Intelligent Soil Compaction Systems (2010) / Chapter Skim
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Implementation of roller-integrated compaction monitoring technologies into earthwork specifications requires an understanding of relationships between roller MVs and soil compaction measurements. Previous studies (e.g., Floss et al.
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0 6.1 Materials and Testing 6.1.1 materials A total of 17 different soils were evaluated as part of 60 TBs for the correlation study. The soils were divided into nongranular subgrade (13 TBs)
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6.2 Simple Linear Regression Relationships Simple linear regression analysis involves developing a relationship between independent and dependent variables using an intercept and slope coefficient. This analysis has the advantage of being simple enough to perform on a hand calculator.
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situ point measurements (γ d , CBR, and E LWD-Z2 ) were obtained at roller passes 0, 1, 2, 4, and 8 at five test locations along the centerline of the roller path.
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with a Dynapac CA362 vibratory smooth drum roller at constant operation settings, with nominal A = 0.90 mm (0.035 in)
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Figure 6.3. Example 1: Average compaction curves of roller MV and in situ point measurements (top)
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Figure 6.4. Example 2: Roller MV and in situ point measurement compaction curves (top)
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Figure 6.5. Example 3: Comparison MV and in situ point measurements.
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Figure 6.6. Example 3: Compaction curves of average roller MV and in situ point measurements and simple linear regression relationships.
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Figure 6.7. Example 3: Comparison of pass 16 roller MVs with CBR profiles.
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Figure 6.9. Example 4: Influence of roller "jumping" on regression relationships, TBs MD6–MD9 (granular base material, USCS: SP-SM)
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90 RollerMV = + × + × + × + × + × + × b b b w b A b b b w 0 1 2 3 4 5 6 2 α β γ + × + ×b f b v7 8 (6.2) where b 0 = intercept; b 1 , b 2 , b 3 , b 4 , b 5 , b 6 , b 7 , and b 8 = regression coefficients; A = amplitude (mm)
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6.11, the relationships showed different trends, primarily due to differences in the A and f settings between the test beds. Multiple regression analysis was performed by combining data from different MN and FL test beds by incorporating the E LWD-Z2 , E LWD-Z3 , A, and f values as independent variables.
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Figure 6.11. Influence of amplitude and frequency on roller MVs.
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kN/m3 (105.20 lb/ft3) and 16.4%, respectively.
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Figure 6.12. Roller MVs in comparison with in situ point measurements (TB MN11)
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Figure 6.13. Simple regression relationships between roller MVs and in situ point measurements (top)
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layer measurements and therefore were removed from the model. For all the test beds considered, the frequency variation was within ±2 Hz and the speed variation was 3.0 to 5.0 km/h (1.9 to 3.1 mph)
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An attempt was made as part of this study to relate roller MVs to laboratory-determined M r . Laboratory M r tests were conducted on "undisturbed" Shelby tube (ST)
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M k P P P k k r a a d a = 1 2 3θ σ (6.4) where M r = resilient modulus; k 1 , k 2 , and k 3 = regression coefficients, typically with k 1 > 0, k 2 ≥ 0, and k 3 ≤ 0; θ = sum of principal stresses (σ 1 + σ 2 + σ 3 )
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00 Figure 6.17. Comparison of Evib and in situ compaction measurements.
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0 γ d point measurements and roller MVs, the relationships presented here to M r are also affected. This is expected as the predicted M r values are based on the measured w-γ d point measurements.
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0 table 6.11. Simple linear regression relationships between roller MVs and Mr.
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0 For a few cases, including soil moisture content in multiple regression analysis improved the regression relationships. Variations in machine vibration amplitude and frequency were also found to influence the regression relationships.
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