Article in Transportation Research Record Journal of the Transportation Research Board · July 2015
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Ernesto Ferreira Nobre Júnior
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Prata3, Ernesto Ferreira Nobre Júnior4 9
10
1
Engineer, MSc 11
Phone: +5584 88298327 12
eemoreira@gmail.com 13
Transportation Infrastructure National Department 14
15
2
Graduate Student 16
Phone: +5585 87586203 17
edjanesoares@det.ufc.br 18
Universidade Federal do Ceará 19
Campus do Pici - Bloco 703 - CEP 60455-760 - Fortaleza – CE – Brasil 20
CORRESPONDING AUTHOR 21
22
3
Assistant Professor, PhD 23
Phone: +5585 33669607 24
baprata@ufc.br 25
Universidade Federal do Ceará 26
Campus do Pici - Bloco 703 - CEP 60455-760 - Fortaleza – CE - Brasil 27
28
4
Phone: +5585 33669488 (r.223) 1
nobre@ufc.br 2
Universidade Federal do Ceará 3
Campus do Pici - Bloco 703 - CEP 60455-760 - Fortaleza – CE – Brasil 4
5
6
7
ABSTRACT
8
This paper presents an innovative model for the assessment of surface conditions in low volume roads. The
9
proposed approach focuses on intemperate weather influence (rain, wind, etc.), traffic, soil type and relief in the
10
reduction of unpaved road level of service. It treats on the evolution of distress present in unpaved roads. It intends
11
to improve the performance evaluation methodology for surface conditions proposed by (1), the ALYNO Method. A
12
case study is presented for the municipality of Aquiraz, Ceará, Brazil. A mathematical model was developed in
13
order to forecast a performance evaluation model using a topographical survey of unpaved roads, which resulted in
14
the modified method entitled ALYNOMO. Main performance evaluation methods of surface conditions for unpaved
15
roads are also studied. From the case study, several considerations are made on the geometric evolution of the
16
distress according to usefulness of the studied branch. This research contributes for the improvement of the unpaved
17
performance evaluation process and the ALYNO Method, proposing a model to forecast the performance for regular
18
and appropriate unpaved roads. It is important to emphasize the need of the rational maintenance of this kind of
19
road.
20
1. INTRODUCTION
21
2
22
23
The authors (2) call attention to the fact that transportation systems do not last forever. It 24
is due to lack of attention to the roads, paved or not, that in developing countries the 25
precariousness and inefficiency of transport systems represent a major obstacle to its 26
development. After all, without a transportation system that fits with local and regional 27
characteristics, there will be an increase in the cost of transport and consequently, agricultural 28
and industrial products will become more expensive, making them less attractive to consumer 29
In Ceará, 90% of the road network is unpaved, and most of it is under the jurisdiction of 1
the 184 counties that do not have a pavement management system. This situation creates great 2
difficulties for the flow of inputs and products of rural areas, which reinforces and justifies the 3
need for studies in this field of knowledge. 4
It is worth pointing out that while the budgetary reality of many municipalities is 5
insecure, the abandonment of unpaved roads inevitably lead to higher costs when the necessary 6
rehabilitation is carried out to improve their functionality, mainly because the degradation 7
process of a road grows exponentially with time, and consequently, with the necessary resources 8
for their rehabilitation, a factor that increases the Logistics costs in Brazil. 9
The ALYNO method developed by (1) was used in this research, but improvements were 10
made and a model of performance prediction as to the usefulness of unpaved roads was 11
proposed, called ALYNOMO method. 12
In order to accomplish the development of efficient methods for the classification of 13
surface condition in low volume roads, several approaches have been reported in the literature. 14
These methods can be classified into objective and subjective approaches. The latter have 15
reached a greater reception from the planners and decision makers because the subjective 16
methods consider the calculation of condition indexes as a way to measure the overall quality of 17
a rolling surface. 18
Among the subjective methods, one can highlight the proposed approach developed by 19
(3). Among the objective methods developed for pavement management systems for low volume 20
roads, one can highpoint the proposed methodology by (4), which is included in the technical 21
manual by the U. S. Army Corps of Engineers – USACE (5 and 6), as well as the Gravel-22
PAVER system, developed by the Wisconsin University (7). 23
A comparison between the method used by USACE and the ALYNOMO is presented by 24
(8). This method is applied in three stretches in the city of Aquiraz, Ceará, Brazil. As 25
conclusions, one can observe that, despite the high level of complexity of the ALYNOMO 26
method for the surveying of the running surface conditions, such method proved to be closer to 27
reality. 28
This paper aims at presenting an innovative evaluation method for surface conditions for 29
history of surface degradation of the studied segment considering hidrology, traffic, geotechnics 1
and topography. 2
The rest of this paper is structured as follows. In the second section, the proposed 3
approach is presented. In the third section, the new method is applied and the results are 4
presented, analyzed and discussed. The fourth section presents some conclusions and suggestions 5
for future research. 6
2. PROPOSED APPROACH
7
3
8
There is not a standard method for assessing and classifying unpaved roads., in the case 9
study of this research, the method proposed by (1), called ALYNO method was adopted. Based 10
on high precision topographic surveys, the method proved to be easy to use and understand. This 11
method presented the rating of the pavement surface conditions which showed to be easily done 12
in the region of Aquiraz. However, some modifications were necessary, without loss of precision 13
in it. Thus, the ALYNOMO method was created. 14
These adaptations occurred mainly in the isolation of variables of geometric conditions 15
and occurrences of geotechnical materials, leading to the inclusion of surveys of geotechnical 16
data and the definition of topographic sections in the study area. When calculating the density 17
and severity of the registered distresses, the procedure of ALYNOMO method is the same of the 18
ALYNO method. The defects considered in this study and observed in the monitored sections are 19
those used in (1). 20
It is important to note that in the ALYNO method, the final results are calculated from the 21
resulting function, i.e., from the influence of the combined action on all the factors that lead to 22
degradation of the usefulness of the road, while the final result of the ALYNOMO method is 23
calculated based on the types of materials found and relief of the study area, as shown in the 24
following items: 25
a) The definition of the sections is based on the geotechnical classification of the different 26
soil types found, according to their characteristics and the subsequent creation of 27
geotechnical zones (GZ), as shown in Figure 1. With the creation of GZ's, the variable of 28
b) There is also a subdivision of GZ's. This subdivision is based on the sharp variation of 1
the longitudinal ramps of the road axis, thus creating the topographical zones (TZ). Each 2
TZ features a unique value of longitudinal ramp, accepting little variations, for more or 3
for less. So each TZ is studied as a separate section, with straight length depending on the 4
length of the longitudinal ramp considered, in other words, there is great variability in 5
length between the TZ's. With the creation of TZ's, the topographical variable 6
(longitudinal ramp of the road axis) also becomes constant throughout the length of each 7
TZ. 8
c) cadastral topographic surveying with millimeter precision is obtained from a total 9
station. The topographic survey is performed in the analyzed area and in places in which 10
the distresses were found. 11
d) Classification of distresses found, according the type and georeferenced location 12
within each TZ. 13
e) A vector representation of all sections' TZ's with their respective distresses for 14
subsequent generation of Digital Terrain Model (DTM) and Digital Model of Distresses 15
(DMD) is accomplished. 16
f) The measurement of the areas of distresses found is done by means of a topographic 17
app, through the area calculation for georeferenced coordinates (Gauss method). The 18
calculation of the depth of such distresses is given by simply counting the contour lines 19
inserted into the distress area. These contour lines are all spaced one centimeter apart, 20
facilitating the assignment of values to the Level of Individual Severity (LIS). This value 21
is found depending on the type of distress adopted. The attributes of severity "Low", 22
"Medium" and "High" correspond to the values 1, 2 and 3, respectively. 23
g)Once found the LIS of each type of distress in topographic zone, it is time to calculate 24
the average for the type of distress. This average is the Average Severity in a 25
topographical zone (AS). This value also varies from 0 (zero) to 3 (three). 26
4
27
h) Then, it is time to calculate the Relative Surface Density of each Distress of 28
type of distress divided by the total area or total length of the topographical zone where 1
the distresses are inserted. The RS is calculated for each type of defect. 2
i) Once the values for AS and RS for each type of defect are found, they are multiplied to 3
provide an indicative value of the relative severity of distresses in a topographical area. 4
This value is expressed in values to three decimal places, ranging from 0 (zero) to 3 5
(three), and is called the index of relative usefulness by topographical zone (ISR). 6
j) Each distress has its own ISR in the section. In a particular section in study with two or 7
more types of distresses, the section has two or more ISR's. According to (1), the index 8
that measures the severity condition which the section under study is subjected to is the 9
largest ISR found therein. This higher value of ISR is called Index of the Condition of the 10
Topographical Zone (IC), whose classification attributes are presented in Table 1. 11
k) In accordance with the technical literature, the index of usefulness in a section 12
measures how this section is functional and comfortable for traffic. Thus, it is possible to 13
conclude that the usefulness is inversely proportional to the severity. In this work the 14
value of usefulness ranging from 0 (zero) to 3 (three) is expressed in three decimal places. 15
This value was called Index of the Usefulness of the Topographical Zone (IS) whose 16
classification attributes are listed in Table 2. 17
For the phase of analysis and measurement of distresses using the data obtained from 18
topographic survey, a CAD platform capable of receiving this data and treat them 19
topographically in vector form is necessary . From the periodic monitoring of the studied area, 20
the data obtained can be worked in geographical information systems. 21
Knowing the seasonal changes of the sections, this tool can be very useful to provide 22
priority sections, which are usually more damaged during rainy periods. This whole process is 23
fundamental to the decision maker, who can manage the unpaved road network and make 24
decisions that meet their needs. Is important to emphasize that the parameters used in this 25
research satisfy the local conditions and require adaptation studies to be applied in other regions. 26
27
3. CASE STUDY: THE MUNICIPALITY OF AQUIRAZ
28
29
3.1 Initial Considerations
1
The municipality has an area of 482.8 km ², located in 32.30 kilometers from the city of 2
Fortaleza, the capital of Ceará. 3
The study area was approximately 2 km long, which allowed us to consider constant both 4
the rainfall and traffic. The rainfall for the study period was 946.20 mm. During the topographic 5
survey a traffic count was also taken, getting 47 vehicles per day, among them: passenger cars, 6
vans, small and medium trucks and dump trucks. 7
To better execution and detailing of the studies, the area was divided into three polygonal 8
called AQZ 01, AQZ 02 and AQZ 03, whose demarcation was performed with the aid of a 9
differential GPS with submeter accuracy (see Figure 1). Approximately 2,500 points were 10
collected in the periodic topographic surveys in polygonal. A total of approximately 10,000 11
points were collected; they were subsequently georeferenced and the proposed
12model was checked by Correia et al. (
2
). .
13After the completion of the data collection, they were downloaded to the computer 14
directly in the software of topographic data analysis, Topograph System 98 SE. This app is used 15
to download, process, and generate the DTM and the DMD, analyze and deliver relevant reports 16
of the results, generating full reports of topographic surveys. 17
With the full reports downloaded, the polygonal generated by the surveys was calculated. 18
The average relative error that was found in the polygonal was 1:3.053.827, which means an 19
average error of 1 (one) mm for every 3.054 km of survey was obtained, which is approximately 20
30 times lower than the accepted by the Brazilian legislation. After closing the polygonal, the 21
coordinates of the irradiated points were calculated . 22
With the coordinates of the points of the polygonal station and irradiated points, the 23
Digital Terrain Model (DTM) and Digital Model of Distressed (DMD) were generated in 24
triangular mesh for later obtaining the contour lines of the terrain, equidistant from 1 (one) cm. 25
With a visual inspection, it was possible to notice the occurrence of various types of soils 26
in the study area. Based on this variety, a tactile-visual survey of these materials was made, 27
resulting in a primary qualification and location of the start and end for each soil type found. 28
material enough to run each borehole (approximately 50 kg) was collected in order to do the 1
characterization tests and then do the geotechnical classification. 2
In this research, the geotechnical classification method chosen was the Highway Research 3
Board (HRB). The reason for choosing this method was simplicity and its diffusion in the 4
technical environment. Is important to emphasize that the grading method has its limitations of 5
applicability in the geotechnical classification of Brazilian soils. The following geotechnical 6
laboratory tests for purposes of HRB classification were performed: Determination of the 7
consistency limits and Particle Size Analysis of Soil. Through these data, regions with the same 8
kinds of soil were delimited, thus creating the Geotechnical Zones (GZ). 9
10
3.2 Modeling of Performance
11
12
The proposed performance model is based on the calculation of the following indexes: 13
a) Level of Individual Severity (LIS): This level is determined by calculating the depth of 14
each distress type registered. With the depth of the distress calculated, the severity is 15
obtained consulting the severity table corresponding to the type of distress; 16
b) Average Severity in a topographical zone (AS): this severity is obtained from the 17
simple arithmetic average of the LIS for type of distress and for all distresses; 18
c) Relative Surface density of each distress of topographical zone (RS): this density is the 19
result of the sum of the areas or lengths of each type of distress divided by the total area 20
or total length of the topographical zone 21
d) Index of the usefulness of the topographical zone (IS): this index is the result of 22
multiplication between AS and RS ; 23
6
24
e) Index of the condition of the topographical zone (IC): this index is the largest of the 25
f) Index of the usefulness of the topographical zone (IS): This index is calculated 1
subtracting the IC from the maximum usefulness possible (IS = 3 - IC). 2
The study area is composed of 29 topographical zones. For each topographic area a 3
spreadsheet with the indexes mentioned above was made for each distress type registered during 4
the topographic surveys in addition to their respective performance modeling. 5
In this work the following types of performance equations were obtained: 18 straight 6
lines equations and 11 parabolic equations (examples in Figure 2) for a total of 29 equations, 7
which means one equation for each of the 29 topographical zones considered that generate the 8
study area of this work. According to the technical literature, the likely mathematical model of a 9
performance curve is a parabola. The parabolic equations obtained corroborate the above 10
statement. However, the 18 equations of the straight lines obtained are special cases. 11
The quadratic equation or equation of degree 2 is an equation of the type: 12
y = ax2+ bx+ c (1)
13
Where a, b and c are the coefficients and have distinct geometric properties into the 14
equation. These properties are: 15
a) Coefficient a is the coefficient of 'openness' of the equation, in other words, how much 16
the parabolic curve is smooth or not. Thus, the closer to zero the value is, the smoother 17
the curve and, consequently, the section under study deteriorates more slowly. Likewise 18
it is possible to say that as this coefficient moves away from zero, the curve is less 19
smooth and, consequently, the section under study will deteriorate faster. The negative 20
sign of coefficient a means that the concavity of the curve is downwards. The positive 21
sign means that the concavity is upward; 22
b) Coefficient b represents the point of maximum value of the curve displaced as relative 23
to the ordinate axis (y axis), that is, the horizontal distance (x coordinate) of the point of 24
maximum value. When the value of coefficient b is negative, the x coordinate is positive 25
and when it is positive, the x coordinate will be negative; 26
c) Coefficient c represents the point where the parabolic curve intersects the ordinate axis 27
it is negative, the y coordinate will be negative, representing the highest level of 1
usefulness obtained by survey executed. 2
After associating the HRB classification obtained with the location of points where the 3
occurrences of the same type of soil were initiated and finalized, areas with the same 4
geotechnical behavior were delineated. These areas simply named geotechnical zones (GZ) for 5
this study. 6
7
3.3 Analysis and discussion of results
8
9
In GZ - 01 it was possible to perceive the predominance of one type of distress in all its 10
extension. In this case, the defect was Inadequate Transversal Cross Section. Because of that fact 11
the occurrence of other types of distress not to influence the calculation of the equation, which 12
represents the performance modeling of the section under study. As a result the parabolic 13
equation becomes a straight line equation, ever constant and equaling the value of IS of the 14
section under study, in this case it is equal to 2. 15
Table 3 shows the coefficients of the parabolic equation as a function of the values of the 16
longitudinal ramp of each topographic zone for the Geotechnical Zone 1 (GZ - 01). 17
GZ - 02 has only a topographical zone due to the fact that the section presents a nearly 18
constant longitudinal slope. There is not, however, the predominance of one type of defect. Its 19
cross section has camber enough to drain the water from rainfall without difficulties. The type of 20
soil present in the subgrade in this geotechnical zone was classified by the HRB as A-1-b. Table 21
3 also shows the coefficients of the parabolic equation based on the values of ramp of the 22
topographic zone 10 (TZ - 10). 23
Analyzing these coefficients, it is checked that: 24
a) Coefficient a is negative. Therefore, the parabola is concave down. Its value is small, 25
causing the parabolic curve to be smooth, and the degradation to be slow in this case, 26
b) Coefficient b is small, but positive, which means that the point of greatest usefulness 1
(IS) was before the first surveying; 2
c) Coefficient c is the point where the curve intersects the y-axis and as b is very close to 3
the y axis. This value of c (2.7622) is positioned between the maximum IS of the curve and the 4
IS obtained (2.7567). 5
In GZ - 03 there are some problems localized in drainage devices. These problems were 6
caused by the invasion of neighboring vegetation on the road. This fact allows the flow of water 7
from rainfall to go in the longitudinal direction, and consequently, with this flow the entrainment 8
of fine will occur, slowly eroding the road. The coefficients of the parabolic equation according 9
to the value of the longitudinal ramp for each topographic zone for the geotechnical zone 3 (GZ - 10
03) are also found in Table 3. 11
Analyzing these coefficients for such topographical zones, it was possible to observe that: 12
a) Coefficients a are negative, so the parables have the concavity facing downwards. 13
Their values are small and have little variation between the topographical zones, making 14
parabolic curves smooth and very close to each other. This causes degradation, which is 15
slow in this case and it would take a little more than 8 (eight) months to reach a IS 1.5; 16
b) Coefficients b are small, but positive, which means that the points of greatest 17
usefulness (IS) were before the first surveying; 18
c) Coefficients c are the points where the curves intersect the y axis, and coefficients b 19
are very close to the y axis. . 20
In GZ - 04, the problems located in drainage devices are caused by the invasion of the 21
neighboring vegetation on the road. It is worsening due to the fact that the vegetation becomes 22
increasingly dense and the soil type (A- 6) hampers the infiltration. The type of soil observed in 23
field contains about 20% of a clay material in its grain size distribution, making it more difficult 24
to drain. Table 3 shows the coefficients of the parabolic equation as a function of the values of 25
the longitudinal ramp in geotechnical zone 4 (GZ - 04) in its topographical zones. 26
Analyzing the coefficients for such topographical zones, it was possible to verify that: 27
a) Coefficients a are negative, so the parables have the concavity facing downwards. 28
parabolic curves smooth and very close to each other. This causes degradation, which is 1
slow in this case and it would take a little more than 6 (six) months to reach a IS 1.5; 2
b) Coefficients b are small, but positive, which means that the points of greatest 3
usefulness (IS) were before the first surveying; 4
Na ZG – 04, os problemas localizados nos dispositivos de drenagem são causados pela
5
invasão do corpo estradal da vegetação lindeira, agravando-se devido ao fato de que a vegetação
6
fica cada vez mais densa e que o tipo de solo (A-6), dificulta sua infiltração. O tipo de solo
7
observado em campo contém cerca de 20% de material argiloso em sua composição
8
granulométrica, dificultando ainda mais a drenagem. A Tabela 3 apresenta os coeficientes da
9
equação parabólica em função dos valores de rampa longitudinal, para a zona geotécnica 4 (ZG –
10
04) nas suas zonas topográficas.
11
c) Coefficients c are the points where the curves intersect the y-axis and coefficients b are 12
very close to the y axis. These values of c are positioned between the maximum IS's of 13
the curves and IS's. 14
In topographical zones TZ - 20, TZ - 21 and TZ - 22, there is the predominance of one 15
type of distress. It is the Inadequate Transversal Cross Section, in its entire length. In this case, 16
the neighboring vegetation protects the base of the cross section against erosion caused by water 17
flow. Thus, water is forced to flow in the road causing the entrainment of fine materials, with 18
consequent erosion in the road. It is important to note that in this case the longitudinal ramp acts 19
as a catalyst in the process of erosion. 20
In cases where the degree of inadequate transversal cross section is large, the water from 21
rainfall erodes and drags the fine in the road, and also heavily erodes the road paths originating 22
from passing traffic and causes the entrainment of fine materials to the base. Is important to note 23
that this erosion only occurs when it is not protected by neighboring vegetation of the unpaved 24
road. 25
Due to the fact that there is an inadequate distress of the transversal cross section in the 26
topographic zones analyzed, the occurrence of other types of distresses does not influence the 27
calculation of the equation that represents the performance modeling of the section under study. 28
Due to this reason, the parabolic equation turns into an equation of straight line, ever constant 29
and equaling the value of IS in the referred sections. 30
In GZ - 05 the problems located in drainage devices are caused by the invasion of the 31
dense, hindering the drainage of water from rainfall and forming "pools" on the unpaved road 1
during winter, causing the vehicles wheels to get stuck to the ground. Table 3 also shows the 2
coefficients of the parabolic equation as a function of the values of longitudinal ramp for each 3
topographic zone in a given geotechnical zone 5 (GZ - 05). 4
Analyzing these coefficients, is was possible to verify that: 5
a) Coefficient a is negative, therefore the parabola is concave down. Its value is small, 6
causing the parabolic curve to be smooth and the degradation to be slow in this case, 7
taking almost 7 (seven) months to reach a IS 1.5; 8
b) Coefficient b is small, but positive, which means that the point of greatest usefulness 9
(IS) was before the first surveying; 10
c) Coefficient c is the point where the curve intersects the y-axis and coefficient b is very 11
close to the y-axis. The maximum IS of the curve is positioned between the c value 12
(2.2250) and IS (2.2956). 13
In GZ - 06, in all its length, there is a predominance of one type of distress. In this case is 14
inadequate transversal cross section. This fact makes the occurrence of other types of distresses 15
not to influence the calculation of the equation, which represents the performance modeling of 16
the section under study. Due to this reason, the parabolic equation turns into an equation of 17
straight line, ever constant and equaling the IS value of the section under study, in this case equal 18
to 2 (two). 19
The coefficients of the parabolic equation as a function of the values of each longitudinal 20
ramp in each topographical zone for geotechnical zone 6 (GZ - 06) are also shown in Table 3. 21
9
22
Coefficient c, obtained from the equations' performance, represents, in this work, a point 23
of usefulness which is less than the maximum obtained from the equation. This is natural, 24
because the first topographical survey was conducted in January 2002, with the route already 25
deteriorated due the abrasive action of traffic. Coefficient b depicts that the peak of usefulness 26
was before the first topographical survey. It is still possible to conclude that even if the absolute 27
value of this coefficient is small, it can not be neglected. This is due to the fact that this value 28
Coefficient a of the performance equations determines the speed with which a given 1
section deteriorates with time. The higher the coefficient, the greater the deterioration speed and 2
vice versa. The variables that most influence the deterioration process obtained in this study 3
were: the values of longitudinal ramp and the presence or absence of drainage equipment. 4
It has been found that the presence of effective drainage devices in the road causes its 5
deterioration more slowly. It was also possible to notice that, where the efficiency of drainage 6
devices decreases, the respective speed of deterioration of the sections increases proportionally. 7
The absence of these devices makes the section deteriorate very quickly. 8
The geotechnical type of material also has a great influence on the drainage, since the 9
greater the amount of clay and/or silt materials in the surface layers of the road, the greater the 10
difficulty of water from rainfall to infiltrate into the soil. 11
The value of the longitudinal ramp acts as a catalyst in the process of deterioration. The 12
higher the value of the ramp, the faster the section will deteriorate, because with this, the 13
entrainment of fine and the consequent soil degradation will occur. It is important to note that for 14
small values of the ramp, the water finds difficult to superficially drain and, consequently, 15
infiltrates in greater quantity, increasing soil humidity. If the humidity reaches high value, 16
whatever the humidity gains, there will be a proportional loss of mechanical resistance of the 17
soil. In other words, the soil looses the supporter capacity becoming less resistant and, 18
consequently, deteriorating more rapidly. 19
The behavior of geotechnical soil found in areas GZ - 02 (A-1-B), GZ - 03 (A-2-4) and 20
GZ - 04 (A-6) was as expected for these types of materials, that is, they had a decreasing 21
behavior respectively. It is important to note that the geotechnical zones GZ - 03 (A-2-4) and GZ 22
- 05 (A-2-4) should, in principle, present similar behaviors, due to having the same material, 23
which did not occur. 24
O comportamento do solo encontrado nas zonas geotécnicas ZG – 02 (A-1-B), ZG – 03 25
(A-2-4) e ZG – 04 (A-6) foi de acordo com o esperado para estes tipos de materiais, ou seja, 26
tiveram comportamento respectivamente decrescente. 27
The behaviors were presented decreasing respectively, which is due to the fact that in GZ 28
- 03, the presence of surface drainage devices was noted, a fact which did not occur in GZ - 05. 29
more speed, also considering similar changes in the value of longitudinal ramp in those zones 1
cited above. 2
Because the geotechnical zones GZ - 01 (A-1-A) and GZ - 06 (A-1-B) present inadequate 3
transversal cross section in all its longitudinal extent, it was not possible to calculate the correct 4
performance equation of both zones. Consequently, the comparative analysis of the behavior 5
between the referred geotechnical zones was not possible. Thus, a similar behavior between the 6
two was admitted. 7
Due to rainfall, a natural increase of vegetation occurs (grass, leaves, twigs, etc.). 8
Therefore, the vegetation tends to invade the road and, consequently, in some cases it damages 9
the already disabled/non-existent drainage devices. This situation forces the longitudinal flow in 10
road, consequently the entrainment of fine, disaggregating materials and, finally, accelerating the 11
deterioration process. 12
The sections without drainage equipment in the study area showed compromised 13
efficiency. One of several factors (poor geometric conformation of the track in some places) that 14
can be pointed out (in the rainy season it impedes any drainage) as catalysts for the process of 15
deterioration. And in some situations this bad conformation causes the stoppage of traffic. 16
10
17
The rain, one way or the other, causes a more rapid deterioration of the road, but when 18
there is an efficient stormwater drainage in the road, this process becomes slower. It is important 19
to note that the geometric conformation of the road, both transverse and longitudinal, associated 20
with a network of efficient drainage enables, at any time of year, - wet or dry, the trafficability of 21
vehicles is not interrupted in the sections of the road. 22
Currently, the model of maintenance performed on unpaved roads by responsible 23
agencies, in the case of the municipalities in Ceará, their own governments, is based on the 24
simple scraping of the top layer of unpaved roads using motor graders and without replacing the 25
scraped material. Is important to emphasize that the continued use of this model will cause a bad 26
conformation of the cross section, which in itself is already a serious defect, repeatedly causing 27
the appearance of other defects, corrugations and potholes. 28
The costly model management (maintenance of unpaved roads) quoted above is fed 29
mainly by the lack of human resources skilled in the subject, in addition to the scarce financial 30
maintenance of transport vehicles, increased travel time and the consequent increase in fuel 1 consumption. 2 3 4. CONCLUSION 4 5
The procedure for performance modeling for unpaved roads proposed and tested in this 6
research is easy to understand and simple to apply from the technical point of view, but it is 7
constituted of major steps contained in any other methodology for performance modeling. 8
According to the technical literature, performance modeling is the center of a management 9
system that is well suited to local conditions and supports the decision in a Maintenance 10
Management system (MMS). 11
Evidence shows that the system currently in force in the municipalities of Ceará and 12
especially in the city of Aquiraz is unable to change the current trend. Recognizing its 13
shortcomings in the area of road maintenance, many agencies responsible for this maintenance 14
began a search for answers to the many problems of management, conservation and maintenance 15
of roads. 16
Although the implementation of a Pavement Management System (PMS) for unpaved 17
roads is not an easy task, it is urgent for the vast majority of municipalities of Ceará, since their 18
unpaved road networks are not being dealt with properly, making them increasingly deteriorated 19
and, consequently, resulting in more expensive services arising from their use. 20
The model was applied in Aquiraz, where there is an occurrence of red-yellow podzolic 21
soils, whose occurrence in the municipality is 19% and in the State is 29%. For the application of 22
the proposed method in other places with different types of soil, there is the necessity of 23
calibrating the equation for the forecasting of the rolling surface performance. This is due to the 24
nature of the ALYNOMO method, which considers exogenous variables (rainfall, topography, 25
topology and density of the defects, type of performed maintenance), and the type of soil, that 26
impacts directly on the loss of the quality of the road. 27
For this purpose, it is recommended to fit the performance curve for other localities, 28
observing the types of materials and their geotechnical features, as well as the development of 29
simplified procedures for data surveying (topography, geotechnics and hydrology). 30
11
Future studies could develop mathematical correlations between data (topographic, 1
geotechnical and rainfall) and the coefficients of the performance curve obtained or accomplish 2
the formatting and inclusion of a PMS for unpaved roads in a GIS environment, creating a more 3
powerful tool for decision makers use when utilizing public funds for the maintenance and 4
rehabilitation of unpaved roads. 5
6
ACKNOWLEDGEMENTS
7
8
The authors acknowledge CNPq for the promotion of MCT/CNPq 14/2012 Process 9
470609/2012-5 and CAPES for the support to the research. 10
11
BIBLIOGRAPHY
12
13
1. Correia, J. A. B., E. F. Nobre Júnior, and A. P. H. Cavalcante. Evaluation of Unpaved Roads With Aid of
14
Relative Usefulness Index by Road Branch. In Transportation Research Board: 83rd Annual Meeting,
15
2004, Washington, D.C. 83rd Annual Meeting Compendium of Paper CD-ROM, 2004.
16
2. Haas, R.; Hudson, W. R.; Uddin, W. (1997), Infrastructure Management, McGraw-Hill, New York, New
17
York, USA.
18
3. Riverson, J. D. N.; Sinha, K. C.; Scholer, C. F.; Anderson, V. L. (1987), Evaluation of Subject Rating of
19
Unpaved County Roads in Indiana, Transportation Research Record 1128, 53 -61 pp., USA.
20
4. Eaton, R. A.; Gerard, S.; Datillo, R. S. (1987), A Method for Rating Unsurfaced Roads, Transportation
21
Research Record 1106, Volume 2, 34 – 42 pp., USA.
22
5. Eaton, R. A.; Beaucham, R. E. (1992), Unsurfaced Road Maintenance Management, US Army Corps of
23
Engineers – USACE, Cold Regions Research & Engineering Laboratory – CRRL, Special Report 92–26,
24
USA.
25
6. USACE (1995), Unsurfaced Road Maintenance Management, TM 5-626, Technical Manual, United States
26
Army Corps of Engineers, Headquarters, Department of the Army, Washington, D.C., USA.
27
7. University of Wisconsin - Transportation Information Center (1989), Gravel Paser Manual – Pavement
28
Surface Evaluation and Rating, Madison, USA.
8. Almeida, R. V. O., E. F. Nobre Júnior, and J. L. C. Silva. Evaluation Methodologies for Unpaved Road
1
Surface Condition in Municipal District of Aquiraz, Ceara, Northeast Brazil. In Transportation Research
2
Record: Journal of the Transportation Research Board, No. 1989, Vol. 2. Transportation Research Board
3
of the National Academies, Washington, D.C., 2007, pp. 203-210.
4
5
TABLES
1
TABLE 1 Classification For The Index Of The Condition Of The Topographical Zone (IC)
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3
4
IC CLASSIFICATION
0.000 – 0.199 Excellent
0.200 – 0.649 Good
0.650 – 1.099 Regular
1.100 – 1.599 Bad
1.600 – 2.199 Very Bad
2.200 – 3.000 Poor
TABLE 2 Classification For The Index Of The Usefulness Of The Topographical Zone (IS)
1
13
2
3
IS CLASSIFICATION
0.000 – 0.199 Poor
0.200 – 0.649 Very Bad
0.650 – 1.099 Bad
1.100 – 1.599 Regular
1.600 – 2.199 Good
2.200 – 3.000 Excellent
TABLE 3 Longitudinal Ramp Values (LR) And The Parable Coefficients (a, b, c) From
1
Each TZ For Each GZ
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3
4
GZ TZ LR a b c
GZ - 01
TZ - 01 -5.4412% 0.0000 0.0000 2.0000
TZ – 02 -0.2070% 0.0000 0.0000 2.0000
TZ – 03 2.9450% 0.0000 0.0000 2.0000
TZ - 04 -0.7400% 0.0000 0.0000 2.0000
TZ – 05 -3.1719% 0.0000 0.0000 2.0000
TZ – 06 -0.9175% 0.0000 0.0000 2.0000
TZ – 07 -3.9714% 0.0000 0.0000 2.0000
TZ - 08 -1.4263% 0.0000 0.0000 2.0000
TZ - 09 -0.4650% 0.0000 0.0000 2.0000
GZ - 02 TZ - 10 -0.4514% -0.0141 0.0086 2.7622
GZ - 03
TZ - 11 -0.0850% -0.0226 0.0723 2.6932
TZ - 12 -3.7685% -0.0230 0.0769 2.6982
TZ - 13 -0.0796% -0.0213 0.0600 2.7213
TZ - 14 1.0575% -0.0227 0.0733 2.6794
GZ - 04 TZ - 15 -1.0900% -0.0327 0.0988 2.4424
TZ - 17 -1.5117% -0.0323 0.0967 2.4656
TZ - 18 0.3400% -0.0325 0.0950 2.4775
TZ - 19 1.0625% -0.0327 0.0983 2.4544
TZ - 20 6.3200% 0.0000 0.0000 1.0000
TZ - 21 -0.3650% 0.0000 0.0000 2.0000
TZ - 22 -6.5610% 0.0000 0.0000 1.0000
GZ - 05 TZ - 23 -0.7300% -0.0330 0.1036 2.2250
GZ - 06
TZ - 24 1.8800% 0.0000 0.0000 2.0000
TZ - 25 -0.1850% 0.0000 0.0000 2.0000
TZ - 26 -3.7450% 0.0000 0.0000 2.0000
TZ - 27 0.2567% 0.0000 0.0000 2.0000
TZ - 28 2.4363% 0.0000 0.0000 2.0000
TZ - 29 -0.0212% 0.0000 0.0000 2.0000
FIGURE 1 Sketch of the Polygonal and geotechnical zones location, and HRB classification
1
of geotechnical areas (not to scale).
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15
3
FIGURE 2 Examples of graphics for predictive modeling of performance (ALYNOMO
1
method) of topographic areas.
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3
1
2
