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.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/50446 |
| Date | 04 September 2014 |
| Creators | Ackerman Jr, Paul J. |
| Contributors | Civil and Environmental Engineering, Young-Corbett, Deborah E., McGinnis, Sean, Fiori, Christine M., Garvin, Michael J. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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