RESEARCH ARTICLE
Permeable Pavements Hydraulic Modelling: An Experimental Study
Simone Infante1, Mirka Mobilia1, *, Antonia Longobardi1, Mauro Albini2
Article Information
Identifiers and Pagination:
Year: 2021Volume: 15
First Page: 266
Last Page: 278
Publisher ID: TOCIEJ-15-266
DOI: 10.2174/1874149502115010266
Article History:
Received Date: 27/10/2020Revision Received Date: 31/3/2021
Acceptance Date: 6/5/2021
Electronic publication date: 30/09/2021
Collection year: 2021
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Introduction:
The changes in land use associated with urban development cause an increase in urban flooding. Low Impact Development (LID) systems help to mitigate this hazardous phenomenon.
Methods:
Among LIDs, Permeable Pavement (PP) proved to be a very effective technology in reducing surface runoff. In light of this, the present research analyzes the Retention Capacities (RC) of three different PP samples, which differ in terms of composition and percentage of bitumen and aggregates and have been realized according to Italian national regulations and technical specifications. Hydraulic laboratory tests are conducted using a rainfall simulator to quantify the Retention Capacity (RC) of the three samples in response to rainfall events with different intensities (5, 10, 20, 30 mm/h).
Results:
The values of RC range between 85% and 20%, depending on the rainfall and sample properties, confirming the high potential of PPs in reducing surface stormwater production. The accuracy of HYDRUS-1D model in simulating the surface runoff from the PP samples has been investigated. HYDRUS-1D has been calibrated using measured data of runoff from the laboratory tests and adopting NSE as an optimization criterion.
Conclusion:
The parameters sets obtained by the calibration procedure give back Nash–Sutcliffe Efficiency (NSE) values close to 1 for each PP configuration, which means a very high accuracy in model prediction. Finally, a sensitivity analysis has allowed to identify, by means of a global sensitivity index S, the most and the less influential parameters within the model, which respectively are the saturated hydraulic conductivity Ks (S=0.57) and the tortuosity coefficient L (S=0.015).
