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Buttlar, William G. Stribling, John Wesley 2017-03-01T16:37:03Z 2017-03-01T16:37:03Z 2019-03-02T10:15:21Z 2016-12-02 2016-12 Measuring pavement roughness has been an important practice for transportation agencies for many years now. Commonly, this measurement is recorded as International Roughness Index (IRI) and is collected via a data collection vehicle (DCV) using a combination of lasers, accelerometers, and a distance measuring device. Unfortunately, this practice is cost-prohibitive to conduct on an annual basis. However, recent studies have examined the use of low-cost equipment, such as smartphones, to capture the same data and then analyze that data with in-house software. One of the primary goals with this new method is to engineer the capability to crowdsource roadway roughness data collection. This would allow asset managers to have current, and potentially real-time roughness data from which strategic decisions can be made. The challenge with this transition is to correlate roughness data collected from different sized vehicles, going different speeds, and operating in different environments to the standard “golden car” model as used in standard IRI measurements. This study builds off of previous work whereby a smartphone app was used to collect roughness data and then analyzed via an in-house MATLAB script to produce accurate IRI measurements. For this study, that same smartphone app and in-house software was used to analyze data collected from different sized vehicles traveling at different speeds. These measurements were then compared to an official IRI measurement collected just a few months prior. The results demonstrate clear and consistent sensitivity to those factors, thereby opening the door for calibration of the software to account for these variables. To further demonstrate the potential for calibrating this roughness measurement method, a simple vehicle and speed calibration was achieved and validated with further testing. This study enables further research into expanding the crowdsourcing capability not only for highway roughness measurements, but potentially also for airfield roughness measurements. Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-12-01 The student, John Stribling, accepted the attached license on 2016-11-30 at 16:15. The student, John Stribling, submitted this Thesis for approval on 2016-11-30 at 16:22. This Thesis was approved for publication on 2016-12-02 at 14:02. DSpace SAF Submission Ingestion Package generated from Vireo submission #10382 on 2017-02-28 at 14:37:08 Made available in DSpace on 2017-03-01T16:37:03Z (GMT). No. of bitstreams: 2 STRIBLING-THESIS-2016.pdf: 891308 bytes, checksum: 2e0a2a32c7556a7ebf1eba040d1f1928 (MD5) LICENSE.txt: 4211 bytes, checksum: 2750711f6e4ced4c9ef5d142f4416849 (MD5) Previous issue date: 2016-12-02 Embargo set by: Seth Robbins for item 98616 Lift date: 2019-03-01T16:37:19Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only Restriction Lifted for Item 98616 on 2019-03-02T10:15:21Z. application/pdf http://hdl.handle.net/2142/95500 en Copyright 2016 John Stribling Pavement Roughness Crowdsource Pavement Analysis Smartphone Pavement Analysis Low-cost Roughness Measurement Use of smartphones to measure pavement roughness across multiple vehicle types at different speeds text text Civil & Environmental Eng Civil Engineering University of Illinois at Urbana-Champaign Thesis M.S.