Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 41
41 and spatial analysis tools available in GIS can be very useful Question: Do you use the contractor quality control for detecting missing sections, inconsistencies in the location data as part of the quality assurance process? of some sections, and unexpected changes in pavement con- dition. ODOT, for example, has recently begun using GIS for complementing the agency's quality acceptance procedures. Yes Not Sure Zhang and Smadi (73) present another example of the use of 30% 27% GIS to support the data collection quality management. Time-History Comparisons Approximately half of the responding agencies conduct com- No 43% parisons of the pavement data collection with existing time- series data to identify unexpected changes in the condition that may be an indication of data collection problems. Larson FIGURE 18 Percentage of respondents that use et al. (96) presents some interesting approaches for comparing service provider quality control data for quality acceptance. time-history pavement condition data. MDSHA also conducts a comparison of the current percentage of pavement sections in acceptable condition with those obtained over the past 5 years cies that contract the data collection services use the service to highlight potential data collection problems; this approach provider's quality control results as part of the quality assur- is discussed in detail in chapter five. ance process. A significant number responded that they were not using these data (43%) or were not sure (27%). The Florida DOT requires that the Ride Rating (100-point scale) be within plus or minus eight points of the previous year's More than two-thirds (69%) of the agencies that have out- survey. It is important that the data collection crew rerun the sourced at least part of the data collection indicated that it was section if a rating falls outside this range. The second run must a positive step, with only one (3%) responding that it was not not vary by more than plus or minus one ride rating point (Figure 19). The remaining agencies (28%) were not sure. from the first run. If the second measurement differs by more Furthermore, 89% of the agencies are satisfied with the perfor- than plus or minus one rating point, then additional runs are mance of the data collection service provider(s) most recently required (97). used or currently being used. However, this satisfaction is not universal; two agencies responded that they were not satisfied The LTPP distress data quality control protocols require with the performance of their service providers. that the rater analyze the images, compare them with the closest available survey (before or after) in a side-by-side plot, and resolve any differences before the distress maps are INDEPENDENT VERIFICATION shipped to the quality acceptance contractor. The contractor checks 10% of each lot, and the data undergoes a higher-order It is surprising that only a very small number of agencies use quality acceptance, which includes time-series comparison third-party verification of data as a quality management tool; and information management checks. The time-series checks 4% for quality control and 12% for quality assurance. In the plot the distress versus time with a 3-standard deviation error case of the Virginia DOT (VDOT), the use of an independent band (computed based on an average coefficient of variation consultant to review the data led to a substantial reduction obtained from variability studies), preservation treatments, and linear trend lines. A software tool, called Distress Viewer and Analysis has been developed by LTPP to assist in this Question: Overall, would you consider the outsourcing of pavement data collection a positive step in your process. The comparisons allow for identifying missed main- pavement management practices? tenance treatments or errors in the distresses identified (67). Quality Acceptance of Contracted Data Collection Not Sure This section covers some quality assurance issues that are 28% applicable only to agencies that have outsourced the data collection services. An important point is the use of the qual- Yes No 69% ity control data as part of the quality assurance process. For 3% example, an agency may chose to use the service provider quality control data on the control segments for quality assur- ance purposes, and only validate a fraction of these data dur- ing the quality acceptance checks. According to the survey FIGURE 19 Degree of satisfaction with the (Figure 18), only approximately one-third (30%) of the agen- outsourcing of pavement data collection.