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IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE)
e-ISSN: 2278-1684,p-ISSN: 2320-334X, Volume 17, Issue 5 Ser. III (Sep. – Oct. 2020), PP 49-61
www.iosrjournals.org
DOI: 10.9790/1684-1705034961 www.iosrjournals.org 49 | Page
Impact of Overloaded Vehicles on Flexible Pavement: Case Study
of Belhiya-Butwal Road in Nepal
Dr. P. B. Shahi1, B. B. Nepali2
1(Technical Adviser, Department of Transport Management)
2(Engineer, Department of Roads)
Abstract:
The traffic volume along major Highways is rapidly increasing due to the increase in economic growth in
Nepal. Similarly, the proportion of freight traffic is also in the growing trend. Previous studies on the axle load
survey had unveiled that significant proportion of freight vehicles are found beyond the permissible axle loads.
It has been understood that major cause of pavement failure is due to the vehicle overload. This study is aimed
at the determination of impact of axle load intensity to the pavement structure which is related to its service life
and cost of its strengthening.
The quantitative analysis technique for identifying the axle load impacts on the pavement structure at Belhiya-
Butwal Road Section has been adopted in this study. The loading pattern for each vehicle type was categorized
into the three types as fully loaded, partially loaded and empty. These loading patterns have been considered in
terms of equivalent standard axle load (ESAL). ESAL shall be properly taken into consideration during the
design of the new pavement as well as strengthening of existing.
The study has unveiled that freight vehicle loading spectrum significantly vary according to the number of axles
and wheel configuration. The front axle loads of all observed samples were found within the maximum
permissible load limit i.e. six tones. The loading pattern for rear single axle (for two-axle truck) and tandem
axle types was found significantly higher than the permissible loads. The study found that the overloaded truck-
traffic caused the reduction in the service life of the pavement by 29.6 % less than the expected design life for
the case of standard axle loading. The cost of the pavement strengthening becomes 29.73% more with the
increase in the axle load intensity. The study has raised the issues of proper considerations of ESAL during the
pavement design and the development of effective axle load control mechanism.
Keywords: Vehicle damage factor, permissible axle load, standard axle load, vehicle overloading
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Date of Submission: 10-10-2020 Date of Acceptance: 26-10-2020
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I. Introduction
Flexible types of pavements are those which have low flexural strength and wheel load is distributed
from grain of mineral components of the pavement layers. Design life (or design period) is the time from
original construction to terminal condition for a pavement structure. A terminal condition refers to a state where
the pavement needs reconstruction. Road infrastructure is used by various types of vehicles among which heavy
vehicles imposes the most critical loading, causing damage in pavement structure, which ultimately leads to an
increased maintenance and rehabilitation costs. During the design of road pavements, each type of vehicle is
converted into Equivalent Standard Axle Load (ESAL) to consider their impact on road structure [1].
During the life of pavement, various types of vehicles pass on the design lane and numerous factors
influence pavement damage. Traffic loading on road pavements is characterized by a number of different types
of vehicles with variations in load magnitude, number of axles and axle configuration. The increasing axle load
and/or total vehicle weight shortens the pavement service life and increases the departmental cost to maintain
pavement condition at an acceptable level. It is expected that the impact of overweight truck on pavement
service life is affected by pavement structure, traffic characteristics and overweight percentage. It was found
that the greater increasing of Gross Vehicle Weight (GVW) led to significant decreasing of the pavement
service life and more overlays [2]. It was also found that the effect of vehicle loads was diminished by
increasing the asphalt layer thickness and sub-grade stiffness and little effect on the impact of vehicle loads, if
the pavement distress is fatigue cracking [3].
Fatigue criteria determine its failure on pavement with sub-grade modulus otherwise its failure
criterion is based on pavement deformation. Thicker pavement has higher Equivalent Axle Load Factor (EALF)
when its failure is permanent deformation otherwise EALF is lower. Total number of standard axle load is a
parameter used for designing of a new pavement structure or showing its remaining life in service pavement
structures [4]. The pavement structure considered in the analysis includes flexible pavement and composite
Impact of Overloaded Vehicles on Flexible Pavement: Case Study of Belhiya-Butwal Road in Nepal
DOI: 10.9790/1684-1705034961 www.iosrjournals.org 50 | Page
pavement with different distribution patterns were observed between the overweight and non-overweight traffic
in terms of truck classes and axle load spectra. In general, it shows that 1% increase of overweight truck may
cause 1.8% reduction of pavement life [5].
In consideration of above phenomena, axle load is duly considered during pavement design. It is
obvious that the higher the load or pressure from the vehicle is, the need for the thicker pavement structure is
there. Though there is legal provision for maximum permissible axle load of 10.2 tonnes in Motor Vehicle and
Transport Management Regulation-1994 (MVTMR-1994). The maximum permissible axle load or GVW
should be supported with the existing axle load pattern in the country and practices adopted in the neighbouring
country, like India, to address the cross-border freight movement.
An axle load survey conducted by the Department of Roads (DoR) in 2010 has revealed that about
30% of commercial heavy vehicles are overloaded beyond the permissible axle load. Freight movements of
construction materials and industrial raw materials are main commodity causing overloading problems.
Overloading of such freight movement is quite often international from the truck entrepreneurs to get more
benefit from the trip. In this way, overloading has greater loss of the public economy due to early pavement
damage, high maintenance costs as well as it has high operating cost for service provider.
The traffic volume along the major Highways in Nepal is increasing rapidly. At the same time,
proportion of overloaded vehicles in traffic system has been significantly rising. Relatively the movement of
industrial as well as construction materials by using multi axle heavy vehicles is very common. Due to these
overloaded heavy vehicles, the road pavement is deteriorating rapidly with rutting, fatigue cracking and
potholes along the Belhiya-Butwal road section. The commodities loaded on the freight vehicles also affect the
loading pattern. Ultimately, this phenomenon made premature failure of the flexible pavement. Such pavement
failure is related to the loss of service life and increasing cost of maintenance or overlay. The first and most
important issue to be considered is the determination of loading pattern and impact of overloaded heavy
vehicles along this road section.
The main objective of the research is to determine the impact of overloaded vehicles on the service life
and cost implications of flexible pavement in Belhiya-Butwal Road. The specific objectives of the study are as:
To analyse the volume, composition and loading pattern of heavy vehicles on Belhiya-Butwal section of
road.
To analyse the impact on service life of the flexible pavement used in that road due to overloaded vehicles,
To compare the cost variances on flexible pavement due to standard and overloaded vehicles.
II. Literature Review
Vehicles are at present classified into three categories under Vehicles and Transportation Management
Act, 1993 as heavy, medium and light vehicles. This classification is based on the gross vehicle weight. The
heavy vehicles are those vehicles whose gross vehicle weight is more than 10 tonnes. It can be any vehicle with
two axles fitted with pneumatic tires like truck, bus, crane, tanker, tractor-trailer, etc. For the purpose of this
policy, it can be any construction equipment whose operating weight is more than 10 tonnes. The medium
vehicle is that category in which vehicles has GVW of more than 4 tonnes and less than 10 tonnes. Mini trucks,
buses, jeep, pickup are fall under this category. Light vehicle category includes the vehicles with the GVW of
less than 4 tonnes. Car, Jeep, Motor-Cycles, Pickups generally fall in this category.
Permissible axle loads
Maximum allowable axle load is fixed by the regulations as 10.2 tonne. Furthermore, this axle load has
been elaborated in the axle load control guidelines approved by the Ministry of Physical Infrastructure and
Transport (MoPIT) as per the number of axles and wheel configurations. The axle load regulation is
implemented by the Department of Transport Management (DoTM). Permissible axel load as per the wheel
configuration is shown in
Table 1.
Table 1: Permissible Axle Load and GVW (tonne)
S.N.
Wheel Configuration
Max GVW,t
1
Two-axle
16.2
Two tyres on front axle
6
Four tyres on rear axle
10.2
2
Three-axle
25.0
Two tyres on front axle
6
Eight tyres on rear tandem axle
19
3
Four-axle (12-wheel with rear tandem)
31.0
Front axle two tyres
6
Lift axle two tyres
6
Eight tyres on rear tandem axle
19
Impact of Overloaded Vehicles on Flexible Pavement: Case Study of Belhiya-Butwal Road in Nepal
DOI: 10.9790/1684-1705034961 www.iosrjournals.org 51 | Page
S.N.
Wheel Configuration
Max GVW,t
4
Four-axle (14-wheel with rear tridem axle)
30.0
Front-axle two-tyres
6
12-tyres on rear tridem axle
24
Source: Axle load Control Guidelines [6]
Vehicle Loading Pattern
A previous study of Thakuri [7] shows that the axle load survey data along the Mugling-Naubise
section of the busy national highway (Prithwi Highway) shows that, 20% of 2-axle trucks are overloaded.
Similarly, it was found that there is 6% of overloaded in 4-axle heavy trucks. It has been found that goods-
vehicles carry higher overloading ranges from 6% to 20% along this highway.
The global scenario shows that, traffic on the road pavement is characterized by a large number of
different vehicle types, and these can be considered in pavement design by using truck factors to transform the
damage they apply to the pavement to the damage that would be applied by a standard axle. The truck factors to
convert trucks into standard axles are defined by considering the average loads for each axle. This process
includes the vehicles that travel with axle loads above the maximum legal limit. There are also a substantial
number of overloaded vehicles in terms of total vehicle weight. These vehicles cause significant damage to the
pavements, increasing the pavement construction and rehabilitation cost. The study revealed that the presence of
overloaded vehicles can increase pavement costs by more than 100% compared to the cost of the same vehicles
with legal loads [8].
One of the defects caused by heavy traffic on the road is the deformation of the pavement surface due
to overloading that is more than the design load. Deterioration of pavements arises from deformation generally
associated with cracking under heavy commercial vehicles. The increased traffic loading will then cause
failures such as cracks and depressions on the pavement [9]. The defects that most often cause injuries to
people and damage to vehicles include inadequate road shoulders, pavement surface that is uneven, improperly
marked signs, malfunctioning stop lights, construction negligence, and municipal negligence. Traffic volume
and size (especially for overloading) contributes to road safety and conditions. Recognizing of vehicles' uses
and applications (industrial transportations) is the key for decreasing road deterioration [10].
Overloaded vehicle has a significant impact on pavement fatigue life and distress. As the studies show,
the phenomena intensify when the control of traffic is poor. Increase of percentage of overloaded vehicles from
0 to 20% can reduce the fatigue life of asphalt pavement up to 50%. The calculation of fatigue life of an asphalt
pavement structure used in Poland considering data from traffic management at Weigh in motion (WIM)
stations indicated that the decrease of percentage of over loaded vehicles by 10% may cause the increase of
service life of the pavement from 4 to 6 years [11].
Pavement Failure
The modes of failure for those material types include the fatigue of asphalt material, deformation of
granular material, crushing and effective fatigue of lightly cemented material, and deformation of selected sub-
grade material. The critical parameters and transfer functions for those material types and modes of failure are
discussed and included in the pavement life prediction process [12].
As a developing country, in Taiwan, a large number of infrastructure projects have been undertaken in
recent years. Due to these large-scale constructions, not only has the number of heavy vehicles (especially the
aggregate-hauling trailers and dump trucks) grown rapidly, but the size and weight of heavy vehicles has also
increased dramatically. These factors included a very serious truck overloading problem which significantly
affects pavement performance and bridge safety [13].
Properly specified pavement deterioration models are an important input for the efficient management
of pavement, the allocation of cost responsibilities to various vehicle classes for their use of the highway
system, and the design of pavement structures. However, most empirical deterioration progression models
developed to date have had limited success [14].
A recursive non-linear model was developed for the prediction of pavement performance as a function
of traffic characteristics, pavement structural properties, and environmental conditions. The model developed as
part of this research enables the deformation of an unbiased exponent of the so-called power law and the
equivalent loads for different axle configurations. The estimated exponent confirms the value of 4.2
traditionally used. However, it should be noted that this exponent is only to be used for determining damage in
terms of serviceability [15].
A procedure developed to estimate the remaining service life of flexible pavements is based upon
predicted ride and distress conditions. These conditions are forecasted using equations that involve measurable
values of material properties, climatic conditions and design factors. The most significant distress types
affecting pavement service life were identified using a discriminant analysis approach. For each of the prevalent
Impact of Overloaded Vehicles on Flexible Pavement: Case Study of Belhiya-Butwal Road in Nepal
DOI: 10.9790/1684-1705034961 www.iosrjournals.org 52 | Page
Texas flexible pavements the probability of needing rehabilitation is assessed for different levels of ride and
distress, using discriminant functions [16].
The existing design methods assume that the Equivalent Standard Axle Load (ESAL) are valid for all
pavement structures and do not consider the thickness and stiffness of the pavement layers. The model was
developed based on the tensile strain at the bottom of the asphalt layer that is responsible for bottom-up
cracking in asphalt pavement, which is the most widely considered distress mode for flexible road pavements.
The work developed in this study also presents the influence of the type of wheel (single and dual) on pavement
performance. The result of this work allowed the conclusion that the ESALs for single wheels are
approximately 10 times greater than those for dual wheel [17].
The Remaining Service Life (RSL) is the anticipated number of years that pavement is unacceptable
condition to accumulate enough functional or structural distress under normal conditions, given that no further
maintenance is performed. RSL is calculated from the condition of the asset during that year and the projected
number of years until rehabilitation is required. Once RSL is estimated for each pavement section in the
network, the section is grouped into different categories It combines the severity and extent of different
distresses and the rate of deterioration. It requires development of a performance model and establishment of a
threshold value for each distress type. Based on these threshold values, the current distress level and
deterioration model for each distress to reach the threshold value, can be computed. The shortest of these time
periods is the RSL of the pavement. The definition of the threshold value depends on the criteria used to control
long term network conditions. Existing methods rely on various concepts from purely empirical to truly
mechanistic. Lack of adequate preference prediction models has been the major impediment in predicting
remaining life [18].
An overloaded truck has a load or gross weight exceed their maximum legal loads. In Indonesia, main
factor behind the overloaded trucks is economic issues, for example the owner of commodities or truck owner
attempt to minimize transportation cost by carrying an overload. On the other hand, law enforcement has not
been optimal yet. Overload truck restrictions through weigh station failed to prevent it. Without further
intervention by the government, the continuous use of overload truck causes a serious problem on pavement
preservation and planning policy in Indonesia. To calculate the impact of overloaded vehicles on pavement
structure, it can be calculating the remaining service life of the pavement. Remaining Service Life (RSL) has
been defined as the estimation of total years that a pavement will be functionally and structurally in a normal
condition by only routine preservation. The RSL will be calculated using Equation 1[19].
=
Equation 1
Where RSL is remaining service life of pavement (years) and DL is design life and CESAL is Cumulative
Equivalent Single Axle Load.
AASHTO (1993) developed a model to estimate the total number of traffic during the service life by using the
following cumulative equivalent single axle loads (CESAL) by Equation 2.
=365
Equation 2
Where, Directional distribution factor (DD) is 0.375 in the case of Belhiya-Butwal Road assuming the 20% of
the total vehicles diverted in the access road. Lane distribution factor (DL) is 100%. Annual growth factor (GR)
was calculated using the formula ( =(1+)1
). The growth rate is practiced to adopt as 5 percent.
The reduction of service life could be indicated by the deviation of the pavement service life due to the different
magnitudes of traffic load that have to withstand by the pavement structure. To calculate the reduction of
service life, a relationship between traffic load and service life is possible to be developed by using the
American Association of State Highway Transport AASHTO (1993) design guide as shown in Equation 3.
=(1 + )1
Equation 3
In which, is the predicted traffic load in ESAL, is the traffic load in basic year in ESAL, is the traffic
growth rate for which type i (%) and is service life in year.
The impact of the vehicles and mainly the overloads on the pavement performance was analysed by converting
all axle loads and vehicles into a representative axle, i.e. ESAL. According to the AASHTO Guide of Pavement
Structure (1993), ESAL is the ratio between the damage of the passage of an axle on pavement and the damage
Impact of Overloaded Vehicles on Flexible Pavement: Case Study of Belhiya-Butwal Road in Nepal
DOI: 10.9790/1684-1705034961 www.iosrjournals.org 53 | Page
of a standard axle, usually the 80 KN single axle loads, passing on the same pavement. It can be presented in
Equation 4.
=
Equation 4
Where, is the actual axle load, is the standard axle, mainly with = 4, even though it is
recognized that there is no unique power value and it varies with pavement type, distress considered, failure
level and contact stresses [20]. For tandem or tridem axles, the Equation 4 is applied for all individual axles of
the axle group, meaning that for tandem axle, it is applied two times whereas for tridem axles it is applied three
times. Taking into account the type of axle, i.e. single, tandem or tridem, Language and Compilers for Parallel
Computing, LCPC (1994) proposed Equation 5, for the calculation of ESAL, which it is based on Equation 4
and added the coefficient „k‟ which is a function of the axle type (single, tandem or tridem), and „‟ is a
coefficient that is a function of the type of pavement, most importantly the pavement stiffness. The „k‟
coefficient, extracted from the French Pavement Design Guide, has been presented in Table 2.
=
Equation 5
Table 2: Values of the k and coefficients for the French method
Pavement Type
k
Single axle
Tandem axle
Tridem axle
Flexible Pavement
4
1
0.75
1.1
Rigid and Semi-rigid Pavement
12
1
12
113
Source: LCPC (1994)
Because of the effect of one load on pavement can be completely different on another pavement,
coefficient „k‟ of Equation 6 must quantify this effect. Also, the effect of a single or dual load have different
effects on the pavement, coefficient „k‟ can also be used to measure this effect. Thus, Pais & Pereira (2018)
proposed a model to calculate the coefficient „k‟ as function of the pavement composition, axle type and wheel
load based on a mechanistic analysis of an extended set of different configurations of pavements, type of axles
and wheels.
=12345(6)
Equation 6
=(3 +3
3
Equation 7
where, is the thickness of the asphalt layer (m), is the thickness of the granular layer (m), is the
stiffness of the asphalt layer (MPa), is the stiffness of the sub-grade (MPa), ALP is the Axle Load
Parameter as defined in
Table 3. The values of a1, a2, a3, a4, a5, a6 and ET (Equivalent Thickness) are given in
Table 4, where the value of is 4.
Table 3: Axle Load Parameter (ALP)
Single axle
Single wheel
Single axle
Dual wheel
Tandem axle
Single wheel
Tandem axle
Dual wheel
Tridem axle
Single wheel
Tridem axle Dual
wheel
1.0
2.0
2.7
4.1
3.8
5.2
Source: Pais and Pereira (2018)
Table 4 Constants for Equation 6
ET(m)
4
1.2
1.08E+01
-9.41E-01
6.69E-02
-2.85E-01
3.04E-01
-1.41E+00
0.992
1.2
5.20E+00
3.33E-02
1.82E-03
1.15E-01
-1.17E-01
-1.33E+00
0.975
Impact of Overloaded Vehicles on Flexible Pavement: Case Study of Belhiya-Butwal Road in Nepal
DOI: 10.9790/1684-1705034961 www.iosrjournals.org 54 | Page
Source: Pais and Pereira (2018)
The VDF is the multiplier to convert the number of commercial vehicles of different axle loads and
axle configuration to the number of standard axle load repetitions. It is defined as equivalent number of standard
axles per commercial vehicle. The VDF varies with the vehicle axle configuration, axle loading and terrain type
from region to region. The VDF is derived from axle load surveys on typical sections so as to cover various
influencing factors, such as traffic mix, mode of transportation, commodities carried, time of the year, terrain,
road conditions and degree of enforcement [21].
Table 5: Table Values of Standard VDF
Vehicle Type
VDF
Remarks
Heavy Trucks (three axle or more)
6.50
Heavy two axles
4.75
Hilly terrain 3.50
Mini trucks/tractors
1.00
Large buses
0.50
Buses
0.35
Source: DoR (2013)
Cost implication due to Overloaded Vehicles
Pais and Pereira (2018) investigated the impact of overloaded vehicles using a vehicle weight database
by examining the truck factors for different vehicle categories. The study concluded that overloaded vehicles
increase pavement damage and life-cycle costs by about 30% compared to the cost of the same vehicles with
permissible axle loads. Using the fatigue equations defined by Shell method and considering a pavement with a
granular layer with 20 cm, the researchers defined the thickness of the asphalt layer (h) as expressed in Equation
8.
() = +(log)2+
Equation 8
Where, N is the cumulative number of standard axels. The constants a, b, and c are factors depending
on the stiffness of the sub-grade and asphalt layer as given in Table 6. The value of stiffness of the asphalt layer
is the stiffness of the asphalt layer andis the stiffness of the subgrade. This equation represents the
best fit of the thickness of the asphalt layer.
Table 6: Constants used in Equation 8
Easp (MPa)
Esubg (MPa)
a
b
c
5000
20
-4.94E-01
6.63E-03
-2.79E+00
40
-2.66E-01
5.32E-03
-4.49E+00
60
-8.48E-02
4.53E-03
-5.91E+00
80
1.22E-01
3.67E-03
-7.44E+00
100
2.80E-01
3.12E-03
-8.71E+00
120
3.10E-01
3.18E-03
-9.23E+00
140
3.99E-01
2.95E-03
-1.01E+00
Source: Pais and Pereira (2018)
Thus, the study of impact caused by overloaded vehicles has been made by calculating the pavement
thickness required to support the traffic which includes the vehicles with legal (permissible) loads and
overloaded vehicles. For determining the cost increment due to the overload vehicles, the cost of the
construction is calculated both conditions of fatigue laws [8].
III. Methodology
The study adopted the method of quantitative analysis of the axle load survey data then identifying the
impact of the axle load on the flexible pavement for the Butwal-Belhiya road section.
The study has conducted the axle load survey data of 2630 vehicle was taken into the considerations.
The study area is presented in Figure 1. The most recent axle load survey data was referenced from the
Department of Transport Management (DoTM).
Impact of Overloaded Vehicles on Flexible Pavement: Case Study of Belhiya-Butwal Road in Nepal
DOI: 10.9790/1684-1705034961 www.iosrjournals.org 55 | Page
Figure 1: The Study road section
Axle load pattern:
The axle load pattern was determined from the analysis of axle load survey data which was comprised
of weights of front and rear axles (tandem/single) and the gross vehicle weight. Each vehicle has been
differentiated as the empty, partially loaded and fully loaded trucks. The axle load was compared with the
permissible axle load limits and equivalent factors were determined by the statistical analysis. On the basis of
median value of axle-weights of all trucks, ESAL values of all axles and gross vehicle weight were determined
using the combined model of AASHTO (2013) and Pais and Pereira (2018) as presented in Equation 5.
Coefficient „k‟ has been calculated by using the existing thickness of asphalt (Hasp) and granular layer
thickness (Hgra). Similarly, modulus of elasticity of asphalt (Easp), sub-grade layer (Esubg) and granular layer
(Egra) were taken from the model of Pais and Pereira (2018). Equivalent Thickness (ET) was calculated by using
the Equation 7. Then it was compared with the value of 1.2 and constants a1, a2, a3, a4, a5 and a6 were taken form
the Table 4. Then the coefficient „k‟ was calculated using the Equation 6. After the determination of the
coefficient „k‟, all the ESAL values were determined using Equation 5.
The pavement design life (DL) period was taken as 10 years. Using the values of VDF as in the Table
5, predicted traffic volume the cumulative equivalent standard axle load (CESAL) for both standard and
overloaded condition were determined using Equation 2.
The Model developed by Jihanny, Subajiyo and Hariyadi (2018) was used for the calculation of
remaining service (RSL).
For analysing the cost variances on flexible pavement due to the overloaded vehicles, thickness (h) of
overlay was calculated. The ESAL values for standard and overloaded condition were calculated using the
Equation 5. On the basis of modulus of elasticity of sub-grade (Esubg) the constants a, b and c were determined.
Using the Model developed by Pais and Pereira (2018), the thickness of overlay was calculated using Equation
8. The analysis of axle load pattern and overloading has been considered for two and three axle trucks.
IV. Results And Discussion
The results of the study are comprised of mainly breakdown of traffic flow as per the loading intensity
and loading pattern (fully, partially and empty) as well as the method of calculation of remaining service life.
Similarly, the study has unveiled the method of determining the VDF for particular type of vehicles and the cost
implications of overloaded vehicles.
Impact of Overloaded Vehicles on Flexible Pavement: Case Study of Belhiya-Butwal Road in Nepal
DOI: 10.9790/1684-1705034961 www.iosrjournals.org 56 | Page
Vehicle Composition and loading pattern
The axle load survey data of 2630 heavy vehicles was analysed for the various loading pattern and axle
configuration. The composition of the study traffic is shown in Figure 2.
Figure 2: Vehicle Composition in the study road section
In the study road-section, it has been noted that the freight vehicles are more than the passenger vehicle
due the road is connecting the cross-border trade route. Major commodity for the trucks is the industrial goods
and raw-materials imported from India. The vehicle loading condition has been separated as empty, partially
and fully loaded and overloaded for the analysis of loading pattern. The percent of empty, partially loaded and
fully loaded were found as 16.27, 18.56 and 65.17 percent respectively.
Vehicle overloading was analysed as per the axle configurations. It has been found that 11.5 % two-
axle trucks were overload on front axle. Similarly, 48.3 % of rear axles of the similar type of trucks were found
overloaded. Similarly, it has been found the 32.6 percentage of trucks were found overloaded considering with
the gross vehicle weight (GVW) limits as shown in
Table 1. In the case of three-axle trucks, the front axle and tandem axle were overloaded by 13% and
38.30% respectively. The GVW of three-axle trucks was found as 27.97% more than the permissible load limit.
Loading intensity of fully loaded, partially loaded and empty trucks on front and rear axle is shown in Figure 3.
Figure 3: Axle load intensity of two-axle trucks
Load distribution of two axle trucks was plotted in the cumulative percentile graphs as in Figure 4. It was found
that 72 percent of rear axles of the two-axle trucks were exceeded the permissible axle load of 10.2 tonne.
Impact of Overloaded Vehicles on Flexible Pavement: Case Study of Belhiya-Butwal Road in Nepal
DOI: 10.9790/1684-1705034961 www.iosrjournals.org 57 | Page
Figure 4: Rear axle load in percentile distribution of two-axle trucks
It has been noticed that the most overloaded commodity causing vehicle overload are cargo of cement, potatoes,
steel rods, urea (fertilizer), fodder, sand, bricks, wheat flour, gravel, rice, and etc.
After the statistical analysis of individual axle loads by using the coefficient „k‟ of Pais and Pereira the ESAL
factor for each group was calculated and presented in Figure 5. It has been concluded that the ESAL for two-
axle truck differs as per the loading conditions (partially or fully loaded).
Figure 5: ESAL factor distribution for two-axle trucks
The composition of the three-axle trucks is relatively low (only 7.5%). However, axle load as per the
tandem axle configurations shall be taken into considerations for the accuracy of the loading analysis. The
average of GVW data along the study road-section was found 28.61 tonne whereas the permissible limit for this
group is only 25.00 tonne. The averaged axle load distribution of three-axle trucks is presented in Figure 6.
Impact of Overloaded Vehicles on Flexible Pavement: Case Study of Belhiya-Butwal Road in Nepal
DOI: 10.9790/1684-1705034961 www.iosrjournals.org 58 | Page
Figure 6: Axle load distribution of three-axle trucks (with tandem)
The ESAL factor was calculated for all loading conditions of three-axle trucks and is presented in
Figure 7. The maximum ESAL factor for fully loaded trucks was found to be 8.028. The average ESAL factor
was found to be 8.028 which exceeded the standard limit by 6.5 [6]. The minimum ESAL 0.245 was found in
empty trucks. The ESAL of front and tandem axle was found within the standard limit but the ESAL of full
loaded trucks was found exceeding the standard limit. The ESAL factor distribution is presented in Figure 4.8.
Figure 7: ESAL distribution for three-axle trucks (with tandem)
Graphical representation in above figure shows that the front axle ESAL for 3-axle trucks was found
slightly more than the standard axle load limit, whereas the ESAL for tandem and gross axle was found
exceeding the standard limit in fully and partially loaded conditions.
Percentile distribution of tandem Axle Load:
The percentile graph of the tandem axle distribution was plotted for the analysis of percentile of
exceeding the permissible axle load limits along the study road section. The result in the graph shows that only
34% of trucks were found within legal load in terms of tandem axle and remaining 66% trucks were found
exceeding the axle load limit. The percentile graph of the tandem axle of three-axle truck is shown in Figure 8.
Impact of Overloaded Vehicles on Flexible Pavement: Case Study of Belhiya-Butwal Road in Nepal
DOI: 10.9790/1684-1705034961 www.iosrjournals.org 59 | Page
Figure 8: Tandem axle load of three-axle truck in percentile distribution
These types of truck generally carry industrial raw materials or the products such as iron-ore for cement factory,
iron beams for steel industry and the steel rods.
Impact of vehicle overload on design service life:
The base year axle load per day was calculated and projected for the 10 years of design life as
cumulative equivalent standard axle load (CESAL) by taking the growth rate of 5%. The CESAL for both
standard and overloaded condition were calculated for design period. The model developed by Jihanny, Subajio
and Hariyadi [19] was used for the calculation of remaining service life (RSL). The remaining service life
(RSL) was found as 2.04 years. This study was carried out in 2018 after three years from the completion the
construction. Hence, the reduction of the service life due to the overloaded vehicles was found as 2.96 years
from the total design life of 10 years. The projection of the standard and overloaded freight vehicles for entire
design period of 10 years is given in Figure 9.
Figure 9: Calculated CESAL for both standard and overloaded trucks
Impact on vehicle overloading on the cost of the flexible pavement:
The impact of the overloaded vehicles on the flexible pavement in terms of the overlaying cost was determined
by using the axle load survey data for calculation of the ESALs. Cumulative ESALs of all types of vehicles was
found 10.99 million for standard loading conditions and 52.62 million for overloaded conditions. Similarly, the
designed thickness was found to be 20 cm and 26 cm for standard and overloading conditions respectively.
Thereafter, the cost of vehicle overlaying was found as increased by 29.73%.
2.26
4.63
7.13
9.76
12.53
15.44
18.50
21.72
25.09
28.63
10.79
22.16
34.11
46.70
59.95
73.90
88.54
103.92
120.06
137.02
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
CESAL (number in millions)
Years
CESAL Standard
CESAL Overloaded
Impact of Overloaded Vehicles on Flexible Pavement: Case Study of Belhiya-Butwal Road in Nepal
DOI: 10.9790/1684-1705034961 www.iosrjournals.org 60 | Page
V. Conclusions And Recommendations
The study was carried out to interpret the impact of vehicle loading on the flexible pavement. The axle
load survey data of 2018 were analysed for this study was taken from the Butwal-Bhairahawa section of
Siddharth highway in Nepal. The spectrum of impact of overloading encompassed the deduction of pavement
service life and increase in the cost of construction as well as strengthening the pavement.
The loading pattern of the trucks shows that the axle load intensity is varied for each axle
configurations. Most of the front axle loads are found within the permissible limits. However, the rear axles
bear more loads and most of them are overloaded. The common reason of the vehicle overloading was found as
the type of type of freight such as construction materials and industrial products especially the cement and steel
and iron products from industrial areas.
The CESAL which is the threshold for the expected pavement life increases rapidly with increase in the
axle load intensity. Hence, it causes the reduction in service life. The study unveiled that the impact of the axle
load intensity caused the premature failure of the pavement. The pavement damaging effect is exponentially
increased with the rate of increment in the axle load. The overloaded vehicles comprising 38.44 % in the traffic
volume is reducing the service life by 49.60% of the pavement life.
The impact of the axle loads, i.e. the overloaded vehicle during the entire pavement service life, results
in the poor serviceability of the pavement. This requires the frequent repair and maintenance which is related to
the cost of the pavement. Furthermore, the thickness of the overlay for the pavement strengthening becomes
more with the increase in axle load intensity.
The reduction of service life and the increase in the cost of pavement has tremendous impact in overall
transport sector economy, which constitutes the significant portion of Gross National Product. This research
recommends that the strategy for the vehicle overloading shall be developed and implemented on the basis of as
been vehicle loading patter and axle load configurations.
Acknowledgements
Authors would like to thank to Department of Transport Management for providing the access to the report on
axle load survey and other relevant guidelines for the purpose of this study.
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