Khan, UsmanButler, LiamMalik, Arham2022-03-032022-03-032021-122022-03-03http://hdl.handle.net/10315/39133Permeable pavements are a type of low impact development technology that is an alternative to conventional asphalt pavements. These pavements are used to address urban stormwater runoff concerns through infiltration and storage. Overtime, sediments carried by stormwater runoff degrade the performance of these pavements and can eventually diminish the infiltration capacity to the point where no infiltration takes place. The objective of this research is to develop a data-driven model to predict the infiltration rate of permeable pavements. Four permeable concrete lab specimens were constructed and subjected to clogging cycles while obtaining surface images and infiltration data. An artificial neural network was created to investigate the relationship between the images of the pavement surface and its associated surface infiltration rate. Results indicated that image parameters do change significantly as pavements clog and are suitable as inputs to predict surface infiltration rate, although model variability needs to be addressed.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.SustainabilityElectronic Thesis or Dissertation2022-03-03Permeable pavementsPredictingPermeable concreteCloggingMaintenanceArtificial neural networkModellingImage analysisSurface infiltration ratesPerformance