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The Asbestos Hazard Emergency Response Act (AHERA) requires public schools to manage asbestos containing materials. Twenty five years after AHERA was enacted public schools continue to struggle with documenting and managing asbestos containing material assets. In addition, the manufacturing of lead based paint (LBP) was banned over thirty years ago yet public schools continue to have to manage LBP assets with no guidelines specific to public schools. When compared to current civil infrastructure asset management systems, AHERA and the HUD guidelines lack a rating system based on visual inspection data. The development of a condition index algorithm and risk of failure model would provide school planners an efficient management tool to predict the future condition of asbestos containing material and lead based paint assets. As a result, school planners would be able to prioritize maintenance, repair, and abatement projects based on the risk to the indoor air quality of their facilities and more efficiently utilize their limited resources to mitigate such risks. This paper presents initial work toward the development of a visual condition index algorithm and a risk of failure model to support prioritization of maintenance, repair, and abatement projects. The condition assessment categories provided by AHERA and HUD were adapted and incorporated in an evaluation form created to assist in rating the various stages of accessibility, deterioration, and detection of typical ACM and LBP building components. The evaluation form can be utilized by inspection and school personnel when reclassifying ACM and LBP components during semi-annual inspections of their facilities and also ensure the repeatability of the condition assessment and risk of failure methodologies. A risk of failure model was developed utilizing the FMEA process, specifically the calculation of a risk priority number (RPN). Three schools were selected for a field pilot study to develop the accessibility, deterioration, detection, and RPN algorithms and evaluate for repeatability. The algorithms will provide a quantitative and consistent means for documenting the condition and RPN of asbestos containing material and lead based paint assets and allow the condition of these assets to be monitored and reclassified over a period of time. / Ph. D.

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Deterioration of bridge decks is an ongoing problem faced by transportation agencies across the country. In past decades, Non-Destructive Evaluation (NDE) techniques, capable of detecting various deteriorations types, e.g., cracking, delamination and reinforcing steel corrosion, have emerged. These techniques generate large amounts of data representing different underlying physics, (decibels for ground penetrating radar and volts for half-cell potential), making data interpretation and comparison difficult for bridge owners and practitioners. The deterioration threshold, or the transition between healthy and deteriorated areas, is essential in understanding NDE data. However, this threshold is determined empirically in former research and engineering practice. In the present research, a probability-based method is proposed to identify deterioration thresholds for specified confidence levels. NDE data measuring different underlying physics are transformed into a binary format by threshold values to compare and combine multiple NDE techniques for bridge deck assessment. The finite element method is also implemented to correlate bridge deck surface stresses with deteriorations measured by NDE techniques, and to study the causes on concrete bridge deck degradation. The general methodology developed in this study will be demonstrated on three bridges, i.e., Virginia, New Jersey and New York Pilot Bridges, which were studied under Federal Highway Administration (FHWA)'s Long-Term Bridge Performance (LTBP) Program. / Ph. D.

Modeling pavement deterioration and predicting the pavement performance is crucial for optimum pavement network management. Currently only a few models exist that incorporate the structural capacity of the pavements into deterioration modeling. This thesis develops pavement deterioration models that take into account, along with the age of the pavement, the pavement structural condition expressed in terms of the Modified Structural Index (MSI). The research found MSI to be a significant input parameter that affects the rate of deterioration of a pavement section by using the Akaike Information Criterion (AIC). The AIC method suggests that a model that includes the MSI is at least 10^21 times more likely to be closer to the true model than a model that does not include the MSI. The developed models display the average deterioration of pavement sections for specific ages and MSI values. Virginia Department of Transportation (VDOT) annually collects pavement condition data on road sections with various lengths. Due to the nature of data collection practices, many biased measurements or influential outliers exist in this data. Upon the investigation of data quality and characteristics, the models were built based on filtered and cleansed data. Following the regression models, an empirical Bayesian approach was employed to reduce the variance between observed and predicted conditions and to deliver a more accurate prediction model. / Master of Science

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