Prediction of Clean-out on Permeable Interlocking Concrete Pavers using Laboratory Experiments and Machine Learning

dc.contributor.advisorGharabaghi, Bahram
dc.contributor.authorSiwakoti, Sachet
dc.date.accessioned2019-05-16T13:55:27Z
dc.date.available2019-05-16T13:55:27Z
dc.date.copyright2019-05
dc.date.created2019-05-14
dc.date.issued2019-05-16
dc.degree.departmentSchool of Engineeringen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Applied Scienceen_US
dc.degree.programmeEngineeringen_US
dc.description.abstractA major problem with permeable interlocking concrete pavers (PICPs) is the significant cost associated with its clean-out to restore the original functionality, which is substantial and discouraging for potential users and municipalities. The current investigation employs a novel understanding of variables affecting the sustainable and economically feasible maintenance of PICP. Two new models have been derived to predict more accurately the percent mass removal from the PICPs using Artificial Neural Network (ANN) and Gene Expression Programming (GEP). Four novel non-dimensional parameters were developed using five independent variables (cleaning equipment speed over the pavement; air speed in the cleaning jets; lateral depth of the cupule, top opening width of the cupule, and filter media gradation) that affect the cleaning of the permeable pavement. The findings of this research can be applied to the industrial application of Regenerative Air Street Sweepers, which is economically feasible PICP maintenance.en_US
dc.identifier.urihttp://hdl.handle.net/10214/16114
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectlow impact developmenten_US
dc.subjectpermeable pavementen_US
dc.subjectneural network modelsen_US
dc.subjectartificial neural networken_US
dc.subjectgene expression programmingen_US
dc.subjectPICPen_US
dc.subjectclean outen_US
dc.titlePrediction of Clean-out on Permeable Interlocking Concrete Pavers using Laboratory Experiments and Machine Learningen_US
dc.typeThesisen_US

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