Predicting Pervious Concrete Pavement Performance for Usage in Cold Climates
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Pervious Concrete Pavement (PCP) has the potential to provide significant benefits. To better understand the technical, economical, and environmental impacts of PCP, the performance must be comprehensively evaluated and quantified. Because PCP is a new material, there is no mechanism for properly quantifying its performance. In addition, the application of this technology in cold climates is limited and therefore limited in-service performance data is available. A comprehensive engineering based performance model quantifies the deterioration rate and predicts future performance. Pavement performance models are developed using a pavement condition index and extensive pavement condition databases. A pavement condition index is a value which expresses the overall condition of pavement by considering various factors such as surface distresses, structural defects, and ride quality. This research will assist pavement engineers and managers in the design, construction, and management of PCP. The review of published literature reveals that there is currently a large gap in the performance evaluation of PCP in cold climates. Neither extensive condition indices nor comprehensive performance models have been developed for PCP. This research involves development of comprehensive performance models for PCP in cold climates using laboratory and field experiments and existing available data in order to predict functionality (permeability rate) and surface distresses of PCP. This study is, furthermore, aimed at developing an extensive condition index for better management of PCP by predicting and quantifying the various types of distresses and the associated functionality of PCP with particular emphasis on cold climate usage and performance. The scope of this research is to design a comprehensive tool which is simple and cost-effective. The tool involves first defining the typical types of distresses that are occurring on PCP. This is facilitated through laboratory and field design, construction, and evaluation of two test sites located in Ontario. It also involves continuous evaluation of these sites and evaluation of several other sites in the United States. The main sources of data in this research are panel rating data and field investigations data. A panel rates the condition of PCP in terms of surface distresses and permeability rates. In addition to this, field measurements of distresses and permeability rates are obtained manually. As a result, the Pervious Concrete Condition Index (PCCI) is developed through incorporation of field measurements and panel ratings. By using regression analysis, performance models are developed between PCCI and pavement age. The performance models are validated using the data splitting technique. Ultimately, the performance models are calibrated using field data by applying the Markov Chain process (acquiring expert knowledge by distributing questionnaires) and the Bayesian technique.
Cite this version of the work
Amir Golroo (2010). Predicting Pervious Concrete Pavement Performance for Usage in Cold Climates. UWSpace. http://hdl.handle.net/10012/4934