Tall building frames that respond to large seismic ground motions tend to have significant spatial variability of damage over their height, often with a concentration of that damage in the lower stories. In spite of this spatial variability of damage, existing damage and loss models tend to focus on taking the maximum story drift and then assuming the same drift applies over the entire height, damage is then calculated for the building—clearly a conservative approach. A new loss analysis approach is thus recommended that incorporates the effects of spatial distribution of earthquake induced damage to frame buildings. Moreover, the approach aims to discriminate between required repair and replacement damages. Suites of earthquakes and incremental dynamic analysis along with the commercial software SAP2000 are used to establish demands from which story damage and financial losses are computed directly and aggregated for the entire structure. Rigorous and simplified methods are developed that account for spatial distribution of different damage levels arising from individual story drifts.

The main objective of this study is to develop a methodology to assess seismic vulnerability of concrete structures and to estimate direct losses related to structural damage due to future seismic events. This dissertation contains several important components including development of more detailed demand models to enhance accuracy of fragility relationships and development of a damage assessment framework to account for uncertainties. This study focuses on concrete structures in the Mid-America region where a substantial seismic risk exists with potential high intensity earthquakes in this geographic region. The most common types of concrete structures in this area are identified based on the building inventory data and reinforced concrete (RC) frame buildings and tilt-up concrete buildings are selected as case study buildings for further analysis. Using synthetic ground motion records, the structural behavior of the representative case study buildings is analyzed through nonlinear time history analyses. The seismic performance of the case study buildings is evaluated to describe the structural behavior under ground motions. Using more detailed demand models and the corresponding capacity limits, analytical fragility curves are developed based on appropriate failure mechanisms for different structural parameters including different RC frame building heights and different aspect ratios for tilt-up concrete structures. A probabilistic methodology is used to estimate the seismic vulnerability of the case study buildings reflecting the uncertainties in the structural demand and capacity, analytical modeling, and the information used for structural loss estimation. To estimate structural losses, a set of damage states and the corresponding probabilistic framework to map the fragility and the damage state are proposed. Finally, scenario-based assessments are conducted to demonstrate the proposed methodology. Results show that the proposed methodology is successful to evaluate seismic vulnerability of concrete structures and effective in quantifying the uncertainties in the loss estimation process.

At present there is no simple, yet scientifically robust method for calculating insurance loss estimates due to a fire. Therefore building owners and insurers can not make suitably informed decisions when selecting fire protection measures or setting premiums as they have no way of defining the true risk they face. As a consequence this research aims to investigate a number of techniques in an effort to define one as appropriate for further research. Three different methods were explored and consist of risk based analysis, deterministic hand calculations and Computational Fluid Dynamics (CFD). Extensive literature reviews were conducted in each area and the final models were based on the outcomes of this research. Rack storage warehouses were chosen for analysis as they are currently topical within the fire engineering community and are a particular concern for insurers. The risk based analysis employed statistical techniques including event tree analysis and monte carlo simulation to calculate loss distributions and sensitivity analyses. The hand calculation method was based on equations presented in the literature and incorporated the use of a zone model (BRANZFire) to calculate deterministic loss estimates. The CFD model used was Fire Dynamics Simulator and full scale warehouse fires were modelled using this programme. It was concluded that Fire Dynamics Simulator is an inappropriate tool as the capability for providing loss estimates in a timely manner is currently beyond the model's capabilities. Of the two remaining methods the statistical risk based model was selected as the most appropriate for further investigation. The primary reasons for this decision were the ability to calculate loss distributions and conduct sensitivity analyses, as well as its versatility and user friendliness. Improved statistical data was defined as imperative for future development of the model.

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Determining where people are most likely to suffer losses and have difficulty in evacuating from a hurricane is important to developing a hurricane response strategy. This thesis proposes a methodology for modeling and assessing evacuation vulnerability to a specific hurricane and applies this method to study Pinellas County, Florida. The vulnerability of Pinellas County to evacuation problems and the degree of loss that may be suffered from a hurricane is quantified in the Hurricane Evacuation Vulnerability Index. This index is the sum of three indices that represent social, transportation, and geophysical aspects of hazards research. Social vulnerability is assessed with an existing social vulnerability model that uses census data to locate areas where people will have difficulty evacuating based on demographic variables. Areas where people are vulnerable to traffic problems due to the condition of the evacuation routes are identified with a model developed using GIS. The degree of damage these areas may suffer from a specific hurricane is modeled using a tightly coupled GIS program, HAZUS-MH. These loss estimates are used to identify areas where evacuation may be necessary. The Hurricane Evacuation Vulnerability Index is mapped to show the areas that are most vulnerable to evacuation problems and may suffer losses to the built environment and subsequent human displacement. The Hurricane Evacuation Vulnerability Index is a valuable tool for emergency planning. The results are useful in allocating and directing resources to facilitate the evacuation of vulnerable areas. Emergency management officials can prepare evacuation plans based on the modeled results. Traffic management strategies can be implemented to reduce traffic congestion along evacuation routes. Transportation resources, such as buses, can be directed to areas where people do not have the resources to evacuate.

Knowledge of fire risk is crucial for manufacturers and regulators to make correct choices in prescribing fire protection systems, especially flame retardants. Methods of determining fire risk are bogged down by a multitude of confounding factors, such as population demographics and overlapping fire protection systems. Teasing out the impacts of one particular choice or regulatory change in such an environment is crucial. Teasing out such detail requires statistical techniques, and knowledge of the field is important for verifying potential methods. Comparing the fire problems between two states might be one way to identify successful approaches to fire safety. California, a state with progressive fire prevention policies, is compared to Texas using logistic regression modeling to account for various common factors such as percentage of rural population and percentage of population in ‘risky’ age brackets. Results indicate that living room fires, fires in which the first item ignited is a flammable liquid, piping, or filter, and fires started by cigarettes, pipes, and cigars have significantly higher odds of resulting in a casualty or fatality than fires started by other areas of origin, items first ignited, or heat sources. Additionally, fires in Texas have roughly 1.5 times higher odds of resulting in casualties than fires in California for certain areas of origin, items first ignited, and heat sources. Methods of estimating fire losses are also examined. The potential of using Ramachandran’s power-law relationship to estimate fire losses in residential home fires in Texas is examined, and determined to be viable but not discriminating. CFAST is likewise explored as a means to model fire losses. Initial results are inconclusive, but Monte Carlo simulation of home geometries might render the approach viable. / text

Insulated Gate Bipolar Transistor (IGBT) based voltage source converter (VSC) applica-tions embedded in power systems are growing. Optimal design of thermal management systems for such converters requires estimation of IGBT losses under various operating conditions, both normal and abnormal. Therefore, development of tools for estimating IGBT losses in EMT simulators is important as converters embedded in large power sys-tems are simulated in EMT simulators. Two circuit models are developed to simulate turn-on and turn-off transients using a be-havioral approach. These circuit models mimic the observed behavior in distinct phases of the turn-on and turn-off transients under the inductive load switching. In this model, the nonlinear nature of the circuit model of the IGBT is treated and converter specific in-fluential parameters are taken in to account. An excellent correlation between the meas-ured and simulated waveforms as well as measured and estimated switching losses is ob-served. Finally, an efficient method to incorporate switching loss calculation in an EMT program in the form of a lookup table created using the developed transient model is pro-posed.

Insulated Gate Bipolar Transistor (IGBT) based voltage source converter (VSC) applica-tions embedded in power systems are growing. Optimal design of thermal management systems for such converters requires estimation of IGBT losses under various operating conditions, both normal and abnormal. Therefore, development of tools for estimating IGBT losses in EMT simulators is important as converters embedded in large power sys-tems are simulated in EMT simulators. Two circuit models are developed to simulate turn-on and turn-off transients using a be-havioral approach. These circuit models mimic the observed behavior in distinct phases of the turn-on and turn-off transients under the inductive load switching. In this model, the nonlinear nature of the circuit model of the IGBT is treated and converter specific in-fluential parameters are taken in to account. An excellent correlation between the meas-ured and simulated waveforms as well as measured and estimated switching losses is ob-served. Finally, an efficient method to incorporate switching loss calculation in an EMT program in the form of a lookup table created using the developed transient model is pro-posed.

Catastrophic events such as dam failures or severe floods are considered to be of low probability, although their consequences can be extremely high and might include loss of life. Earlier studies have linked circumstances surrounding historical darn failure events to actual loss of life and produced formulations using statistical analysis of these events. Shortcomings of these methods include the inability to adjust life-loss estimates based on the type of darn failure, global averaging of population at risk, and ignoring the dynamics of the evacuation process. The main objective of this research is to develop a practical and improved life-loss estimation approach for use in dam safety risk assessment and emergency planning. The methodology is specifically formulated to overcome the limitations of previous. purely empirical, approaches. The approach takes into account the spatial and temporal distribution of flood water depth and velocity, fate of buildings, simulation of warning diffusion, and tracking the movement of people from their original location towards safe shelters. The model created, called LlFESim, is designed to serve multiple function s. First, it can be used in a Deterministic Mode using best estimate inputs to obtain point estimates, or to test different policies for evacuation as well as different times of the day and for different dam breach flooding scenarios. Second, the Uncertainty Mode represents input and parameter uncertainties to provide estimates of life loss, and other variables relating to warning and evacuation effectiveness, as probability distributions. These distributions of life loss can be combined with estimates of the uncertainties in other risk assessment inputs, to obtain estimates of uncertainties in risk assessment results, including evaluations against tolerable risk guidelines. Two communities were used to demonstrate the model performance. Deterministic Mode results display the various possible model outputs. Sensitivity analysis for the Deterministic Mode shows that the effect of warning issuance time is the dominant factor in the estimated life loss. However, other factors play an important role such as the time of day, effectiveness of the warning system, and shelter location. Uncertainty Mode results demonstrate the effect of uncertainties in model parameters and inputs on the model results.

Seismic rehabilitation of structural systems is an effective approach for reducing potential seismic losses such as social and economic losses. However, little or no effort has been made to develop a framework for making decisions on seismic rehabilitation of structural systems that systematically incorporates conflicting multiple criteria and uncertainties inherent in the seismic hazard and in the systems themselves. This study develops a decision support framework for seismic rehabilitation of structural systems incorporating uncertainties inherent in both the system and the seismic hazard, and demonstrates its application with detailed examples. The decision support framework developed utilizes the HAZUS method for a quick and extensive estimation of seismic losses associated with structural systems. The decision support framework allows consideration of multiple decision attributes associated with seismic losses, and multiple alternative seismic rehabilitation schemes represented by the objective performance level. Three multi-criteria decision models (MCDM) that are known to be effective for decision problems under uncertainty are employed and their applicability for decision analyses in seismic rehabilitation is investigated. These models are Equivalent Cost Analysis (ECA), Multi-Attribute Utility Theory (MAUT), and Joint Probability Decision Making (JPDM). Guidelines for selection of a MCDM that is appropriate for a given decision problem are provided to establish a flexible decision support system. The resulting decision support framework is applied to a test bed system that consists of six hospitals located in the Memphis, Tennessee, area to demonstrate its capabilities.