Introduction
• Indian road network of 33 lakh Km is second largest in the
world
• Majority of the pavements are flexible type.
• Design is based upon the IRC 37 -2001
No performance prediction
No alternate design
Design upto 150 msa
• Last five years private sector involved in construction and
funding of public infrastructure work.
• DBOT (Design/Build/Own/Transfer) projects are public
infrastructure projects which employ a particular form of
structured financing.
• Many of the road projects have been awarded as DBOT
projects.
• Need for the design which is capable of
Predicting the performance
Future maintenance requirements
Identifying the cash flow for the project
Best usage locally available materials
Life cycle cost
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Testing • Four representative soil samples collected from Ahmedabad Viramgam
Maliya road project.
• The properties of soil like gradation , plasticity index, maximum dry
density and optimum moisture content ,california bearing ratio,
unconfined compressive strength, sulphate content and Ph were tested.
• The locally available soils are fine grained with medium to high
plasticity with low CBR.
Tests on soils.
1.362.47 1.46
19.31
0
5
10
15
20
25
CBR, %
Sample - 1
Sample - 2
Sample - 3
Sample - 4
1.78 1.871.71
2.19
0.00
0.50
1.00
1.50
2.00
2.50
MDD g /cc
Sample - 1 Sample - 2 Sample - 3 Sample - 4
14.70
12.50
15.20
5.80
0
2
4
6
8
10
12
14
16
18
20
OMC %
Sample - 1 Sample - 2 Sample - 3 Sample - 4
68
21
92
6152
19
70
61
82
26
54
77
25
1220 18
0
20
40
60
80
100
LL, % PL, % FSI, % SILT+CLAY, %
Sample - 1 Sample - 2 Sample - 3 Sample - 4
Observations
• The soils exhibit high plasticity with more percentage of fines and free swell
index. Three soil samples are highly plastic with plasiticity index more than 30
and fails the criteria of MORTH.
• The free swell index of the same soils are more than 50 and are higher than
allowable limits.
•The california bearing ratio of soil 4 is good and remaining 3 soils are below
3% .
•The soil requires modification and the modifiers are selected from the chart
below.
Guide for selection of binders for various plasticity index values andcontent of fines ( after AUSTROADS 1998)Source :Mix design for stabilised pavement layers AUSTROADS(2002)
Selection of binder
Soil stabilisation• Lime and chemical modifiers are selected for modification.
• Soil is modified with Lime and RBI grade 81 at 2 %, 4% , 6%, 8% to
the weight of soil.
• The unconfined compressive strength of modified soil are tested for 7
days by curing at room temperature and packing the samples with
polythene bags to prevent moisture loss.
• Resilient modulus of the modified soil is tested at the percentage of
modifier at which UCC is high .
Soil stabilisation with lime
0.00
0.20
0.40
0.60
0.80
1.00
1.20
0 2 4 6 8 10
UC
C S
tren
gth,
N/m
m2
Lime, %
UCC StrengthSample 1 Sample 2Sample 3 Sample 4
0.29
0.22
0.18 0.
23
0.53
0.40
0.27
0.70
0.55
0.46
0.23
0.96
0.57
0.66
0.23
1.00
0.52
0.70
0.22
1.14
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1 2 3 4
UC
C M
Pa
Samples
Untretaed 2% 4% 6% 8%
Soil stabilisation with RBI grade 81
0.0
1.0
2.0
3.0
4.0
0 2 4 6 8 10
UC
C M
pa
Lime %
Sample 1 Sample 2 Sample 3 Sample 4
0.29
0.22
0.18 0.23
0.26 0.
46
0.25
1.43
0.40 0.
58
0.35
2.22
0.73 0.
93
0.51
2.67
0.98 1.
22
0.63
3.43
0.0
1.0
2.0
3.0
4.0
1 2 3 4
UC
C M
pa
samples
un treated 2% 4% 6% 8%
Soil stabilisation with lime
586
117
3319
330
500
1000
1500
2000
2500
3000
3500
SO3 mg/l
Sample - 1
Sample - 2
Sample - 3
Sample - 4
8
8.5
9
9.5
10
10.5
11
11.5
0 5 10pH
Lime, %
pH
Sample 1 Sample 2
Sample 3 Sample 4
Comparision of stabilised soil
0
0.5
1
1.5
2
2.5
1 2 3 4
UC
C M
pa
Soil samples
2 %
Lime RBI
0
0.5
1
1.5
2
2.5
1 2 3 4
UC
C M
pa
Soil samples
4 %
lime RBI 81
0
0.5
1
1.5
2
2.5
3
3.5
4
1 2 3 4
UC
C M
pa
Soil samples
6 %
Lime RBI 81
0
0.5
1
1.5
2
2.5
3
3.5
4
1 2 3 4
UC
C M
pa
Soil samples
8%
Lime RBI 81
Observations
• The unconfined compressive strength of soils increased with increase of
lime content except third sample. So the soil sample 3 was tested for Ph at
varying percentages of lime and the soil is not reactive with lime.
• The unconfined compressive strength of soil is increasing with increasing
percentage of RBI grade 81. Even soil sample 3 reacted well with RBI
grade 81.
• The resilient modulus of stabilised soil are tested at 6 % of lime and RBI
grade 81
Resilient Modulus• The Resilient Modulus (MR) is a subgrade material stiffness test.
• A material's resilient modulus is actually an estimate of its modulus of
elasticity (E).
• While the modulus of elasticity is stress divided by strain for a slowly
applied load, resilient modulus is stress divided by strain for rapidly
applied loads – like those experienced by pavements.
Resilient modulus
0
50
100
150
200
0 5 10 15 20
Mr i
n M
pa
Cycles
Mr Soil 3
soil rbi
0
50
100
150
200
250
300
0 5 10 15 20
Mr i
n M
pa
Cycles
Mr Soil 2
soil lime rbi
0
50
100
150
200
0 5 10 15 20
Mr i
n M
pa
Cycles
Mr Soil 1
soil lime rbi
0
100
200
300
400
0 5 10 15 20
Mr i
n M
pa
cycles
Mr Soil 4
soil lime rbi
Design resilient modulus• The resilient modulus values are varying in each cycle and the
designed value is difficult to calculate as the modulus varying much.
• Mr value determined at a deviatoric stress of 6Psi is adequate for
design purposes (after Little,2000).
• Out of 4 soils , soil3 was not reactive with lime and it has high
sulphate contents.
• Soil 3 is not considered for design
Design resilient modulus
• The cost of RBI81 is many times the cost of Lime and the design
modulus were more or less equal. So the design was carried out with
lime stabilised soil only.
105
160
255
115
180195
0
50
100
150
200
250
300
1 2 3
Mr i
n M
pa
samples
Design resilient modulus
lime rbi
Pavement design• Pavement design involves calculating the thickness of pavement
layers. For some set of inputs like traffic loading , environmental
conditions and material properties.
• The design of the pavement is done in both empirical and Mechanistic
empirical method in the present study.
• Design as per IRC 37 2001( empirical design) is considered as basic
design.
• Mechanistic empirical design is used to develop alternate designs.
Design as per IRC 37 2001• Design inputs
– Design CBR = 5– Rolling terrain/ plain – Design traffic 150 msa– Annual traffic growth 7.5
Mechanistic Empirical Design• The mechanistic part of ME design is directed to calculating one or more
responses in the pavement structure as a function of material properties
,layer thickness and loading conditions.
• These responses must then be related to observed performance
(smoothness, deterioration, fatigue cracking etc.,).
• The tensile strain value at the bottom of bituminous layer and compressive
strain at the top of subgrade are critical strains used in these models.
• Circly software is used to calculate the critical strains in the pavement.
• Prediction models for distress to predict the performance
• The resilient modulus (Mr) for all layers except subgrade are calculated as
per IRC 37 (2001)
Design frame workENVIRONMENTAL
CONDITIONSTRAFFIC
CONDITIONSMATERIAL
PROPERTIES LAYER THICKNESS
MECHANISTIC PAVEMENT MODEL
PAVEMENT RESPONSE
PERFORMANCE PREDICTION
Damage factors
D<critical value
FINAL DESIGN
NO
YES
Inputs for M-E designTYPE OF MIX RESILIENT MODULUS
(Mpa)
POISON’S RATIO
Bituminous concrete 60/70 1270 0.35
Dense bituminous macadam
80/100
797 0.35
Wet mix macadam 250 0.40
Granular sub base 170 0.40
Stabilised subgrade type1 100 0.40
Stabilised subgrade type2 150 0.40
Stabilised subgrade type3 250 0.40
• The design is done by keeping the modulus of the layers as constant
and changing the thickness. The critical strains for each input variables
are calculated.
• The fatigue life and rutting life of the pavement are predicted from
models.
• The design developed as per IS 37 (2001) has critical strains below
the trigger value and so the both fatigue and rutting life are found to be
more than 150 msa. This critical strains are considered as bench mark.
• For the same values of critical strains the various design thicknesses
are developed by introducing the stabilized layers with resilient
modulus 100 Mpa , 150 Mpa and 250 Mpa respectively.
PREDICTION MODELS FOR DISTRESS•
Fatigue cracking prediction model:
Nf = 2.21*10-4 *(1/et)3.89 * (1/E)0.854
Where
Nf = Number of cumulative standard axles to produce 20 percent cracked
surface area
et = Tensile strain at the bottom of bituminous layer.
E = Elastic modulus of bituminous layer.
Source IRC 37(2001)
Rutting prediction model:
Nr = 4.1656*10-8*(1/ez)4.5337
Where
Nr= Number of cumulative standard axles to produce rutting of 20mm
ez = vertical subgrade strain
Source IRC 37(2001)
Roughness prediction model
IRI = 60.301* e0.096*Age
Where
IRI = International roughness index (inch/mile)
Age in years
Source : zero maintenance performance models using LTTP data
BACK
Back
•The construction cost for a typical four lane for 1 Km is calculated.
•The rates are taken as per the BOQ ( bill of quantities) of the GSRDC
road packages.
• The cost calculated includes only the pavement layers cost other costs
like kerb , earthen shoulder etc., are not considered.
•The cost of the pavement designed as per IS 37 (2001) is considered as
datum and the savings of alternate designs are calculated comparing with
that design cost.
Sl.no BC (mm) DBM (mm)
WMM (mm)
GSB (mm) SUBGRADE (mm)
MODULUS SUBGRADE (Mpa)
COST FOR Ikm croresrs
SAVINGS in rs
1 50 170 250 300 500 50 3.00 ----------
2 50 170 240 270 200 100 2.99 165495
3 50 170 240 250 200 150 2.97 361495
4 50 170 240 240 200 200 2.96 459495
5 50 170 225 240 200 250 2.93 716737.5
0
200000
400000
600000
800000
1000000
100150
200250
Savi
ngs
in R
s
Stabilised subgrade Modulus
Back
In the present study no structural maintenance is required as the pavement
is safe structural.
Functional maintenance is required to maintain pavement in good
condition.
Various maintenance strategies are developed which includes timing and
treatments.
The trigger value of UI is 2000 mm/ Km.
Six strategies are considered and in which four maintenance treatments are
used. The performance jump of each treatment are given below.
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TREATMENT PERFORMANCE JUMP
IRI inch/ Mile UI mm/Km
Thin overlay 71 1121
Micro surfacing 21 331
Surface dressing 11 174
Crack sealing 3 47
Source :
Maintenance strategy
no
Thin overlay Micro surfacing Surface dressing Crack sealing
1 FA 12 years FA 7years FA 9 years FA 4 years
2 FA 6 years
NA 6 years
------------- FA 3 years
NA 3 years
FA 2 years
NA 2 years after Thin
overlay.
3 FA 6 years
NA 6 years
------------- FA 3 years
NA 3 years after thin
HMA
Every year except at
times of other
treatments
4 ------------- FA 3 years
NA 8 years
NA 3years
FA 2 years
NA 13 years
Every year except at
times of other
treatments
5 ------------ FA 3 years
Every 3 years
13 year 14 year
6 ------------ FA 5 years
NA 5 years
NA 4 years
FA 3 years
Every 3 years
------------
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The zero maintenance model for roughness progression is used to calculate
the roughness of pavement with age.
The preventive maintenance strategies as stated above are compared with
the zero maintenance to find their effectiveness.
Effectiveness of each strategy is graphically represented.
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0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 2 4 6 8 10 12 14
UI m
m/K
m
age in years
Maintenance strategy 1
maintenance 1 zero maintenance THRESHOLDVALUE
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0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 2 4 6 8 10 12 14
UI m
m/K
m
age in years
Maintenance strategy 2
maintenance 2 zero maintenance THRESHOLDVALUE
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 2 4 6 8 10 12 14
UI m
m/k
m
age in years
Maintenance strategy 3
maintenance 3 zero maintenance THRESHOLD…
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0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 2 4 6 8 10 12 14
UI m
m/K
m
age in years
Maintenance strategy 4
maintenance 4 zero maintenance THRESHOLD…
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 2 4 6 8 10 12 14
UI m
m/K
m
age in years
Maintenance strategy 5
maintenance 5 zero maintenance THRESHOLD…
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0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 2 4 6 8 10 12 14
UI m
m/K
m
age in years
Maintenance strategy 6
maintenance 6 zero maintenance THRESHOLD…
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 2 4 6 8 10 12 14 16
UI m
m/K
m
age in years
Maintenance strategies
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• Preventive maintenance strategies are of bituminous mixes , future
prediction of the price of bitumen is a problem.
• The rise in bitumen price is not in the range of variation of consumer price
index.
• In order to predict the rise in bitumen price , analysis of the bitumen price
for past 5 year is done.
• It is found that the rise in bitumen cost is in the range of 12 to 15%.
• In the present study the future cost of the treatments are found at a rise of
12 %.
•
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• After calculating the future rate of all treatments, then they were converted
to present worth by using discounted rate of interest as 10%.
• The treatment cost is calculated for one kilometre length of a typical four
lane highway.
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0.00
0.50
1.00
1.50
2.00
12
34
56
Net
pre
sent
wor
th in
rs
cror
es
Maintenance strategies
Net present worth of maintenance strategies
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• Even though the net present worth of maintenance strategy 1 is less than
all other strategies.
• The best strategy is found by using benefit – cost analysis.
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Back
The benefit area is calculated for each strategy and the ratio of benefit
area to net present worth is calculated.
The strategy which has greater value is the best.
Benefit area for each maintenance strategy is the area between the
threshold curve, maintenance performance curve and zero maintenance
curve.
The benefit area indicates the decrease in roughness and increase in life of
the pavement.
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From the above analysis it is clear that the maintenance strategy 2 has more
benefit. So it is considered as best
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0
2000
4000
6000
8000
12
34
56
bene
fit a
rea
strategy
Benefit cost analysis
Back
In the present study only initial construction cost and maintenance costs are
considered.
As the resilient modulus of the subgrade is increasing the life cycle cost is
decreasing.
Stabilisation is not only for poor soils but also for good soils to yield
economic designs.
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sl.no Design type
Construction cost in crores Rs
Maintenance costs in crores Rs
Life cycle cost in
crores Rs
1 without SSG
3.00 1.34 4.34
2 100 Mpa SSG
2.99 1.34 4.32
3 150 Mpa SSG
2.97 1.34 4.30
4 250 Mpa SSG
2.93 1.34 4.27
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The performance of the pavement is dependent on responses ( strains).
The critical pavement responses
– Tensile strain at bottom of bituminous layer.
– Compressive strain on top of subgrade.
The thickness design procedure is based on limiting the critical pavement
responses in pavement layers.
Sensitivity analysis for pavement responses is carried out .
The input variables mostly affecting the fatigue cracking and rutting are
determined.
In the analysis each layer thickness and modulus are varied one at a time
and keeping other values to their corresponding base values.
The change in the predicted critical pavement response due to a change in a
given parameter is evaluated using the CIRCLY finite element program.
A data of 400 designs are generated to establish synthetic data base.
Pavement structure for synthetic data base generation
Input parameter Minimum Maximum Increments Base value
H1 50 mm 100 mm 10 mm 50 mm
H2 50 mm 475 mm 25 mm 170 mm
H3 50 mm 500 mm 50 mm 250 mm
H4 50 mm 500 mm 50 mm 300 mm
H5 100 mm 500 mm 100 mm 400 mm
E1 1000 Mpa 3500 Mpa 500 Mpa 1270 Mpa
E2 1000 Mpa 3000 Mpa 500 Mpa 797 Mpa
E3 100 Mpa 700 Mpa 50 Mpa 250 Mpa
E4 50 Mpa 500 Mpa 50 Mpa 170 Mpa
E5 50 Mpa 800 Mpa 50 Mpa 100 Mpa
A regression approach is adopted to develop the pavement response model
using input variables and critical responses in pavement layers.
Tensile strain modelεt = 0.00051-6.4e-7H1-1.78e-8E1-5e-7H2-3.3e-8E2-8.4e-8H3-2.8e-7E4-1.9e-
8H4-2.3e-7E4+3.64e-10H5-1.8e-8E5 ( R2 = 0.792)
Compressive strain model:
εc = 10-8(2.85e4-66H1-0.51E1-25H2-E2-16H3-5.4E3-2.6H4+10.5E4-
1.8H5-9.2E5)
A visual basic program is developed by using strain models.
The program calculates the fatigue life of the pavement, construction cost
and year of treatment required.
The inputs required are layer thicknesses , resilient modulus and traffic
data.
The program gives an idea about the performance of the pavement, cost
etc., without using the CIRCLY software.
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Stabilising the subgrade soil has great impact on the pavement
construction cost. From the present study
It is better to stabilise the good soil also because higher the modulus
lower the crust thickness. In the present study soil 4 is modified even it is
good.
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0
500000
1000000
100150
200250
Savi
ngs
in R
s
Stabilised subgrade Modulus
The thickness of the bituminous layers can be decreased by inducing higher
modulus base , sub base and stabilised subgrade courses.
Type of maintenance and timing of maintenance is more important. In the
present study the strategy 2 has right type of maintenance and timing , so it
has more benefit than other strategies.
Existing natural soil can be stabilised instead of transporting the soil from
borrow areas. Soil samples 1 and 2 are existing soils in the present study.
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As the soil properties varies greatly in a stretch , a common design for a
total stretch may not be economical. Develop alternate designs based upon
the properties of materials available in stretch.
Even though the initial construction cost of the pavement by stabilising soil
it will give more long term benefits.
Best utilisation locally available materials can be done by stabilisation
process.
Proper inspection should be ensured while stabilising is done like mixing of
additive, addition of water etc.,
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Selection of additive plays a key role in stabilisation process. Proper
preliminary investigation should be done before selecting additive.Adequate curing and proper handling of test samples like Unconfined
compressive strength , Resilient modulus is important. It will affect the test
results.
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