Procedures and Guidelines for Validating Contractor Test Data (2020) / Chapter Skim
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Pages 7-26
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From page 7... ...
7 Research Approach The research approach gave special consideration to the diverse state of the practice, current federal policy, AASHTO guidelines and standards, relevant research, recent developments in project procurement methods, modified and new alternative tests and procedures, concerns with potential bias, and practical constraints. It built upon the findings of the literature review, assessment of the state of current practice as identified through the survey of SHAs, review of selected SHAs' practices, and review of the fundamental statistics associated with procedures currently in use.
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8 Procedures and Guidelines for Validating Contractor Test Data statewide level resulted in sample sizes that were so large such that non-compliant material gets lost in the abundance of compliant results, making the statistical tests irrelevant. Wani and Gharaibeh used Monte Carlo simulation to evaluate contractor test data verification processes based on F- and t-tests in terms of the probability of detecting data manipulation and expected pay (10)
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Research Approach 9 and related pay factors. The OC curves showed that even at relatively high sampling rates (five SHA to 20 contractor test results)
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10 Procedures and Guidelines for Validating Contractor Test Data contractor data and/or inspection. This approach is becoming common with design-build and public-private partnerships.
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Research Approach 11 used to assess the reasonableness of these assumptions. When using a t-test, the data normality assumption should first be assessed (using box plots and histograms)
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12 Procedures and Guidelines for Validating Contractor Test Data of many publications (3, 4, 12)
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Research Approach 13 For unequal sample sizes, a preliminary F-test is typically applied prior to the t-test. However, Markowski and Markowski noted that the F-test is unlikely to detect many situations where the t-test should be avoided even when sampling from a normal distribution (36)
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14 Procedures and Guidelines for Validating Contractor Test Data FY 08 FY 14 Figure 4. Number of SHAs meeting high-risk QA best practices (7)
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Research Approach 15 Note: Topics 2, 3, 9, 16, and 18 have a weighting factor of 7. Topics 15 and 17 have a weighting factor of 5.
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16 Procedures and Guidelines for Validating Contractor Test Data Asphalt Concrete Mixture Of the 21 SHAs that indicated use of contractor test results for acceptance of asphalt concrete mixtures, 17 agencies provided information on the process for validating contractor test results; these are listed in Table 2. Table 2 shows that F- and t-tests are used by four SHAs (24%)
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Research Approach 17 3 3 1 1 1 3 2 1 1 3 3 2 1 7 3 3 3 1 Asphalt Concrete Mixture Portland Cement Concrete Mixture Base or Drainage Aggregate Subgrade or Embankment Reinforcing or Structural Steel 0 2 4 6 8 10 12 14 16 18 Number of SHA responses F- & t- test (independent) F- & t- test (split)
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18 Procedures and Guidelines for Validating Contractor Test Data (unequal variance t-test) , paired t-test, Mann-Whitney test, and Kolmogorov-Smirnov test.
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Research Approach 19 then calculated. The next step was generating another random sample from a second distribution with a known mean, µ2, and a known standard deviation, s2 (illustrated by the normal distribution curve on the left half of Figure 7)
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20 Procedures and Guidelines for Validating Contractor Test Data Scenario 1 Scenario 2 Scenario 3 Scenario 4 Figure 8. Numerical simulations distribution scenarios for in-place density.
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Research Approach 21 2.3.1 Data Processing SHA data were received in PDF files and spreadsheets. The PDF files were scanned test reports from multiple projects within a SHA.
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22 Procedures and Guidelines for Validating Contractor Test Data Processing of SHAs' data revealed the following observations: • Most of the SHA data were obtained using independent sampling techniques, but some data were obtained using split samples with contractors. Using split rather than independent samples can influence the acceptance and payment decisions.
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Research Approach 23 validating contractor test data as a function of SHA buyer's risk (β) , contractor seller's risk (α)
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24 Procedures and Guidelines for Validating Contractor Test Data Figure 9. Sampling and validation process – Case 1.
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Research Approach 25 Since the primary validation tests are based on independent samples, there is a potential for differences in sampling, testing, and materials variability. For example, material segregation, whether it occurs in haul vehicles or during placement or sample handling, is a common source of variability.
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26 Procedures and Guidelines for Validating Contractor Test Data contractor's data are not validated. Because the power of the paired t-test increases with the increase in sample size, use of all available split sample results for secondary validation is desired.
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