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An Introduction to Continuous Compaction Control Systems
Presented by: Christopher L. Meehan
Primary Project Sponsor: Delaware DOTGraduate Student: Faraz S. Tehrani
Department of Civil & Environmental Engineering
February 18, 2010 – Presentation at DelDOT Winter Workshop
Co-Sponsors• Caterpillar, Inc.• Giles & Ransome CAT• Greggo & Ferrara, Inc.• Kessler Soils Engineering Products, Inc.• Maryland DOT• Humboldt Inc.• EDG Co.
Outline of Presentation
• Some background & operating principles of continuous compaction control systems
• Compactometer Value (CMV) & Machine Drive Power (MDP) theory
• Delaware-based soil compaction field study • In-situ test results• Roller-measured test results• Comparisons between roller data & in-situ test
results
CCC versus IC• Continuous Compaction Control (CCC) technology
continuously and instantaneously measures machine parameters that are related to the effectiveness of soil compaction, and uses these parameters to control the compaction process.
• Intelligent Compaction (IC) uses CCC data to adjust the operational behavior of the compactor in real-time, effectively optimizing the compaction process.
Equipment Used in a CCC System
A CCC Compactor in Action
Stakeless Grading - A Tangential Benefit of GPS
IC/CCC Manufacturers
Manufacturer Measurement Value
AMMANN Ks
BOMAG Evib
Caterpillar CMV and MDPGeodynamik CMV
DYNAPAC CMVIngersoll CMV
Sakai CCV
Vibration-Based Compaction Monitoring
= amplitude of the first harmonic of the acceleration response signal
= amplitude of the exciting frequency
= a constant value chosen to empirically scale the output CMV values to an easier-to-interpret range.
Compactometer Value (CMV)
)2(ˆ 0ωa
)(ˆ 0ωa
C
)(ˆ)2(ˆ
0
0
ωω
aaCCMV =
Machine Drive Power System (MDP)
Pg = gross power needed to move the machine;W = roller weight;V = roller velocity;α = slope angle; a = acceleration of the machine;g = acceleration of gravity;m and b = machine internal loss coefficients specific to a particular machine
)()(sin bmVgaWVPMDP g +−+−= α
z2θ1
θ2
α
σ
τ
RM
W
z1
CCC Field Study in the State of Delaware• Location: Burrice Borrow Pit, Odessa, Delaware• Time: July 21, 2008 to July 25, 2008
General Descriptions
• Embankment:– 61 m (200 ft) by 6 m (20 ft) embankment– Five 20 cm (8 in.) loose lift layers– Final height after compaction: 0.9 m (3.0 ft)
• Material:– Poorly-graded sand with silt (SP-SM) which conforms
to DelDOT “Select Fill” borrow specifications (Class G, Grades 5 & 6) .
Sieve Analysis Results
Equipment Utilized
Construction Equipment:• Caterpillar 980H bucket loader• Caterpillar D6K dozer• Water Truck• Caterpillar CS56 compactor
Sand Cone
Nuclear Density Gauge (NDG)
Electrical Density Gauge (EDG)
In Situ Tests Performed: Density Based Tests
300 mm Zorn LWD
200 mm Zorn LWD
Dynamic Cone Penetrometer (DCP)
GeoGauge
Falling Weight Deflectometer (FWD)
In Situ Tests Performed: Modulus Based Tests
The Results of Nuclear Density Gauge Tests
Measuring:Dry Unit Weight
Moisture Content
The Results of 1-Pt Proctor Tests
For a Standard Energy, Measuring*:Maximum Dry Unit Weight
Optimum Moisture Content
*(Determined using a family of curves approach)
NDG Degree of Compaction (All Final Passes)
88
90
92
94
96
98
100
102
104
0 10 20 30 40 50 60
Station (m)
Rela
tive
Com
pact
ion
(%)
Base Layer - 2/2
Lift 1 - 6/6
Lift 2 - 6/6
Lift 3 - 8/8
Lift 4 - 9/9
Lift 5 - 7/7
Del DOT Spec.
*Note: Relative Compaction determined using Standard Proctor test, 1-pt method, family of curves
NDG Moisture Content (All Final Passes)
2
4
6
8
10
12
14
16
0 10 20 30 40 50 60
Station (m)
NDG
ω (%
)
Base Layer - 2/2
Lift 1 - 6/6
Lift 2 - 6/6
Lift 3 - 8/8
Lift 4 - 9/9
Lift 5 - 7/7
1-pt. Opt. w (%)
NDG Dry Unit Weight (Lift 5)
17.0
17.2
17.4
17.6
17.8
18.0
18.2
18.4
18.6
18.8
19.0
0 10 20 30 40 50 60
Station (m)
NDG
γd
(kN/
m3 )
Lift 5 - 1/7
Lift 5 - 2/7
Lift 5 - 3/7
Lift 5 - 5/7
Lift 5 - 7/7
Continuous Compaction Control
Roller-Recorded Measurements of:
Location (x,y,z)Roller Speed
Machine Drive Power (MDP)Compactometer Value (CMV) Roller Measured Value (RMV)
Variation of MDP Values along the Centerline
0
5
10
15
20
25
0 10 20 30 40 50 60
Station (m)
MDP
(kW
)
Lift 5 - Pass 2 Lift 5 - Pass 3 Lift 5 - Pass 7Average 2 Average 3 Average 7
Variation of CMV Values along the Centerline
0
5
10
15
20
25
30
0 10 20 30 40 50 60
Station (m)
CMV
Lift 5 - Pass 2 Lift 5 - Pass 3 Lift 5 - Pass 7Average 2 Average 3 Average 7
Histogram of Recorded Data for Lift 5, Pass 3/7
*Note: Data included in the histogram includes all values recorded from three side-by-side roller transects
0
4
8
12
16
20
24
28
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
MDP (kW)
Freq
uenc
y (%
)
MDP Lift 5 - 3/7µ = 12.0 kWσ = 1.8 kWCV = 15.0 %
Comparison of Histograms: MDP Values
Final Passes
Lift 5, Pass 1-7
Comparison of Histograms: CMV Values
Final Passes
Lift 5, Pass 1-7
Average of CCC Values for Each Lift & Pass
Lift 5, Pass 1-7
Final Passes
Spatial Averaging of the CCC Data: Kriging Method
RMP3
RMP2
RMP1
(x1,y1,z1)
(x2,y2,z2)
(x3,y3,z3)
TP
l3
l2
l1
drum area
Comparison Between Kriged Data Points and MDP Data Trace Along the Centerline for Lift 5, Pass 7
2
4
6
8
10
12
14
16
0 10 20 30 40 50 60
Station (m)
MDP
(kW
)
Middle Lane KPµ = 8.32 σ2 = 1.77
L: R2 = 0.17 P: R2 = 0.1816.5
17.0
17.5
18.0
18.5
19.0
0 5 10 15 20MDP (kW)
NDG
γd
(kN/
m3)
Univariate Regression Analysis Results:NDG vs. CCC Data – All In-Situ Test Points
L: R2 = 0.03 P: R2 = 0.0816.5
17.0
17.5
18.0
18.5
19.0
0 10 20 30CMV
NDG
γd
(kN/
m3 )
L: R2 = 0.66
y = -0.01x2 + 0.18x + 17.72R2 = 0.82
17.6
17.8
18.0
18.2
18.4
18.6
18.8
6 8 10 12 14 16 18MDP (kW)
NDG
γd
(kN/
m3 )
Univariate Regression Analysis Results:NDG vs. CCC Data – Avg Values for Each Lift
L: R2 = 0.51
P: R2 = 0.54
17.6
17.8
18.0
18.2
18.4
18.6
18.8
10 11 12 13 14 15 16 17 18 19 20CMV
NDG
γd
(kN/
m3 )
Conclusions
• For the soil that was studied, both MDP & CMV technologies showed reasonable & consistent behavior with increasing compaction
• Effect of compaction amplitude should be taken into account for interpretation of the CMV values.
• CMV values are affected by underlying layers• Point-by-point comparisons or calibrations with in-situ test results do
not work well• Average values for a given lift correlate much more strongly with
average in-situ test results than do point-by-point comparisons• MDP showed slightly better correlation with in-situ testing
measurements than did CMV.
Thank You !
Questions?
