Mn/ROAD Data Mining, Evaluation and Qualification - Phase 1
Principal Investigator(s):Randal Barnes, Associate Professor, Civil, Environmental and Geo-Engineering
- Lev Khazanovich, Former Professor, Civil, Environmental and Geo-Engineering
Project summary:The database at MnROAD is one of the main products of MnROAD's first decade of operation. It contains valuable information derived from in-depth pavement research studies for cold climates. Its size and impact is comparable to that of the Long-Term Pavement Performance (LTPP) database, the premier database in the United States for pavement performance data. However, at the present time, only small portions of the data contained in the very large MnROAD database have been extracted and analyzed for various pavement-related research studies. The objective of this project was to improve effectiveness and quality of the temperature data in the MnROAD database. For this research project, a data filtering system for the MnROAD temperature database was designed and implemented. Fourteen inter-dependent quantitative tests were developed to identify and flag erroneous, questionable, or exceptional data. Four of the tests identify missing and intermittent data streams. Three of the tests analyze the time series from individual sensors and identify outliers. Three of the tests compare data streams of similar sensors; "similar" implies identical pavement type, general location, and sensor depth. The remaining four tests are summary tests that identify periods of unreliable data.
The specific analysis and quantitative results are based upon the 471,178,324 data records from 1,313 thermocouple sensors in 48 MnROAD test cells collected from 1 January 1996 through October 2007. The considered test cells include both hot mix asphalt and Portland cement concrete sections from both the Mainline and Low Volume Road.
The majority of the sensors performed very well: 714 of the 1,282 operational sensors produced reliable data more than 99 percent of the time. Only 18 of 1,282 operational sensors produce reliable data less than 50 percent of the time. Only 31 of the original 1,313 sensor were wholly non-operational. A wide variety of statistical tables and graphical representation were produced in a digital format for the considered data.
Although this project focuses on a particular set of data, the concepts and tools developed in this project are designed to be extensible to accommodate the filtering of the ongoing and future data collection efforts at MnROAD.