Arabian Journal of Geosciences (2022) 15: 898
https://doi.org/10.1007/s12517-022-10048-y
ORIGINAL PAPER
Estimation of field CBR from DCP for subgrade soils
Kundan Meshram1
Received: 17 December 2021 / Accepted: 4 April 2022 / Published online: 30 April 2022
© Saudi Society for Geosciences 2022
Abstract
The performance of pavement depends on the strength of sub-grade soil. In order to execute an effective and reliable pavement design, an accurate and representative material characterization technique is essential. Such a technique would be even more beneficial if it were simple and could be applied rapidly. In situ California bearing ratio
(CBR) value is one of the traditional methods for the strength characterization of the sub-grade soil. The dynamic
cone penetrometer (DCP) is an instrument, which can be used for rapid evaluation of the strength of soil in field
condition. DCP has been intended to improve on many of the deficiencies of systems that are manually pushed into
soil or proving materials. The instrument is relatively simple in design and operation, and operator variability is
reduced and thus correlations with soil parameters are more accurate. In the present study, an empirical correlation has been developed between DCP penetration rate and CBR value with different soils collected from different
roads. Statistical compatibility analysis has been done for comparison with different models available in literature.
Keywords DCP · CBR · Correlation · Black Cotton Soil · Yellow Soil · Moorum (lateritic soil)
Introduction
California bearing ratio (CBR) is widely used as an indicator to
measure the strength of sub-grade soils (IS2720-31, “Method
of test for soils - part 31 field determination of California bearing ratio” 1990). CBR value determined using both laboratories as well as field approaches. This measurement is costly;
moreover, the process also consumes a lot of time in laboratory.
CBR value depends on the basic index properties of a soil,
i.e., density, moisture content, liquid limit, and plastic limit.
Responsible Editor: Mohamed El-Ghali.
Highlights
• A correlation between California bearing ratio (CBR) and
dynamic cone penetrometer (DCP) penetration rate is developed.
• Three different types of soil have been taken in consideration to
help accomplish a generalized correlation.
• The correlation is compared with relations available in literature.
This article is part of the Topical Collection on Diagenesis and
Reservoir Quality within Sequence Stratigraphy.
* Kundan Meshram
kundan.meshram@ggu.ac.in
1
Guru Ghasidas Vishwavidyalaya (A Central University),
Bilaspur, Chhattisgarh 495009, India
Correlations to estimate the CBR values from the index properties were available in literature. These relations vary for different types of soil. Dynamic cone penetrometer (DCP) is a field
test used to determine the sub-grade soil strength (ASTM 2009;
Lin et al. 2019). In this method a cone is being penetrated into
the strata and rate of penetration is noted down. Many investigators have developed correlation between these two strength
parameters, viz. CBR value and DCP penetration rate (DCP
PR) for different types of soils. In this paper an attempt has been
made to develop a generalized correlation between CBR value
and DCP PR, considering three different soils, i.e., black cotton
soil, yellow soil, and moorum (lateritic soil).
Literature review
A laboratory-based empirical correlation between DCP and
CBR was developed for different soils compacted using
standard proctor procedure suggested by AASHTO with
variations in the moisture content, and it was concluded that
the correlation of DCP-CBR was independent of moisture
content (Kleyn 1975). A laboratory-based DCP-CBR correlation for clay soils, well-graded sand, and gravel was
developed in Harison (1987). It was concluded that soaking processes gave an insignificant effect on the CBR-DCP
13
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898 Page 2 of 5
Table 1 Correlations available
in literature
Arab J Geosci (2022) 15: 898
Sr. no
Correlation
−1.27
Kleyn 1975)
Livneh 1987)
Harison 1987)
Webster et al. 1992)
Livneh et al. 1473)
Transport Research
Laboratory 1993)
Coonse 1999)
Gabr et al. 2000)
George et al. 2009)
Sahoo and Reddy 2009)
CBR = 410 × DCP
log (CBR) = 2.56 – 1.16 log(DCP)
log(CBR) = 2.55 – 1.14 log(DCP)
CBR = 292 × DCP−1.12
log (CBR) = 2.45 – 1.12 log(DCP)
log(CBR) = 2.48 – 1.057 log(DCP)
Unknown
Granular and cohesive
Granular and cohesive
Granular and cohesive
-
7
8
9
10
CBR = 338.8 × DCP−1.14
log (CBR) = 1.4 – 0.55 log(DCP)
CBR = 47.32 × DCP−0.785
log (CBR) = 2.758 – 1.274 log(DCP)
Piedmont residual soil
Aggregate base course
Lateritic soils
Fine-grained soils
Study area
A total of 13 roads were selected from Madhya Pradesh,
India, for the present study. Out of these, nine roads were
in Indore district and four roads in Sheopur district. These
roads were identified as given in Table 2. Different soils
were used to construct the subgrade on these roads as
reported in Table 2.
13
Investigator
1
2
3
4
5
6
relationship. The effect of vertical confinement on DCP
for evaluating strength values for pavement and sub-grade
materials was investigated, and the study concluded that
the penetration rate was not affected by vertical confinement in sub-grade soil, but due to friction in bituminous and
granular layers, there is decrease in penetration rate with an
improvement of strength (Livneh et al. 1473). Different tests
were performed using portable falling weight deflectometer
(PFWD)-CBR, CBR-DCP PR for lateritic soils in George
et al. (2009). Table 1 enlists various relations developed by
different investigators for different types of soils.
Table 2 Details of road
identified for data collection
Type of soil
A total of 60 stations were chosen on these roads at different locations for investigation. These soils were found to
be black cotton soil, yellow soil, and moorum (lateritic soil).
Table 3 shows station ID, road ID, and chainages of the road
where DCP test was performed.
Analysis
The variation between obtained field CBR values with
DCP PR observed at different sites is shown in Fig. 1. A
set of data observed at 60 locations was considered. CBR
value decreases significantly with an increase in DCP PR
as shown in Fig. 1.
A regression analysis was carried out to develop a
correlation between the CBR value and DCP PR. The
variation as shown in Fig. 1 was represented by a power
function. A first-order power function was adopted for the
analysis. Randomly 40 data sets were used in regression
analysis for the development of correlation for three different soil types, i.e., black cotton soil, yellow soil, and
moorum.
Sr. no
District
Road ID
Name of road
Stretch (km)
Soil type
1
2
3
4
5
6
7
8
9
10
11
12
13
Indore
Indore
Indore
Indore
Indore
Sheopur
Sheopur
Sheopur
Sheopur
Indore
Indore
Indore
Indore
R-1
R-2
R-3
R-4
R-5
R-6
R-7
R-8
R-9
R-10
R-11
R-12
R-13
Baroli–Panchderia Road
Palia–Alwasa Road
HawaBunglow–Ahirkhedi Road
S.V.P. Road
Sanjay Nagar–Ringnodiya Road
RaghunathpurBaroli Road
GorasSyampur Road
BagwasKila Road
Seopur–Pali Road
PhotiKothi–HawaBunglow Road
HawaBunghlow–Ring Road
Bijasan–Gomatgiri Road
Rajendra Nagar–Sukhniwas Road
2.5
2.4
2.0
1.0
3.0
3.0
2.3
3.7
1.5
1.4
1.0
4.0
3.0
Black cotton soil
Black cotton soil
Black cotton soil
Black cotton soil
Black cotton soil
Yellow soil
Yellow soil
Yellow soil
Yellow soil
Moorum
Moorum
Moorum
Moorum
Arab J Geosci (2022) 15: 898
Table 3 Details of selected
stations
Page 3 of 5 898
Sr
no
Station
ID
Road
ID
Chainage
(m)
Sr
no
Station
ID
Road
ID
Chainage
(m)
Sr
no
Station
ID
Road
ID
Chainage
(m)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
BC-1
BC-2
BC-3
BC-4
BC-5
BC-6
BC-7
BC-8
BC-9
BC-10
BC-11
BC-12
BC-13
BC-14
BC-15
BC-16
BC-17
BC-18
BC-19
BC-20
R-1
R-1
R-1
R-1
R-1
R-2
R-2
R-2
R-2
R-3
R-3
R-3
R-3
R-4
R-4
R-5
R-5
R-5
R-5
R-5
300
800
1300
1700
2200
400
1000
1550
2200
200
650
1150
1650
300
600
400
900
1450
2000
2500
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Y-1
Y-2
Y-3
Y-4
Y-5
Y-6
Y-7
Y-8
Y-9
Y-10
Y-11
Y-12
Y-13
Y-14
Y-15
Y-16
Y-17
Y-18
Y-19
Y-20
R-6
R-6
R-6
R-6
R-6
R-6
R-7
R-7
R-7
R-7
R-7
R-8
R-8
R-8
R-8
R-8
R-8
R-9
R-9
R-9
300
800
1300
1800
2300
2800
200
700
1200
1700
2200
400
900
1500
2100
2700
3400
300
800
1300
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
M-1
M-2
M-3
M-4
M-5
M-6
M-7
M-8
M-9
M-10
M-11
M-12
M-13
M-14
M-15
M-16
M-17
M-18
M-19
M-20
R-10
R-10
R-10
R-11
R-11
R-11
R-12
R-12
R-12
R-12
R-12
R-12
R-12
R-12
R-11
R-11
R-11
R-11
R-11
R-11
300
800
1250
200
600
900
250
750
1250
1750
2250
2750
3250
3750
200
700
1200
1700
2200
2700
The following correlation was developed between DCP
PR and CBR values after performing the regression analysis
on these data.
CBR = 416 × (DCPPR)−1.253
(1)
The coefficient of determination, R2, for this relationship
was found to be 0.987. Figure 1 shows observed data with
the developed relation. The remaining 20 data was used for
the validation of the developed correlation. Standard error
and root mean square error on the validated data were found
to be 6.91% and 7.23%.
Various investigators previously developed correlation
between CBR value and DCP penetration rate for different
soils, as discussed earlier. Observed data was plotted along
with the developed relation and the relations reported by
other investigators and were shown in Fig. 2.
It is noted that the correlation developed in the present
study (firm line) was in good agreement with the observed
data (Kleyn 1975). CBR values obtained from the correlations were closer to the observed data for higher CBR values
(> 8%) (Coonse 1999; Livneh 1987; Webster et al. 1992).
10
18
Observed Value
Present Study
[7]
[8]
[5]
[9]
[13]
[12]
[2]
[3]
[4]
[11]
16
8
14
6
CBR Value (%)
Field CBR Value (%)
12
Comparison of results with previous
investigators
4
2
12
10
8
6
4
0
0
20
40
60
DCP penetration rate (mm/blow)
80
Fig. 1 Variation of DCP PR with CBR values and developed correlation
2
0
10
15
20
25
30
35
40
45
50
DCP Penetration Rate (mm/blow)
55
60
65
Fig. 2 Various available correlations with the observed data
13
898 Page 4 of 5
Table 4 Statistical comparison
with various available
correlations
Arab J Geosci (2022) 15: 898
Sr
no
a
b
R2
RMSE
% Compatibility
t-test
F-test
Investigator
1
2
3
4
5
6
7
416.00
410.00
363.08
354.81
292.00
281.83
302.00
1.253
1.270
1.160
1.140
1.120
1.120
1.057
0.975
0.959
0.842
0.759
0.944
0.962
0.422
0.067
0.095
0.199
0.235
0.149
0.127
0.330
94.409
92.897
81.378
77.605
86.484
88.402
68.070
0.950
0.534
0.071
0.024
0.324
0.534
0.000
0.938
0.603
0.500
0.380
0.816
0.614
0.231
8
9
10
11
338.80
25.12
47.32
572.80
1.140
0.550
0.785
1.274
0.850
0.108
0.074
0.611
0.202
0.621
0.785
0.233
81.245
55.507
31.678
77.263
0.072
0.000
0.000
0.016
0.599
0.000
0.000
0.051
Present study
Kleyn 1975)
Livneh 1987)
Harison 1987)
Webster et al. 1992)
Livneh et al. 1473)
Transport Research
Laboratory 1993)
Coonse 1999)
Gabr et al. 2000)
George et al. 2009)
Sahoo and Reddy 2009)
The CBR values obtained from correlation were in agreement with the observed data for lower CBR values (Gabr
et al. 2000; George et al. 2009).
A study was carried out for statistical comparison of the
formulated correlation with the studies reported in literature. As some of the investigator expressed the correlation
in logarithmic form, the same was converted to an equivalent
power function, given below:
CBR = a(DCPPR)−b
(2)
Constants of equations, i.e., a and b, R-squared value,
RMSE, percentage compatibility, t-test, and F-test, for
different relations were available in literature as shown in
Table 4.
It is noted from Table 3 that R-squared values calculated were well within an acceptable range (Kleyn 1975;
Livneh et al. 1473; Webster et al. 1992). However, RMSE,
i.e., 12.7% and 14.9%, are quite higher (Livneh et al. 1473;
Webster et al. 1992).
F-test and t-test were carried out on the CBR values
obtained from all the listed correlations. Higher “t” or “F”
value implies a better prediction for the anticipated values.
It was clearly observed that the present study given a better
correlation of DCP penetration rate and CBR values when
compared to all the other correlations.
Conclusions
1. A series of field California bearing ratio (CBR) and
dynamic cone penetrometer (DCP) tests were carried out
on road sub-grade at 60 sites. Sub-grades, at these sites,
were constructed from different materials, namely black
cotton soil, yellow soil, and moorum. It was observed that
13
DCP PR value significantly increases with a decrease in
the CBR value.
2. A correlation between CBR value and DCP penetration was developed considering all the three types of
soils. A comparative analysis was carried out to check
applicability of developed correlation, considering different correlations available in the literature. Major
point observed from this analysis was R-square, t-test,
and F-test values for present correlation show higher
agreement compared to other CBR-DCP correlations
with least root mean square error, and on comparison it
was found that present correlation is more compatible
(94.409%) with the observed data in comparison to the
available correlations.
Data Availability There is no availability of data and material.
Code availability There is no code availability.
Declarations
Conflict of interest The author declares no competing interests.
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