of the fire to the other. The proposed model is a reusable framework that can be of importance to product design engineers and fire safety regulators. Operational unavailability is at risk of being over-estimated because of assuming a constant degradation rate in a slowly ageing system. In the last case study, it is justified that variability in time to degradation has a remarkable effect on the choice of an effective maintenance policy. The proposed model is able to accurately predict the optimal maintenance interval assuming a non-exponential time to degradation. Further, the model reduces to a binary state Markov model equivalent to a classic probabilistic risk assessment model if the degradation and maintenance states are eliminated. In summary, variability in time to an event is not properly captured in existing Markov type reliability models though they are stochastic and account for uncertainties. The proposed semi-Markov process models are easy to implement, faster than intensive simulations and accurately model the reliability of engineering systems.

Taiwan, as an island country, should place future aspiration on the usages of ocean energy and marine resources, such as offshore wind power and deep ocean water. The sound development of marine services relies on a strong industry of maritime engineering. The perilous marine environment has posed the highest risk for all maritime civil engineering activities. It is therefore imperative to restrain the risk associated with current maritime work, other than just engineering technique itself. By doing so, the quality of maritime work can be assured, and as the improvement of overall engineering capability, Taiwan can compete worldwide in the maritime engineering industry. Maritime works have developed their own standard construction procedures. To mitigate risk of maritime works depend mainly on the domain experts¡¦ experience and know-how. However, problems appear when less experienced experts are available, or qualitative experience exists in a narrative form. It is therefore important to structure clearly an engineering risk factor relation, and quantify and control these risk factors. The proposed study will first collect and review related literatures, and then interview an expert from the designate maritime service company to establish the risk factors associated with offshore piping. Eventually a complete Bayesian network (BN) was formulated based on the cause-effect diagram, using Interpretive Structural Modeling (ISM), and experts¡¦ experience was transformed into a set of prior and conditional probability to be embedded in the BN. The BN can clearly show that certain earlier operational factors affect final operational process deeply. Besides, the backward reasoning using the BN is possible to identify the factors causing a project failure.

In overwhelming majority of the petroleum or petrochemical plants, pressure vessels and process piping play important roles among the major elements of static equipments. So, based on the integrity of safety management for the petroleum or petrochemical plants and reduction of the operation risks, some objective schemes of the systematic failure evaluations and assessments should be established in order to optimize the resources of inspection and maintenance. However, performing the inspections based on the conventional methodologies, some uncontrolled factors which caused by the environments and inspection methods may exist and affect the assessment of the estimated corrosion rate. If the influences of the uncontrolled factors were not considered and compensated in the assessment of the estimated corrosion rate, some underestimate or unreasonable results would be obtained which lead potential risks may exist in the plants. Moreover, the measured data of some parameters, for example, operation pressure, corrosion condition, allowable stress, which were used to evaluate the estimated corrosion rate of the pressure components may exhibit a normal or non-normal distribution. Under such circumferences, if one used the nominal values of the measured parameters to assessment the safety conditions of the pressure components, potential risks may exist in the petroleum or petrochemical plants at the final stage of long-term operation. With an eye to obtain more conservative and objective assess results for pressure equipments in the long-term operation, three subjects will be differentiated between the evaluation of estimated corrosion rate, failure probability of pressure vessels and pressure safety valve (PSVs). First, based on the pressure boundaries suffered from general corrosion, a statistical methodology was proposed to modify the assessment of estimated corrosion rates for the pressure components in conventional methodology. Furthermore, the obtained results of the estimated corrosion rates will be used to assess the failure probability of pressure components based on the upper limited value. By adopting First Order Second Moment (FOSM) method, the failure probability was approached for the pressure components in long term operation. Moreover, for the sake of optimize the inspection and maintenance resources based on the acceptable risk of the plant owners, typical semi-quantitative risk based inspection (RBI) methodology to each pressure vessel are proposed in safety management based on the approached failure probability. Besides, the final protection for the pressure equipments when the pressure systems were upset - pressure safety valves (PSVs), are also play important roles to system evaluation and safety management for pressurized system. So, follow the semi-quantitative RBI methodology, the objective evaluation schemes together with the suggested inspection interval were conservatively established. Based on the conclusion of the studies, few pressure components with high failure probability will raise the operation risk of the pressurized system. It is an effective way to reduce the operation risk of the pressurized system by shift the limited resource of inspection/maintenance on the pressure components with high risk and obtain further control with effective strategies. Moreover, the conclusion also shows the prospective inspection intervals of PSVs which time-based strategy according to the local regulations (2-year based) should be change to condition-based strategy to reduce the operation risk.

<p>This study examines to what extent preposition stranding is used in connection with which, whom and who in three different UK papers. Also what factors influence the use of preposition stranding has been studied. The hypothesis that pied-piping is more common than preposition stranding has been confirmed.</p><p>A factor that has a certain influence on the use of preposition stranding is the style of the paper. The more formal of the papers studied, The Times, did not use preposition stranding to the same extent as the other two, The Sun and Today.</p><p>The subject domain of the texts has influence on the use of preposition stranding, with more informal domains such as sports and miscellaneous (e.g. gossip) using stranding to a higher extent than the other domains, e.g. business, politics and culture. The prepositions themselves also influence the use of preposition stranding with some prepositions, such as on, with, for and into, that are likely to appear stranded and others, such as in that are likely to appear pied-piped.</p>

Over fifty small sinkholes (~1 meter in depth and width) were found in conjunction with structural damage to homes in an area south of Cleveland, TX. The local geology lacks carbonate and evaporite deposits associated with normal sinkhole development through dissolution. The morphology and distribution of sinkholes, and the geologic setting of the site are consistent with piping erosion. However, the site lacked the significant hydraulic gradient or exit points for sediment associated with traditional piping erosion. In areas of sinkholes, geophysical measurements of apparent electrical conductivity delineated anomalously high conductivity levels that are interpreted as a brine release from a nearby oil-field waste injection well. The contaminated areas have sodium adsorption ratios (SAR) as high as 19, compared to background levels of 3. Sodium has been shown to cause dispersion of soil colloids, allowing for sediment transport at very low velocities. Thus, subsurface erosion of dispersed sediment could be possible without significant hydraulic gradients. This hypothesis is backed by the observation of the depletion of colloidal particles within the E-horizon of sinkholes. However, there is a lack of precedence of waste brines initiating colloid dispersion. Also, sodium dispersion is not thought to be an important process in piping erosion in humid settings such as this one. Therefore, laboratory experiments on samples from the site area, designed to simulate field conditions, were conducted to measure dispersion verses pH, SAR and electrical conductivity (EC). Analysis of the experimental data with neural networks showed that an increase in SAR did increase dispersion. A dispersion prediction map, constructed with the trained neural network and calibrated geophysical data, showed correlation between sinkhole locations and increased predicted dispersion. This research indicates that a contaminant high in sodium content has caused colloidal dispersion, which may have allowed nontraditional subsurface erosion to occur in an area lacking a significant hydraulic gradient.

Predators are a threat to many ground-nesting shorebirds, although it remains largely unknown how they interact with passive predator management techniques such as nest exclosures. I examined the effects of nest exclosures on incubating Piping Plovers (Charadrius melodus melodus) and their predators on nesting beaches in Kouchibouguac and PEI National Parks. A combination of behavioural observations, video monitoring of nests and an artificial nest experiment was used to examine the effects of nest exclosures in this study system. The behaviour of Piping Plovers did not differ between exclosed and unexclosed nests, although different types of predators seemed to have an effect on plover nest attentiveness. Predators visited exclosed nests more often than unexclosed nests and spent more time in the vicinity of exclosed nests than unexclosed nests. Since increased adult mortality and nest abandonment have been documented at exclosed Piping Plover nests, as well as nests of other shorebirds, the results of this study provide evidence of a link between predator harassment and these negative effects.

No description available.

The probabilistic safety assessment of engineering systems involving high-consequence low-probability events is stochastic in nature due to uncertainties inherent in time to an event. The event could be a failure, repair, maintenance or degradation associated with system ageing. Accurate reliability prediction accounting for these uncertainties is a precursor to considerably good risk assessment model. Stochastic Markov reliability models have been constructed to quantify basic events in a static fault tree analysis as part of the safety assessment process. The models assume that a system transits through various states and that the time spent in a state is statistically random. The system failure probability estimates of these models assuming constant transition rate are extensively utilized in the industry to obtain failure frequency of catastrophic events. An example is core damage frequency in a nuclear power plant where the initiating event is loss of cooling system. However, the assumption of constant state transition rates for analysis of safety critical systems is debatable due to the fact that these rates do not properly account for variability in the time to an event. An ill-consequence of such an assumption is conservative reliability prediction leading to addition of unnecessary redundancies in modified versions of prototype designs, excess spare inventory and an expensive maintenance policy with shorter maintenance intervals. The reason for this discrepancy is that a constant transition rate is always associated with an exponential distribution for the time spent in a state. The subject matter of this thesis is to develop sophisticated mathematical models to improve predictive capabilities that accurately represent reliability of an engineering system. The generalization of the Markov process called the semi-Markov process is a well known stochastic process, yet it is not well explored in the reliability analysis of nuclear power plant systems. The continuous-time, discrete-state semi-Markov process model is a stochastic process model that describes the state transitions through a system of integral equations which can be solved using the trapezoidal rule. The primary objective is to determine the probability of being in each state. This process model ensures that time spent in the states can be represented by a suitable non-exponential distribution thus capturing the variability in the time to event. When exponential distribution is assumed for all the state transitions, the model reduces to the standard Markov model. This thesis illustrates the proposed concepts using basic examples and then develops advanced case studies for nuclear cooling systems, piping systems, digital instrumentation and control (I&C) systems, fire modelling and system maintenance. The first case study on nuclear component cooling water system (NCCW) shows that the proposed technique can be used to solve a fault tree involving redundant repairable components to yield initiating event probability quantifying the loss of cooling system. The time-to-failure of the pump train is assumed to be a Weibull distribution and the resulting system failure probability is validated using a Monte Carlo simulation of the corresponding reliability block diagram. Nuclear piping systems develop flaws, leaks and ruptures due to various underlying damage mechanisms. This thesis presents a general model for evaluating rupture frequencies of such repairable piping systems. The proposed model is able to incorporate the effect of aging related degradation of piping systems. Time dependent rupture frequencies are computed and the influence of inspection intervals on the piping rupture probability is investigated. There is an increasing interest worldwide in the installation of digital instrumentation and control systems in nuclear power plants. The main feedwater valve (MFV) controller system is used for regulating the water level in a steam generator. An existing Markov model in the literature is extended to a semi-Markov model to accurately predict the controller system reliability. The proposed model considers variability in the time to output from the computer to the controller with intrinsic software and mechanical failures. State-of-the-art time-to-flashover fire models used in the nuclear industry are either based on conservative analytical equations or computationally intensive simulation models. The proposed semi-Markov based case study describes an innovative fire growth model that allows prediction of fire development and containment including time to flashover. The model considers variability in time when transiting from one stage of the fire to the other. The proposed model is a reusable framework that can be of importance to product design engineers and fire safety regulators. Operational unavailability is at risk of being over-estimated because of assuming a constant degradation rate in a slowly ageing system. In the last case study, it is justified that variability in time to degradation has a remarkable effect on the choice of an effective maintenance policy. The proposed model is able to accurately predict the optimal maintenance interval assuming a non-exponential time to degradation. Further, the model reduces to a binary state Markov model equivalent to a classic probabilistic risk assessment model if the degradation and maintenance states are eliminated. In summary, variability in time to an event is not properly captured in existing Markov type reliability models though they are stochastic and account for uncertainties. The proposed semi-Markov process models are easy to implement, faster than intensive simulations and accurately model the reliability of engineering systems.

This dissertation contains two essays on the analysis of market imperfections. In the first essay, I empirically test whether in a three-level hierarchy with asymmetries of information, more competition among intermediaries leads to more deception against the principal. In this setting, intermediaries supervise agents by delegation of the principal, and compete among themselves to provide supervision services to the agents. They cannot be perfectly monitored, therefore allowing them to manipulate supervision results in favor of the agents, and potentially leading to less than optimal outcomes for the principal. Using inspection-level data from the vehicular inspection program in Atlanta, I test for the existence of inspection deception (false positives), and whether this incidence is a function of the number of local competitors by station. I estimate the incidence of the most common form of false positives (clean piping) to be 9% of the passing inspections during the sample period. Moreover, the incidence of clean piping -- passing results of a different vehicle fraudulently applied to a failing vehicle -- per station increases by 0.7% with one more competitor within a 0.5 mile radius. These results are consistent with the presence of more competitors exacerbating the perverse incentives introduced by competition under this setting. In the second essay, we test whether electricity consumption by industrial and commercial customers responds to real-time prices after these firms sign-up for prices linked to the electricity wholesale market price. In principle, time-varying prices (TVP) can mitigate market power in wholesale markets and promote the integration of intermittent generation sources such as wind and solar power. However, little is known about the prevalence of TVP, especially in deregulated retail markets where customers can choose whether to adopt TVP, and how these firms change their consumption after signing up for this type of tariff. We study firm-level data on commercial and industrial customers in Texas, and estimate the magnitude of demand responsiveness using demand equations that consider the restrictions imposed by the microeconomic theory. We find a meaningful level of take-up of TVP ? in some sectors more than one-quarter of customers signed up for TVP. Nevertheless, the estimated price responsiveness of consumption is still small. Estimations by size and by type of industry show that own price elasticities are in most cases below 0.01 in absolute value. In the only cases that own price elasticities reach 0.02 in absolute value, the magnitude of demand response compared to the aggregate demand is negligible.