Browsing by Author "Martey, Emmanuel Nii"
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Item Conditional probability of release of hazardous materials from railroad tank cars using Bayesian networks(University of Delaware, 2015) Martey, Emmanuel NiiRisk managers assessing hazardous materials release risk along various railroad routes and regions are tasked with evaluating the average likelihood of hazmat release from a derailed fleet of tank cars given varying proportions of tank car safety designs and operating conditions. These variations or changes may be as a result of retrofitting or phasing out of existing safety features (which have been deemed outmoded or unacceptable in the new safety climate), tank car fleet upgrade, construction of tank cars with new specifications, enhanced advanced braking rules or varying operating speeds. This thesis seeks to present Bayesian Networks (BNs) as a viable approach for modelling and supporting decision making in the fields of hazardous materials transportation risk and rail tank car safety. This approach estimates the average Conditional Probability of Release (CPR) of an existing or projected fleet of cars plying a given railroad route or region. CPR is one of the two primary components used in the analysis of hazardous materials release risk. This methodology can be used in assessing the reduction (or otherwise) of the average CPR of an existing or proposed fleet of tanks cars given a change in risk reduction option (tank car design safety feature or operating conditions). BNs allow for the evaluation of the effect of new or alternate risk reduction options (RRO) on the total network. They can also be used to evaluate the merits and demerits of the practice of grandfathering from a release probability point of view. Furthermore, Bayesian Networks can be used to easily rank the effect of various safety features and operating conditions given a CPR estimate dataset of all possible state combinations of the variables (risk reduction options) being considered. This allows researchers and decision makers to make decisions on which RRO to employ. As a result of interactive and flexible nature of BNs, these models can be integrated with other models to arrive at such a decision. The resulting average CPR value obtained from these models can be subsequently incorporated into the analysis of hazmat transportation risk. A CPR estimate dataset of possible combinations of four tank car design safety features was considered in the study. The features were tank thickness, insulation, head shield protection and top fittings protection. The aforementioned along with the total CPR made up the random variables of the Bayesian Network. The BN modelling was implemented using the commercially available HUGIN software. The average CPRs of the tank cars were computed given varying proportions of risk reduction options combinations. Sensitivity analysis was conducted to investigate the effect of various risk reduction options on the CPR of the fleet which were subsequently ranked.Item Copula-based models in railroad maintenance and safety analysis(University of Delaware, 2018) Martey, Emmanuel NiiThe American railroad industry has been a primary stakeholder in the economic development of the nation for close to two centuries. The railroads account for over two-fifths of freight revenue ton-miles and transports about a third of all national exports. To ensure good operable conditions of rail infrastructure particularly the track, the railroads have spent more than 40\% of their revenue on capital expenditure and maintenance since industry deregulation. Due to budgetary and high logistical constraints, there has been a gradual shift to predictive maintenance strategies with railroads planning track geometry maintenance activities in advance. To employ such strategies, there is the need to know beforehand the effectiveness of maintenance activities which can be evaluated by the amount of improvement or recovery in track geometry condition. ☐ Well executed maintenance invariably improves operational efficiency and safety which are primary objectives of the railroads. The huge investment in maintenance led to all-time lows in train derailment rate, accident rate, and collision rate recorded in recent years. Despite their relatively low frequency, derailments remain a major concern for the railroads due to their high consequences which include loss of life and property, disruption of services, injury, and destruction to the natural environment. It is therefore important to carefully examine train derailment severity in order to minimize these ramifications. ☐ In many railroad applications of data analysis; non-normality of data occurs in several forms. For example, exploratory data analysis of both derailment data and track geometry data showed that the marginal and joint distributions of the variables were not normal. Conventional correlation analysis is generally not suitable for analyzing the dependencies between variables with non-normality, tail dependence, asymmetric dependence, skewness and other nonlinearities. Furthermore, conventional correlation analysis also fails to consider the underlying dependence between multiple response variables which may be skewed or discrete in nature. This dissertation focuses on the formulation of copula-based methodologies to analyze railroad maintenance and safety applications considering the underlying dependence between the variables of interest. Copulas allow for the separate modeling of arbitrary marginal distributions and the dependence structure. Copulas are suitable for modeling various forms of dependence and can be employed in the generation of large volumes of data. ☐ Three railroad engineering case studies are undertaken in this dissertation. In the first case study, a bivariate copula-based approach is developed to evaluate the tamping recovery of track geometry parameters such as surface, alignment, cross level, gage, and warp considering the underlying dependence between the variables of interest. In the second case study, a mixed copula-based regression model is developed which simultaneously models the monetary damage and number of derailed cars conditional on a set of covariates that might affect both derailment severity outcomes. Marginal generalized linear regression models are combined with a bivariate copula which characterizes the dependence between the two responses. In the third and final case study, vine copula models, a cascade of bivariate copulas as building blocks, are used to model high-dimensional dependencies within the derailment severity data. ☐ Results from this dissertation provide greater insight and comprehension of the train derailment severity and track geometry recovery phenomena considering various forms of dependence between the variables of interest. These results will aid decision making which would help reduce the consequences of train derailments as well as improve track maintenance strategies.