Mix design with low bearing capacity materials/ Misiniu projektavimas naudojant mazos laikomosios galios medziagas/ Maisijumu projektesana ar zemas nestspejas materialiem/ Segu projekteerimine madala kandevoimega materjalidega.
Dell'Acqua, Gianluca ; De Luca, Mario ; Francesca, Russo 等
1. Introduction
Rural roads, as presented in this paper, are a vital part of the
infrastructure of societies: they allow a flow of goods and services
throughout rural areas, support rural development, supply access to
local markets, help attract teachers to rural schools and encourage
rural technical support from government agencies, as well as providing a
variety of other uses and benefits. About this, for example, safety
performance of existing rural roads should be increased by targeting
investments to the highest accident concentration sections and to the
road sections with the highest accident reduction potential (Jasiuniene
et al. 2012). At the same time, however, road construction makes a
significant adverse environmental impact (Skrinskas, Domatas 2006),
modifying natural terrain, disturbing large areas, and leading to major
cultural and land use changes. Thus, roads need to be well planned, well
designed, well-constructed, and properly maintained for minimal adverse
impact and to be cost effective in the long term with acceptable
maintenance and repair costs. The Low-Volume Roads Engineering Best
Management Practices Field Guide (BMPs) written by the USDA Forest
Service and the U.S. Agency for International Development (USAID) is
applied worldwide and helps to achieve these goals (Molenaar 2007).
Until fairly recently, there has been an inevitable tendency to
rigidly apply imported specifications as "best practice"
simply because there was little alternative other than taking
unquantified risk in using untried materials and methods. However, with
the wealth of research and development work undertaken over the past 28
years, new, "localized" standards and specifications have
emerged in a number of innovative ways on the basis of quantitative
evidence. There is thus a need to find solutions and instruments that
will maximize maintenance and, in particular, make it more
cost-effective from this point of view (Dell'Acqua et al. 2011).
The value of the research and development work undertaken in Botswana in
the roads sector over the last 3 decades has been substantial (Pinard et
al. 1999). Much of this work has enabled best use to be made of the
existing local natural resources that otherwise would have been excluded
from consideration in road construction because of their nonstandard
properties. It has also made it possible to find adequate solutions to
locally prevalent engineering problems that occur because of road
construction challenges posed by the physical environment, as a result
of which substantial cost savings have been made. Therefore the use of
"low bearing capacity" as silt and clay materials represents a
remarkable advantage in the low-cost simplified protocols concerning the
roadway sector.
Berney IV and Wahl (2007), for example, have introduced a rapid
soils classification kit with compact and easily transported instruments
to provide an immediate reading for soil moisture, grain size
distribution, and plastic limit. The ability to determine the
construction requirements for soil without having to conduct laboratory
testing is essential for creating an expedient field design process. The
authors point out how in a military context a rapid soil assessment
process requires the correlation of the Proctor and CBR responses with
material properties which can be measured using field data within the
allowable time frame.
The authors produced a software program which incorporated the
numerical data generated from the soils kit, classified the soil and
performed multiple regression routines based on a statistical analysis
of a large database of soil properties to predict optimum water content
and max dry density for the soil of interest. Built-in, higher-order
regression equations allow the user to visualize complete
moisture-density curves for varying compaction energies as well as
soaked and unsoaked CBR as functions of water content for the
constructed condition of the soil. The moisture-density curve and CBR
strength represent the critical data necessary to enable contingency
design and the construction of highways and airfields.
Bloser (2007) performed a comparative analysis experiment to
provide a better understanding of wearing course aggregates. Three
different road aggregates were compared in this study. The first is 2A:
this aggregate has a max size of 2 in. (51 mm) and has relatively little
fine material (0-10% passing through a sieve ASTM 200-2 mm). The second
is DSA: it is designed to achieve max compaction density and is meant to
be used as a wearing course for unpaved roads. DSA has a max size of 1.5
in. (38 mm) and a larger percentage of fine material (10-20% passing
through a sieve ASTM 200-2 mm). Another important consideration of the
DSA specification is the strict limitations on clay or soil content. No
silt or clay may be added. The last material is DSA variation: it is
similar in gradation to DSA, but has an additional 5% due to the weight
of fine clays added to the material. These aggregates commonly used in
Pennsylvania were compared using two different placement methods for
each type of aggregate as part of a 3-year study to compare their
long-term durability and cost-effectiveness. The two methods tested were
the "dump and spread" method, known as tailgating, and the
application of aggregate by a motor paver. No significant difference in
performance was found between aggregate sections laid using a paver and
the same aggregate laid by tailgating. The driving surface aggregate was
the only aggregate of the three tested that did not show a statistically
significant change in road elevation during the 3-year course of study.
Results illustrate the importance of selecting a properly graded
aggregate containing minimal clay and soil material for use as a surface
aggregate on low-volume roads.
Molenaar (2007) described work done at the Road and Railway
Research Laboratory of Delft University of Technology on the
characterization of some tropical soils. The research work comprised
classifying swelling clays, laterites, volcanic materials such as
cinder, and locally available aggregates, as well as locally produced
bituminous binders. All materials were sieved, and the plasticity
parameters were determined. Then moisture--density relationships were
determined using Proctor tests, and the CBR of the material was
determined. Some materials were subjected to monotonic triaxial tests to
determine the cohesion and angle of internal friction. Repeated load
triaxial tests were performed on some materials to obtain information on
resilient and permanent deformation characteristics. The conclusion of
this research was that these soils are effectively categorized by means
of CBR. Nevertheless, the use of triaxial tests was highly recommended.
Furthermore, some materials originally rated as marginally suited or not
suited for use in base and sub-base courses can be upgraded, effectively
avoiding the high costs of producing and hauling high-quality materials.
Siddiki et al. (2004) have consolidated many results of research on
geotechnical applications of coal combustion by-products, foundry sand,
tire shreds, and crushed glass. These geotechnical applications suggest
that significant cost savings are attained, in addition to a positive
environmental impact by using these materials.
Ahmed and Khalid (2009) studied the use of waste and recycled
materials in pavement foundations; their analysis focused especially on
incinerator bottom ash (IBA) waste mixed with limestone at different
levels, i.e., 0%, 30%, 50%, and 80%, to produce blends for use as
pavement foundation layers. The study focused on evaluating the
resistance to the permanent deformation of IBA-limestone blends, which
is vital to prevent or minimize pavement rutting. To find out whether
IBA was suitable for use as a pavement foundation layer, they studied
its resistance using a cyclic (Amsiejus et al. 2009) triaxial test
(CTT). An experimental program was designed to investigate the influence
of plant-based enzyme treatment on the behaviour of these blends. Enzyme
addition improved permanent deformation resistance for the control
limestone blend; however, it had no noticeable effect on the IBA blends.
Since 2003, the Dept of Transportation Engineering at the
University of Naples has been conducting a large-scale research program
based on drivers behaviour on low-volume roads in Southern Italy and on
its safety (Dell'Acqua 2011; Discetti et al. 2011) and operating
management (Dell'Acqua, Russo 2011a; Dell'Acqua, Russo 2011b).
The goal of this research study is to emphasize the significance of the
recycled materials' use in the roadway mixture employing simplified
low-cost standard.
This paper intends to illustrate an easy procedure to assess the
bearing capacity of the soils employed in roadway construction by CBR
index and max dry density obtained from simple standard tests, i.e.,
Atterberg limits and grain-size distribution (GSD).The proposed
procedure also makes it possible to determine the max percentage of
material with low bearing capacity (silt and/or clay) that are added to
the material with high bearing capacity in the roadway blend to reach a
good performance, once the desired strength of the soils to be utilized
is known.
2. Data collection
The research presented here aims to illustrate a systematic and
rapid procedure to create an optimal mixture for roadway use employing
low bearing capacity and high-quality materials. Different soil types
from various quarries and digs located in southern Italy were employed
to construct the embankments, and to make up the sub-base and surface
courses of the pavement. Table 1 shows the place from which the soil
types come and the number of samples for each location. The initial
phase of laboratory testing focused on the analysis of particle size
distribution, and the designed procedure was developed starting from
some standard ASTM procedures to obtain the soil properties as shown in
Table 1. It was referred to ASTM 10, ASTM 40 and ASTM 200 sieves to
classify materials according to Highway Research Board classification.
[FIGURE 1 OMITTED]
All materials were extracted about 1.00 m below ground level and
their observed moisture content was approximately equal to 10-12%. Fig.
1 shows an example of some of the sites where the materials used for the
experiment were located.
The geological features of soil types are specified as follows:
--MT1 and MT4 materials are highly permeable and were taken from
sandy alluvium and fluvial sediment;
--MT2 material derives from alluvium with inert stony matter and
sand and grit, which reflects the intense washing away that has occurred
in the site;
--MT3 material is a silt-clay soil taken from a road works site;
--MT5 material was collected from alluvium with stony and sandy
inert matter;
--MT6 material is silt-clay soil with small amounts of sand, taken
from a road works site;
--MT7 material is from alluvium with sand, gravel, and small
amounts of medium and small silt and pebbles.
Table 1 shows the Liquid Limit (LL) and Plastic Index (PI) values
for the soil samples according to the ASTM standard requirements.
Moreover, the CBR (California Bearing Ratio) design criteria was
assessed for each soil type for optimum moisture content (OMC) as
determined using a modified AASHTO test. The soil types shown in Table 1
were employed to make different mixtures, varying their percentages.
Table 2 shows the mixture produced and the results of standard
laboratory testing for each blend as explained above; in particular, OMC
was determined using a Proctor test for max dry density (MDD).
3. Data analysis
The classification of the designed mixtures was performed using a
quality index for the mixture [I.sub.q] based on the soil classification
of the Highway Research Board that involves the particle size
distribution and the susceptibility of materials to water. The [I.sub.q]
index is expressed as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1)
where [[I.sub.q].sup.10ASTM]--the percentage of mixture passing
through the 10 ASTM sieve; [I.sub.q.sup.40ASTM]--the percentage of
mixture passing through the 40 ASTM sieve; [I.sub.q.sup.200ASTM] the
percentage of mixture passing through the 200 ASTM sieve;
[I.sub.q.sup.PI]--the Plastic Index (PI) representing the difference
between the Liquid Limit (LL) and the Plastic Limit (PL).
All indices shown in the Eq (1) were normalized according to the
following expressions:
[I.sup.i.sub.q] = [[I.sub.qi] - [I.sub.qmin]]/[[I.sub.qmax] -
[I.sub.qmin]], (2)
where [I.sub.qi]--the index to be normalized with i associated with
the 10 ASTM sieve, the 40 ASTM sieve or the 200 ASTM sieve, and i
associated with PI; [I.sub.qmax]--the max value of the index to
normalize; [I.sub.q min]--the min value of the index to normalize.
Table 3 shows the normalized values of the indices for the
experimental designed mixtures.
It is clear that [I.sub.q] values close to zero are characteristic
of poor mixtures while the index values close to four indicate a
high-quality mixture even using "low bearing capacity".
4. Calibration procedure of the CBR and MDD prediction models
Once the quality index for each mixture was assessed, a series of
linear regressions were performed to provide the users with two
predictive equations to determine the structural condition of the
blends: the first refers to the forecast CBR value and the second refers
to the MDD value.
The models were created using the statistics software STATISTICA 7.
All parameters included in the models are significant to a 95%
confidence level.
The best specification of the Ordinary-Least-Square model (OLS) of
the CBR, where one independent variable appears, has the following Eq
form:
CBR = 21.01 [I.sub.q] + 13.27. (3)
The adjusted coefficient of determination ([r.sup.2]) of the model
is 93.4%.
The best specification of the Ordinary-Least-Square model (OLS) of
MDD, where one independent variable appears, has the following Eq form:
MDD = 0.101[I.sub.q] + 1.950. (4)
The adjusted coefficient of determination ([r.sup.2]) of the model
is equal to 87.7%.
The statistical analysis of the coefficients in a CBR and MDD
prediction models is shown in Table 4.
Table 5 shows the observed CBR and MDD values for each mixture
obtained from laboratory testing, together with their predicted values
obtained by using the regression Eqs (3) and (4), respectively.
5. CBR and MDD prediction model assessment procedure
The CBR prediction model and the MDD prediction models were then
tested.
Two regression equations were applied to four soil types that were
not included in the database used to calibrate prediction models.
The materials used for the assessment procedure were located in
quarries in Southern Italy. These materials reflect the features of
those adopted in the calibration phase as shown in Table 6.
Table 7 shows the observed values of CBR and MDD for each mixture
employed during the validation procedure by means of laboratory testing
and their predictive values calculated using the regression Eqs (3) and
(4), respectively.
The procedure here presented is suitable for an [I.sub.q] index
falling within the range shown in Table 8, and for soils classified by
HRB as follows:
--A1--soils characterized by fragments of stone and sand;
--A3--soils characterized by fine sand;
--A2--sandy soils with silt and clay limited to subgroups A2-4 and
A2-5;
--A4--silty soils with LL < 40;
--A5--silty soils with LL > 40.
In the case of a single mixture (or number of mixtures less than
4), for the correct application of Eqs (1) and (2) the normalization
procedure has to refer to the range of [I.sub.q] values shown in Table
8. A simple preliminary abacus was produced in the Fig. 2.
The Fig. 2 shows how the bearing capacity of the mixture by CBR
index is quickly deduced from [I.sub.q] index. Mixtures with a high CBR
present an MDD value closer to the max value observed in Table 6 (e.g.
MDD = 2.32 g/[cm.sup.3]), while mixtures with a low CBR represent an MDD
value close to the min value observed in Table 6 (e.g. MDD = 1.94
g/[cm.sup.3]).
[FIGURE 2 OMITTED]
6. Results and conclusions
The experiment was carried out using a number of soil types from
quarries and digs in Southern Italy. The study was divided into two
phases: the first was concerned with data collection, the creation of
mixtures using a percentage of "low bearing capacity"
materials, and traditional laboratory tests of designed blends, while
the second concerned the calibration and assessment of predictor CBR and
MDD models using the index quality parameter [I.sub.q]. This is an
artificial parameter that reflects the Atterberg limits and grain size
distribution of the mixture. The procedure presented here shows a strong
linear correlation between the CBR and [I.sub.q], and MDD and [I.sub.q];
these regression equations agree to fast assess the value of CBR index
for a road mixture cutting the work time, the costs and the efforts of
the designers.
The procedure also makes it possible to quantify the percentage of
silt-clay materials that cannot generally be used in the road sector, to
be included in the road mixture so as to reach an acceptable bearing
capacity.
The two prediction models have an adjusted coefficient of
determination ([[rho].sup.2]) greater than 85% and they show the CBR
value and MDD value per mixture without laboratory testing.
The two models were then validated by comparing the predicted
values with the observed values not included in the calibration phase.
This procedure confirmed the correctness of the regression equations.
In conclusion, the practical usefulness of the procedure here
presented is the use of "low bearing capacity" materials,
coming, for example, from trench digging, in mixtures used in road
construction. During the experimental analysis presented here, it was
seen how the CBR value for silt-clay material increases from 14 to 60
when this material is added in the right quantities to alluvium and
fluvial sediment or else A1 and A3 type materials.
Therefore, the method is also particularly useful when there is a
tight budget, which is often the case in the construction of low-volume
roads. The procedure will improve as the database increases, with the
assessment of additional geotechnical parameters using more tests, not
necessarily to be carried out in the laboratory, and adjusting the
quality index [I.sub.q] to calculate the CBR indirectly, optimizing
financial/material resources and decreasing the time needed.
doi: 10.3846/bjrbe.2012.28
Acknowledgements
The authors are grateful to Michael T. Long (Chair of TRB
Low-Volume Roads Committee, Oregon D.O.T.) for his invaluable
contribution to discussion and for his constructive suggestions and
comments on the treatment data.
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Received 11 March 2011; accepted 23 May 2011
Gianluca Dell'Acqua (1), Mario De Luca (2), Russo Francesca
(3) [mail], Renato Lamberti (4)
Dept of Transportation Engineering "Luigi Tocchetti"
University of Naples "Federico II" Via Claudio 21, I-80125
Naples, Italy E-mails: (1) gianluca.dellacqua@unina.it; (2)
mario.deluca@unina.it; (3) francesca.russo2@unina.it; (4)
renato.lamberti@unina.it
Table 1. Soil types analysed and results of standard ASTM laboratory
test
Passing Passing Passing
Number 10 ASTM 40ASTM 200ASTM
of soil (2.00 (0.420 (0.074
Material types mm), mm), mm), LL,
type Site analysed % % % %
MT1 quarry 5 33.3 21.0 14.0 --
MT2 quarry 5 43.0 21.0 12.0 --
MT3 dig 6 97.7 96.2 91.8 34.4
MT4 quarry 4 21.7 12.6 8.4 18.6
MT5 quarry 6 20.0 18.0 8.3 22.7
MT6 dig 7 90.5 82.0 61.7 34.1
MT7 quarry 5 20.0 18.0 8.3 22.7
Material PI, Proctor, OMC, CBR, HRB
type % g/[cm.sup.3] % % classification
MT1 N.P. 2,30 4.90 100 [A.sub.1a]
MT2 N.P. 2.32 5.04 100 [A.sub.1a]
MT3 6.7 1.94 8.00 14.0 [A.sub.5]
MT4 2.0 2.40 6.00 83.0 [A.sub.1a]
MT5 1.0 2.20 4.20 93.1 [A.sub.1a]
MT6 5.1 2.00 11.9 5.1 [A.sub.5]
MT7 1.0 2.23 5.19 93.0 [A.sub.1a]
Note: * CBR is at the MDD moisture condition (OMC).
Table 2. Results of the standard laboratory test for designed
mixtures
Passing Passing
Passing 40ASTM 200ASTM
10 ASTM (0.420 (0.074
(2.00 mm), mm), mm), LL, PI,
% % % % %
50%MT3+25%MT2+25%MT1 65.5 50.9 34.8 23.1 5.5
55%MT3+25%MT2+20%MT1 69.1 54.0 39.0 22.9 5.5
60%MT3+20%MT2+20%MT1 73.8 66.1 60.2 27.2 5.8
80%MT1+20%MT2 86.7 81.0 75.0 28.2 6.0
85%MT3+15MT2 89.4 84.0 79.0 29.0 6.3
90%MT3+10%MT2 92.2 88.6 83.2 31.3 6.4
55%MT4+35%MT5+10%MT7 22.0 14.0 8.00 17.0 2.0
45%MT4+45%MT5+10%MT7 20.3 12.4 6.60 16.1 1.1
35%MT4+35%MT5+30%MT7 20.0 10.0 7.00 17.7 2.5
42%MT4+42%MT5+165MT7 33.4 23.6 10.2 22.5 4.3
MDD, OMC, CBR, * HRB
g/cm % % classification
50%MT3+25%MT2+25%MT1 2.11 5.2 69.0 [A.sub.4]
55%MT3+25%MT2+20%MT1 2.09 5.5 52.0 [A.sub.4]
60%MT3+20%MT2+20%MT1 2.06 5.9 17.7 [A.sub.4]
80%MT1+20%MT2 2.01 6.1 43.0 [A.sub.4]
85%MT3+15MT2 2.02 6.3 30.0 [A.sub.4]
90%MT3+10%MT2 2.01 6.6 17.3 [A.sub.4]
55%MT4+35%MT5+10%MT7 2.40 5.2 76.1 [A.sub.1a]
45%MT4+45%MT5+10%MT7 2.40 5.4 95.0 [A.sub.1a]
35%MT4+35%MT5+30%MT7 2.30 5.4 88.0 [A.sub.1a]
42%MT4+42%MT5+165MT7 2.30 6.0 50.0 [A.sub.1a]
Note: * CBR is at the MDD moisture condition (OMC).
Table 3. [I.sub.q] index of designed mixtures to assess
soil mechanical quality
Mixture [I.sub.q.sup.10ASTM] [I.sub.q.sup.40ASTM]
100%MT1 0.171 0.128
100%MT2 0.296 0.128
100%MT3 1.000 1.000
50%MT3+25%MT2+25%MT1 0.586 0.474
55%MT3+25%MT2+20%MT1 0.632 0.510
60%MT3+20%MT2+20%MT1 0.692 0.651
80%MT1+20%MT2 0.858 0.824
85MT3+15MT2 0.893 0.858
90%MT3+10%MT2 0.929 0.912
100%MT4 0.022 0.030
100%MT5 0.000 0.093
100%MT6 0.907 0.835
100%MT7 0.000 0.093
55%MT4+35%MT5+10%MT7 0.026 0.046
45%MT4+45%MT5+10%MT7 0.004 0.028
35%MT4+35%MT5+30%MT7 0.000 0.000
42%MT4+42%MT5+165MT7 0.172 0.158
Mixture [I.sub.g.sup.200ASTM] [I.sub.q.sup.PI]
100%MT1 0.087 0.000
100%MT2 0.063 0.000
100%MT3 1.000 1.000
50%MT3+25%MT2+25%MT1 0.331 0.815
55%MT3+25%MT2+20%MT1 0.380 0.827
60%MT3+20%MT2+20%MT1 0.629 0.860
80%MT1+20%MT2 0.803 0.894
85MT3+15MT2 0.850 0.939
90%MT3+10%MT2 0.899 0.951
100%MT4 0.021 0.298
100%MT5 0.020 0.149
100%MT6 0.647 0.894
100%MT7 0.020 0.149
55%MT4+35%MT5+10%MT7 0.016 0.298
45%MT4+45%MT5+10%MT7 0.000 0.164
35%MT4+35%MT5+30%MT7 0.005 0.373
42%MT4+42%MT5+165MT7 0.042 0.641
Mixture [I.sub.q]
100%MT1 3.61
100%MT2 3.51
100%MT3 0.00
50%MT3+25%MT2+25%MT1 1.79
55%MT3+25%MT2+20%MT1 1.65
60%MT3+20%MT2+20%MT1 1.17
80%MT1+20%MT2 0.62
85MT3+15MT2 0.46
90%MT3+10%MT2 0.31
100%MT4 3.63
100%MT5 3.74
100%MT6 0.72
100%MT7 3.74
55%MT4+35%MT5+10%MT7 3.61
45%MT4+45%MT5+10%MT7 3.80
35%MT4+35%MT5+30%MT7 3.62
42%MT4+42%MT5+165MT7 2.99
Table 4. The statistical value of the coefficients
in the prediction CBR and MDD model
Prediction model Symbol Coefficient Standard deviation
Constant 13.27 3.907
CBR [I.sub.q] 21.01 1.443
Constant 1.950 0.027
MDD [I.sub.q] 0.101 0.010
Prediction model t-student Significance
3.399 0.0396
CBR 14.556 < 0.01
73.499 < 0.01
MDD 10.333 < 0.01
Table 5. Experimental and laboratory measurements
for the CBR and MDD values
Laboratory
Predicted CBR measurement
Mixture values of CBR value
100%MT1 89.2 100.0
100%MT2 87.1 100.0
100%MT3 13.3 14.0
50%MT3+25%MT2+25%MT1 51.0 60.0
55%MT3+25%MT2+20%MT1 47.9 52.0
60%MT3+20%MT2+20%MT1 37.8 23.0
80%MT1+20%MT2 26.3 35.0
85MT3+15MT2 22.9 28.0
90%MT3+10%MT2 19.8 17.3
100%MT4 89.5 83.0
100%MT5 91.8 93.1
100%MT6 28.3 22.0
100%MT7 91.8 93.0
55%MT4+35%MT5+10%MT7 89.2 76.1
45%MT4+45%MT5+10%MT7 93.2 95.0
35%MT4+35%MT5+30%MT7 89.4 88.0
42%MT4+42%MT5+165MT7 76.0 65.0
Laboratory
Predicted MDD measurement
Mixture value of MDD value
100%MT1 2.32 2.30
100%MT2 2.30 2.32
100%MT3 1.95 1.94
50%MT3+25%MT2+25%MT1 2.13 2.11
55%MT3+25%MT2+20%MT1 2.12 2.09
60%MT3+20%MT2+20%MT1 2.07 2.06
80%MT1+20%MT2 2.01 2.01
85MT3+15MT2 2.00 2.02
90%MT3+10%MT2 1.98 2.01
100%MT4 2.32 2.40
100%MT5 2.33 2.20
100%MT6 2.02 2.00
100%MT7 2.33 2.23
55%MT4+35%MT5+10%MT7 2.31 2.40
45%MT4+45%MT5+10%MT7 2.33 2.40
35%MT4+35%MT5+30%MT7 2.32 2.30
42%MT4+42%MT5+165MT7 2.25 2.30
Table 6. Features of materials adopted in the assessment procedure
Passing Passing Passing
througwh through through
10ASTM 40ASTM 200ASTM
sieve sieve sieve
(2.00 mm), (0.420 mm), (0.074 mm),
Mixture % % %
MTA 28.0 18.2 12.2
MTB 44.0 24.1 15.9
MTC 90.0 87.0 85.0
MTD 60.1 48.0 32.2
IP, CBR, MC, MDD,
Mixture % % % g/[cm.sup.3]
MTA NP 90.5 5.2 2.28
MTB NP 96.7 5.1 2.32
MTC 5.2 16.0 6.9 1.95
MTD 4.4 55.0 4.9 2.11
Table 7. Experimental measures of CBR and MDD values
for the validation mixtures
Predicted CBR Experimental measurement
Mixture measurement of CBR value
MTA 97.3 90.5
MTB 88.9 96.7
MTC 13.3 16.0
MTD 53.8 55.0
Predicted MDD Experimental measurement
Mixture value of MDD value
MTA 2.35 2.28
MTB 2.31 2.32
MTC 1.95 1.95
MTD 2.14 2.11
Table 8. Range of [I.sub.q] according to normalization procedure
Symbol Max value Min value
[I.sub.q.sup.10ASTM] 97.7 20.0
[I.sub.q.sup.40ASTM] 96.2 10.0
[I.sub.q.sup.200ASTM] 91.8 6.6
[I.sub.q.sup.PI] 6.71 0.0
