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dc.contributor.authorLi, Tanen
dc.date.accessioned2019-02-03T07:00:40Zen
dc.date.available2019-02-03T07:00:40Zen
dc.date.issued2017-08-11en
dc.identifier.othervt_gsexam:12579en
dc.identifier.urihttp://hdl.handle.net/10919/87417en
dc.description.abstractTire-pavement interaction is a dominant noise source for passenger cars and trucks above 25 mph (40 km/h) and 43 mph (70 km/h), respectively. For the same pavement, tires with different tread pattern and construction generate noise of different levels and frequencies. In the present study, forty-two different tires were tested over a range of speeds (45-65 mph, i.e., 72-105 km/h) on a non-porous asphalt pavement (a section of U.S. Route 460, both eastbound and westbound). An On-Board Sound Intensity (OBSI) system was instrumented on the test vehicle to collect the tire noise data at both the leading and trailing edge of the tire contact patch. An optical sensor recording the once-per-revolution signal of the wheel was also installed to monitor the vehicle speed and, more importantly, to provide the data needed to perform the order tracking analysis in order to break down the tire noise into two components. These two components are: the tread pattern and the non-tread pattern noise. Based on the experimental noise data collected, two artificial neural networks (ANN) were developed to predict the tread pattern (ANN1) and the non-tread pattern noise (ANN2) components, separately. The inputs of ANN1 are the coherent tread profile spectrum and the air volume velocity spectrum calculated from the digitized 3D tread pattern. The inputs of ANN2 are the tire size and tread rubber hardness. The vehicle speed is also included as input for the two ANN's. The optimized ANN's are able to predict the tire-pavement interaction noise well for different tires on the pavement tested. Another outcome of this work is the complete literature review on Tire-Pavement Interaction Noise (TPIN), as an appendix of this dissertation and covering ~1000 references, which might be the most comprehensive compilation of this topic.en
dc.format.mediumETDen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjecttire-pavement interaction noiseen
dc.subjectnoise separationen
dc.subjectartificial neural networken
dc.subjecttread patternen
dc.subjecttire sizeen
dc.subjectrubber hardnessen
dc.subjectspeed exponenten
dc.titleTire-Pavement Interaction Noise (TPIN) Modeling Using Artificial Neural Network (ANN)en
dc.typeDissertationen
dc.contributor.departmentMechanical Engineeringen
dc.description.degreePHDen
thesis.degree.namePHDen
thesis.degree.leveldoctoralen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineMechanical Engineeringen
dc.contributor.committeechairBurdisso, Ricardo A.en
dc.contributor.committeechairSandu, Corinaen
dc.contributor.committeememberHendricks, Scott L.en
dc.contributor.committeememberTaheri, Saieden
dc.contributor.committeememberKennedy, Ronald H.en


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