Performance Specifications for Rapid Highway Renewal (2014) / Chapter Skim
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55 A p p e n d i x C Recent developments and improvements to in situ testing devices and integrated machine sensors (e.g., intelligent compaction rollers with accelerometer-based measurements of ground stiffness) have provided opportunities to develop more performance-oriented specifications in the areas of embankment and pavement subgrade/subbase construction.
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56 results in this situation cannot be independent RICM measurements; so it is recommended that verification tests involve in situ testing conducted by the agency and correlated to the RICM MV (e.g., LWD, DCP, PLT)
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57 roller-ground interaction measurement value (MV) that correlates to the subgrade bearing capacity as determined from a static plate load test performed in accordance with ASTM 1196.
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58 • RTK-based GPS measurements showing northing, easting, and elevation (e.g., in local project coordinate system) ; • Roller travel direction (e.g., forward or reverse)
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59 Commentary: Simple linear regression analysis involves developing a relationship between independent and dependent variables using an intercept and slope coefficient. This analysis is simple enough to be performed on a hand calculator.
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60 Test Area #1 - QC Proof Map: 88% ≥ RICM-TV = 123 Test Area #2 - QC Proof Map: 0% ≥ RICM-TV = 123 CBR (800 mm Weighted Average)
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61 performance Specification for embankment and pavement Foundation Construction Commentary: This guide specification, developed under the SHRP 2 R07 project, provides a template from which a state highway agency can develop a detailed performance specification for quality control (QC) and quality assurance (QA)
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62 measurements. The point measurements have traditionally been moisture content and density determined from nuclear density tests.
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63 3.1.1 Data Collection, Export, and Onboard Display Provide and export the following data in a comma, colon, or space delimited ASCII file format: 1. Machine model, type, and serial/machine number; 2.
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64 although the magnitude of measurement error (for the range of MVs) is expected to be different for different RICM technologies.
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65 functional design properties (e.g., measures that reflect longterm repetitive loading conditions)
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66 contractor's quality management personnel and organizational structure, documentation and reporting requirements, and procedures related to nonconforming work, corrective action, and similar matters. In case such requirements are not otherwise addressed in the contract's general conditions, a sample general provision addressing quality management is included among the guide specifications developed under the SHRP 2 R07 project.
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67 7.2 Quality Compaction Performance Acceptance Options The department will assess the four primary quality factors using one of the two options described in the following: Commentary: Refer to Figure C.2 and Table C.3 for additional explanation of the two options. Performance compaction Type I: For this option, the department will use the calibrated RICM-MV maps to target locations for QA PMV testing.
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68 Commentary: For modulus/strength measurements simple linear regression analysis is generally suitable, while for correlation to dry unit weight/relative compaction measurements, multiple regression analysis including moisture content as a variable may be needed. If underlying layer support conditions are heterogeneous, relationships are likely improved by performing multiple regression analysis with RICM MV or point measurement data from underlying layers.
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69 preparation of test specimens and selected moisture contents and compaction energies. Tests are then performed to establish the CIV versus moisture content.
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70 Attachment A: Repeatability and Reproducibility Analysis Using Two-Way Analysis of Variance (AnOVA) For roller measurement values m: number of passes on a test strip; I: number of data points across the test strip; and J: change in operator, amplitude, speed, direction, etc.
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71 σreproducibility MSC ml ml MSE = + −( ) −max ,0 1I MSAC m R R reproducibility repeatabili = +σ σ σ& 2 ty2 A two-way ANOVA Table such as indicated in Table C.A.1 can be generated using any standard statistical analysis software (e.g., JMP, SPSS)
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72 Nominal Continuous Multiple pass data Figure C.A.1. Data organization in JMP for repeatability analysis of roller measurement values.
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73 MSE repeatability= σ Figure C.A.2. Repeatability analysis procedure in JMP.
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74 Nominal Continuous Multiple pass data Corresponding Speed setting Figure C.A.3. Data organization in JMP for reproducibility analysis.
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76 Portions of this attachment are reprinted from NCHRP 2009b, with permission from the Transportation Research Board. Implementation of RICM technologies into earthwork specifications requires an understanding of relationships between roller MVs and soil compaction measurements.
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77 (e.g., DCP, PLT, EFWD/ELWD) that provide information deeper than the compaction layer can help improve confidence in correlations for such conditions.
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78 linear regression model, as shown in Equation C.B.2. The statistical significance of each variable is assessed based on p- and t-values.
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79 The section of this attachment on uniformity criteria is reprinted from NCHRP 2009b, with permission from the Transportation Research Board. Uniformity Criteria Uniformity is recognized as an important component of quality compaction (e.g., Davis 1953; Sherman et al.
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80 Nugget effect (C0) : Though the value of the semivariogram at h = 0 is strictly zero, several factors, such as sampling error and very short scale variability, may cause sample values separated by extremely short distances to be quite dissimilar.
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81 References ASTM D6951-03.
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