Permeable Asphalt Hydraulic Conductivity and Particulate Matter Separation With XRT

Mariana Marchioni (), Roberto Fedele, Anita Raimondi, John Sansalone and Gianfranco Becciu
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Mariana Marchioni: Politecnico Di Milano
Roberto Fedele: Politecnico Di Milano
Anita Raimondi: Politecnico Di Milano
John Sansalone: ESSIE University of Florida
Gianfranco Becciu: Politecnico Di Milano

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 6, No 9, 1879-1895

Abstract: Abstract Permeable asphalt (PA) is a composite material with an open graded mix design that provides a pore structure facilitating stormwater infiltration. PA is often constructed as a wearing course for permeable pavements and on impervious pavements to reduce aquaplaning and noise. The pore structure of PA functions as a filter promoting particulate matter (PM) separation. The infiltrating flow characteristics are predominately dependent on pore diameter and pore interconnectivity. X-Ray microTomography (XRT) has successfully estimated these parameters that are otherwise difficult to obtain through conventional gravimetric methods. Pore structure parameters allow modeling of hydraulic conductivity (k) and filtration mechanisms; required to examine the material behavior for infiltration and PM separation. In this study, pore structure parameters were determined through XTR for three PA mixture designs. Additionally, the Kozeny-Kovàv model was implemented to estimate k. PM separation was evaluated using a pore-to-PM diameter categorical model. This filtration mechanism model was validated with data from a rainfall simulator. The filtration model provided a good correlation between measured and modeled data. The identification of filtration mechanisms and k facilitate the design and evaluation of permeable pavement systems as a best management practice (BMP) for runoff volume and peak flow as well as PM and PM-partitioned chemical separation.

Keywords: Permeable pavement; Stormwater; X-Ray microtomography; Urban drainage (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-022-03113-4

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