Clogging Prediction of Permeable Pavement
Publication: Journal of Irrigation and Drainage Engineering
Volume 142, Issue 4
Abstract
This study considers the clogging progression prediction on the permeable pavement by using artificial neural networks (ANNs). Clogging, which is caused primarily by sediment deposition, may result in performance failure of permeable pavement. Measuring the volumetric water content (VWC) by time domain reflectometers (TDRs) is an automated method to track the speed of clogging. Monitoring peak VWC during rain events has been used as an indication of clogging progression over the permeable pavement. New nonlinear solutions are developed to estimate the peak VWC using a multilayer perceptron (MLP) structure. The rain event variables and the maintenance treatment were formulated as the basic site characteristic parameters that affect the clogging progression. Five ANN models are constructed from the recorded VWC to compute the peak VWC from the rainfall parameters and maintenance treatment. A comprehensive set of data, including various rain event characteristics obtained from the rain gauge and the conducted maintenance on the permeable pavement, are used for training and testing the neural network models. The performances of the ANN models are assessed and the results demonstrate the satisfactory accuracy of the models as compared with the measured values. A parametric study is completed to determine the relative importance of peak VWC resulting from the variation of the study parameters. The results indicate that the models are effectively capable of estimating the peak VWC by the permeable pavements for different locations along the permeable pavement. The MLP models consider all known contribution factors and provide more precise prediction value than the linear model. Peak 5-min intensity, the previous rainfall depth, and the cumulative rainfall depth from the installation are the most effective parameters on the hydrologic performance of the permeable pavement. Designing permeable pavement based on the important parameters can lead to more efficient future design.
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Acknowledgments
The research is supported in part by the Louisville Jefferson County Metropolitan Sewer District. The efforts in Louisville are a result of a collaborative effort between the EPA-ORD, Louisville and Jefferson County MSD, AECOM Corporation, PARS Environmental, and the University of Louisville’s Center for Infrastructure Research.
Disclaimer
Any opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the Louisville Jefferson County MSD or the U.S. EPA; therefore, no official endorsement should be inferred. Any mention of trade names or commercial products does not constitute endorsement or recommendation for use.
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Published In
Journal of Irrigation and Drainage Engineering
Volume 142 • Issue 4 • April 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Aug 26, 2014
Accepted: Sep 1, 2015
Published online: Jan 4, 2016
Published in print: Apr 1, 2016
Discussion open until: Jun 4, 2016
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