Assessing the Performance of Subgrade and Unbounded Pavement Materials and Their Effect on Pavement Distress of Alaba-Sodo Exisiting Road; Ethiopia

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Assessing the Performance of Subgrade and Unbounded Pavement Materials and Their Effect on Pavement Distress of Alaba-Sodo Exisiting Road; Ethiopia

1Defaru Katise, 2Nazrawit Aklilu, 3Dr. Ramesh Kumar Verma 1Lecture, 2Investigator and 3Profesor in Geotechnical Engineering Civil Engineering Faculty; Arba Minch Institute of Technology

Abstract :- Since material properties play a significant role to determine the performance of pavement layers. In Ethiopia, now a day, pavement distress is the main problem of roads affecting its intended purposes. This research study is undertaken at Alaba-Sodo road, which is located in SNNPRS, is one of the roads affected by distresses. The main objective of this study is to assess the performance of subgrade and unbound pavement materials and their effect on pavement distress through condition survey and laboratory tests.

Among the total sample units of 199 along the selected road sections, 71 sample units were selected by using systematic random sampling technique. For those selected sample units PCI values were determined in order to know the stations for sample collection for laboratory tests. Based on the PCI values, eight sample locations were identified out of these six locations were selected from severely distressed and two locations were selected from good sections. From eight identified sample locations three samples were taken from each layers of sub-grade, sub-base and base course. Therefore, total of 24 material samples were taken for the laboratory tests. The major tests such as CBR test, Compaction Test, Atterberg limit test, Sieve Analysis were conducted for the above mentioned three layers and ACV, AIV, LAA and Flakiness Index were conducted only for base course material to check performance of the pavement materials.

Out of the surveyed 71 sample units, 1.41% is excellent, 5.63 is very good, 14.08% is good, 23.94% is fair, 28.17% is poor, 21.13% is very poor and 5.63 is failed. The overall laboratory test result shows that the distresses that are frequently observed on the road surface were significantly influenced by subbase and base course materials.

Key words: Performance of subgrade and Unbounded materials, AASHTO and ERA standards, Geotechnical properties, Pavement condition index, Pavement distress

  1. INTRODUCTION ON BACKGROUND

    Subgrade and granular material layers must be investigated and constructed properly thoroughly in order to achieve the overall desire of pavement performance. In Ethiopia, now a day, pavement distress is the main problem of roads affecting its intended purposes. And it is mostly characterized by failure of all kinds; like surface deformation, cracks, disintegration, surface defects etc. There is no just one reason for each type of failure. Factors affecting the pavement performance are climate, material properties, structure and traffic load (ERA, PRAOM, 2013).

    The research area; Alaba- Sodo road is the main part of Addis Alaba- Sodo-Abaminch road and which is flexible asphalt paved road. This study only focuses on Alaba-Sodo 68 km long and one of the reconstructed roads in Ethiopia which is located in the SNNPRS Ethiopia and its operation was started in 2010. It is a two lane-paved 7m wide carriageway with 1.5m gravel shoulders on each side.

  2. STATEMENT OF THE PROBLEM

    The maintenance of the road to well accessible asphalt standard is considered to have a crucial role in road performance. But currently the AlabaSodo road pavement is prematurely damaged by serious distress with alligator/fatigue cracks, rutting, potholes, block cracking and others cracks and disintegration that can cause traffic hazards, taking long time for travel, affecting economic and industrial growth of nearby zones and cities like Wolaita, Kambata, Hadiya, Shashemene and Hawassa. The accessibility, users comfort and national/ social development of the above mentioned zones and cities and increasing vehicle operating cost are directly and indirectly affected by road distress.

    Therefore, this research has been done in order to assess the performance of subgrade and unbound pavement material quality and to relate the material quality with the pavement distresses. Because the characteristics of sub-grade, sub-base and base course layer material properties have a considerable impact on the performance of the pavement.

  3. OBJECTIVES

      1. General objective

        The general objective of this research was to assess the performance of sub-grade and unbound pavement materials and their effect on pavement distress.

      2. Specific objectives

    To determine the types, severity and density of the pavement distress based on pavement condition index (PCI).

    To assess the performance of sub-grade and unbound pavement materials by conducting laboratory tests like Atterberg limit, sieve analysis, compaction test, CBR, AIV, ACV, LAA and Flakiness Index.

    To indicate the effect of performance of subgrade and unbound pavement layers on pavement distress for possible pavement maintenances

  4. LOCATION OF THE STUDY AREA

    This study is conducted in Alaba- Sodo existing road which is located in southwestern part of Ethiopia. It connects Addis Ababa, the capital of Ethiopia with some SNNPRS towns. The distance between Alaba to Sodo is generally 72 km but the exact study location starts at the Bilate River Bridge which is 4.5km away from Alaba town and terminates in Sodo Town. Therefore the total length of the route is 68km. The geographic positions of the total road falls between 7° 18' N latitude to 38° 05' E longitude and 6° 02' N latitude 37° 33' E longitude. The location map of the study road is shown below (Associated Engineering Consultants, 2006).

    Figure1: The Alaba Sodo road stretch

  5. RESEARCH METHODOLOGY

      1. Sample size and Sampling Technique for PCI

        For this research objective, the study road stretch was divided into sections based on preliminary site visit. Each section was divided into sample units. The type and severity of pavement distress was differentiated visually and then the quantity of the distress was measured. The distress data were used to calculate the PCI for each sample unit. The PCI of the road sections were determined based on the PCI of the inspected sample units within the sections. Therefore, road was sectioned in to five different sections as shown in Table 1. Among the five sections, the three sections (Alaba-Adilo, Shone-Buge and Buge-Boditi) were selected and the number of these divided sample units as shown in Table 2 with 200m length according to ASTM D6433 Manual.

        Table 1: Road sections selected for detail survey

        No. of section

        Station (km)

        Start town/village

        End town/village (length)

        Pavement condition survey

        1

        0+00015+000

        Alaba

        Adilo=15km

        Selected for survey

        2

        15+000 26+100

        Adilo

        Shone=11.1km

        Selected for survey

        3

        26+100 33+400

        Shone

        Buge=7.3km

        Selected for survey

        4

        33+400 50+800

        Buge

        Boditi =17.4km

        Relatively good condition

        5

        50+800 68+000

        Boditi

        Sodo =17.2km

        Relatively good condition

        Table 2: The number of sample units in each divided sections

        No. of section

        Station (km)

        Start-End (length)

        Total number of sample units

        Remark

        1

        0+00015+000

        Alaba-Adilo =15km

        15,000/200 =75

        surveyed

        2

        15+000 26+100

        Adilo-Shone =11.1km

        11,100/200 =55.5

        Not selected for survey

        3

        26+100 33+400

        Shone-Buge =7.3km

        7,300/200 =36.5

        surveyed

        4

        33+400 50+800

        Buge- Boditi=17.4km

        17,400/200 =87

        surveyed

        5

        50+800 68+000

        Boditi-Sodo=17.2km

        17,200/200= 86

        Not selected for survey

        From table 2, some sections were needed to survey due to problems observed during site visit which stated in remark column. Therefore, the next steps such as the determination of PCI value for each sample unit and average PCI value for each section were continued after determinations of sample units of each section.

      2. Pavement Condition Survey Procedures

        The PCI provides an objective and rational basis for determining maintenance and repair needs and priorities. Continuous monitoring of the PCI is used to establish the rate of pavement deterioration, which permits early identification of major rehabilitation needs. The Pavement Condition Index (PCI) is determined by measuring pavement distress with a numerical indicator based on a scale of 0 to 100. For each distress measured, there are deduct values depending upon the nature of the distress, its severity and quantity. The deduct values are summed, adjusted to take into account the total number of distresses identified, and then subtracted from 100 to give the PCI index for the pavement (ASTM D6433, 2007).

        Figure 2: Some sample pavement condition surveys on Shone-Buge subsection

      3. Selection of the Sample Site and Sampling technique for Laboratory tests

    To assess the performance of pavement layers of the engineering properties of materials are determined by carrying out different laboratory tests. Samples were collected for laboratory tests as per present pavement conditions/pavement condition index (PCI) values. Those locations were selected as severely distressed and non-distressed pavement condition.

    Hence, it was decided to collect six (6) stations from severely distressed section and two (2) from good (non-distressed) sections for sampling. Three samples were taken from Alaba-Adilo section at stations of 1+600-1+800km, 9+600-9+800 km and 10+200-10+400 km. Two samples were taken from Shone-Buge section at stations of 31+500-31+700 km and 32+100-32+300 km and, and three samples were taken from Buge-Boditi section at stations of 41+000-41+200km, 42+800-43+000km and 43+200-43+400km and check the effect of pavement layers properties on distresses. Therefore, eighteen (18) and six (6) samples will be taken from severely distressed sections and non-distressed sections respectively. Generally, 24 Soil and aggregate samples were collected from both condition of surface. Hence, laboratory tests conducted were summarized in table below.

    Table 3: Material Laboratory Tests conducted and their Procedures

    S.No

    Tests conducted

    Test method/standard

    1

    Liquid limit(LL)

    AASHTO T89 / ASTM D 4318

    2

    Plastic Limit (PL)

    AASHTO T90/ ASTM D 4318

    3

    Grain size analysis

    AASHTO T 88 / ASTM D 422

    4

    Moisture- Density Relations of Soils

    AASHTO T180-97 / ASTM D2937

    5

    Soil classification

    AASHTO M145

    6

    California Bearing Ration (CBR)

    AASHTO T-193 and T-180

    7

    Aggregate Crush Value

    British Standard 812, Part 110; (ERA FPDM, 2013).

    8

    Aggregate Impact Value

    British Standard 812, Part 112, 1990

    9

    Los Angeles Abrasion

    AASHTO T96-99

    10

    Flakiness Index

    British Standard 812, Part 105; (ERA FPDM, 2013).

  6. RESULTS AND DISCUSSIONS

      1. Pavement Condition Survey

        The pavement condition survey was made on selected sections by following ASTM D6433 Manual methods as discussed in chapters 3 to establish the stations for taking sample for the laboratory tests. This was done by dividing road pavement into different sections. Each section was divided into different sample units. Pavement condition survey was mainly done in order to differentiate the severely distressed sections and good sections of the road and then to take sample from both sections. Before starting of the detailed pavement evaluation, the entire road length was visually assessed and an attempt was made to identify the current condition of the road and the types of distresses occurred on the road prism.

        Table 6: PCI and PCR values for Alaba-Adilo section

        Sample unit No

        Alaba-Adilo

        Alaba-Adilo

        The Value based on assumed standard deviation

        The Value based on actual standard deviation (Additional Sample Units)

        Station

        PCI Value

        PCR

        Station

        PCI Value

        PCR

        0+000-15+000; 15km

        0+000-15+000; 15km

        1

        0+000-0+200

        29.5

        Poor

        0+200-0+400

        6

        Failed

        2

        1+200-1+400

        40

        Poor

        1+600-1+800

        3

        Failed

        3

        2+400-2+600

        32

        Poor

        3+000-3+200

        33

        Poor

        4

        3+600-3+800

        52

        Fair

        4+400-4+600

        24

        Very poor

        5

        4+800-5+000

        33

        Poor

        5+800-6+000

        38

        Poor

        6

        6+000-6+200

        26

        Poor

        7+400-7+600

        27

        Poor

        7

        7+200-7+400

        28

        Poor

        8+800-9+000

        18

        Very poor

        8

        8+400-8+600

        47

        Fair

        10+200-10+400

        16

        Very poor

        9

        9+600-9+800

        74

        Very Good

        11+600-11+800

        54

        Fair

        10

        10+800-11+000

        59

        Good

        13+000-13+200

        66

        Good

        11

        12+000-12+200

        58

        Good

        14+600-14+800

        30

        Poor

        12

        13+200-13+400

        48

        Fair

        13

        14+400-14+600

        29

        Poor

        Total-1

        555.5

        Total-2

        315

        Weighted Average = (Total-1+Total-2)/24=36.27

        The above Table 6 shows the values of PCI and PCR values that determined from assumed and actual standard deviation of the section Alaba- Adilo and which indicates the pavement need suitable maintenance works because weighted average PCI value can be rated as poor condition of pavement surface.

        Table 7: the results of the pavement condition survey (PCI) on section-2 (Shone-Buge):

        Sample unit No

        Shone-Buge

        The Value based on assumed standard deviation

        Station

        PCI Value

        PCR

        26+100-33+400; 7.3km

        1

        26+100-26+300

        52

        Fair

        2

        26+700-26+900

        59

        Good

        3

        27+300-27+500

        42

        Fair

        4

        27+900-28+100

        39

        Poor

        5

        28+500-28+700

        41.5

        Fair

        6

        29+100-29+300

        41

        Fair

        7

        29+700-29+900

        57

        Good

        8

        30+300-30+500

        76

        Very Good

        9

        30+900-31+100

        41.5

        Fair

        10

        31+500-31+700

        37.5

        Poor

        11

        32+100-32+300

        34

        Poor

        12

        32+700-32+900

        42

        Fair

        Total-1

        562.5

        The Value based on actual standard deviation (Additional Sample Units)

        1

        26+300-26+500

        88

        Excellent

        2

        30+100-30+300

        54

        Fair

        Total-2

        142

        Weighted Average = (Total-1+Total-2)/12 = 50.32

        The above Table 7 shows the values of PCI and PCR values that determined from assumed and actual standard deviation of the section Shone-Buge and which indicates the pavement need suitable maintenance works because weighted average PCI value can be rated as fair condition of pavement surface.

        Table 8: PCI and PCR values for Buge-Boditi section

        Sample unit No

        Buge-Boditi

        Buge-Boditi

        The Value based on assumed standard deviation

        The Value based on actual standard deviation (Additional Sample Units)

        Station

        PCI Value

        PCR

        Station

        PCI Value

        PCR

        33+400-50+800; 17.4km

        33+400-50+800; 17.4km

        1

        33+400- 33+600

        58

        Good

        33+600-33+800

        72

        Very Good

        2

        34+800- 35+000

        57

        Good

        34+400-34+600

        47

        Fair

        3

        36+200 36+400

        47

        Fair

        35+200-35+400

        32

        Poor

        4

        37+600 37+800

        54

        Good

        36+000-36+200

        55

        Fair

        5

        39+000- 39+200

        16

        Very Poor

        36+800-37+000

        12

        Very Poor

        6

        40+400- 40+600

        18

        Very Poor

        37+800-38+000

        23

        Very Poor

        7

        41+800- 42+000

        28

        Poor

        38+600-38+800

        56

        Good

        8

        43+200- 43+400

        11

        Very poor

        39+400-39+600

        25

        Very Poor

        9

        44+600- 44+800

        13

        Very Poor

        40+200-40+400

        31

        Poor

        10

        46+000- 46+200

        18

        Very Poor

        41+000-41+200

        4

        Failed

        11

        47+400- 47+600

        15

        Very Poor

        42+000-42+200

        20

        Very Poor

        12

        48+800- 49+000

        18

        Very Poor

        42+800-43+000

        73

        Very Good

        13

        50+200- 50+400

        36

        Poor

        43+600-43+800

        7

        Failed

        14

        44+400-44+600

        17

        Very Poor

        15

        45+200-45+400

        33

        Poor

        16

        46+200-46+400

        50

        Fair

        17

        47+000-47+200

        58

        Good

        18

        47+800-48+000

        40

        Poor

        19

        48+600-48+800

        43.5

        Fair

        20

        49+400-49+600

        46

        Fair

        Total-1

        389

        Total-2

        744.5

        Weighted Average = (Total-1+Total-2)/33 = 34.35

        The above Table 8 shows the values of PCI and PCR values that determined from assumed and actual standard deviation of the section Buge-Boditi and which indicates the pavement need suitable maintenance works because weighted average PCI value can be rated as poor condition of pavement surface.

        Table 9: Percentage of pavement condition rating

        PCR

        Total Number of PCR on the three surveyed sections

        Percentage of PCR (%)

        Excellent

        1

        1.41

        Very Good

        4

        5.63

        Good

        10

        14.08

        Fair

        17

        23.94

        Poor

        20

        28.17

        Very Poor

        15

        21.13

        Failed

        4

        5.63

        Total

        71

        %ge of PCR

        6% 1% 6%

        %ge of PCR

        6% 1% 6%

        21%

        21%

        14%

        14%

        Excellent

        Very Good Good

        Fair Poor

        Very Poor

        Excellent

        Very Good Good

        Fair Poor

        Very Poor

        Percentage of PCR was also presented in figure3 below.

        24%

        24%

        28%

        28%

        Figure 3: Percentage of pavement condition rating

        The above all results and discussion are only about the three selected sections out of the total five sections of the study. But the remained two sections are almost in the same condition with the surveyed three sections. Therefore, this implies that the whole 68km road rated as poor and fair condition and it needs suitable maintenance works.

      2. Laboratory Test Results and Discussion

        The engineering properties of materials were determined by carrying out different tests such as Atterberg Limits, Gradation, Soil Classification, compaction, CBR, ACV, AIV, LAA and Flakiness Index tests in the laboratory.

        1. Atterberg limit tests for Sub-grade, subbase & base course materials

          Table 10: Results of Atterberg limit tests for Sub-grade, subbase & base course materials

          Section

          Station

          PCR

          Subgrade Soil

          Subbase

          Base Course

          LL

          PL

          PI

          LL

          PL

          PI

          LL

          PL

          PI

          %

          %

          %

          %

          %

          %

          %

          %

          %

          Alaba- Adilo

          1+600-

          1+800

          Failed

          46

          25

          21

          32

          23

          9

          21

          19

          2

          9+600-

          9+800

          Very good

          43

          22

          21

          35

          27

          7

          21

          20

          1

          10+200-

          10+400

          Very poor

          39

          23

          16

          34

          24

          10

          22

          22

          NP

          Shone- Buge

          27+900-

          28+100

          poor

          23

          N/A

          NP

          36

          31

          5

          24

          17

          7

          32+100-

          32+300

          poor

          40

          21

          19

          30

          23

          5

          32

          N/A

          NP

          Buge- Boditi

          33+600-

          33+800

          Very good

          50

          24

          26

          33

          23

          10

          22.5

          N/A

          NP

          41+000-

          41+200

          Failed

          40

          16

          24

          27

          20

          7

          23

          20

          2

          47+400-

          47+600

          Very poor

          39

          18

          21

          31

          25

          6

          22

          17

          5

          According to ERA Manual, 2013, the subgrade soils with PI values less than 30% and LL< 60 are suitable subgrade materials, for the seasonally wet tropical climate all suitable sub-base materials shall have a maximum Plasticity Index of 12 and Liquid Limit of not exceeding 45 and the base course material which is the fine fraction of a GB1 material shall be non-plastic when determined in accordance with AASHTO T-90. Therefore, Atterberg test results of all station of the subgrade and subbase materials shows that the materials fulfilled the requirement of the ERA specification. This indicates that the subgrade and subbase materials are in a good performance. This implies that the distress on the surface layer is not because of these two materials.

          Among the total 8 station intervals only the base course materials of three station results fulfilled the minimum requirement of ERA specification. i.e. these test value shows that all station interval materials were fulfilled the minimum requirement of ERA specification except materials at station 1+600-1+800, 9+600-9+800, 27+900-28+100, 41+000-41+200 and 47+400-47+600 and which also indicate that the base course materials are not in a good performance.

        2. Sieve Analysis Test Result for Subbase Materials

          For the entire road stretch, red ash blended with soil is used as a sub-base material, and sieve analysis was conducted on this material and the result shows that the samples which are tested are within minimum and maximum limit of ERA specification. For sample, station 10+200-10+400 was presented in Table 11 and Figure below.

          Table 11: Sieve analysis result for subbase material at station 10+200-10+400

          PARTICLE SIZE DISTRIBUTION BY SIEVING TEST METHODS: AASHTO 27/AASHTO 11

          Sub base

          Sieve size(mm)

          Trial one

          Trial two

          Final Result

          Wt. of sample retained(g)

          %ge retained

          %ge pass

          Wt. of sample retained(g)

          %ge retained

          %ge pass

          Average

          %ge retained

          Average

          %ge pass

          Lower limit

          Upper Limit

          50

          0

          0

          100

          0

          100

          0

          100

          100

          100

          37.5

          211

          3.01

          96.99

          337

          3.52

          96

          3.26

          96.74

          80

          100

          20

          812

          11.58

          88.42

          1449

          15.12

          81

          13.35

          83.39

          60

          100

          5

          2015

          28.73

          56.69

          3124

          32.60

          49

          30.66

          52.73

          30

          100

          1.18

          1645

          23.45

          33.23

          1647

          17.19

          32

          20.32

          32.41

          17

          75

          0.3

          1332

          18.99

          14.24

          1954

          20.39

          11

          19.69

          12.71

          9

          50

          0.075

          654

          9.32

          4.92

          549

          5.73

          5

          7.53

          5.19

          5

          25

          Pan

          345

          4.92

          0

          523

          5.46

          0

          Total

          7014

          9583

          Figure 4: Gradation result graph for base course material at station 10+200-10+400

          By similar way, Alaba-Adilo, Shone- Buge and Buge-Boditi sections subbase material revealed that sieve analyses represents material remained within ERA specification. Therefore, subbase material is fair for pavement construction and traffic loading.

        3. Sieve Analysis for Base Course:

          For this road section crushed aggregate has been used as a base course material. All base course materials must have a particle size distribution and particle shape which provide high mechanical stability and should contain sufficient fines (amount of material passing the 0.425 mm sieve) to produce a dense material when compacted. But all stations interval results indicate the materials dont fulfilled the minimum requirement of the ERA standard, i.e. the materials are not uniformly graded except the station interval 9+600-9+800 and this implies the base course materials are not in a good gradation or performance. The graph and the table below show the base course sieves analysis according to AASHTO standard for station of 41+000-41+200.

          Table 12: Sieve analysis result for base course material at station 41+000-41+200

          PARTICLE SIZE DISTRIBUTION BY SIEVING TEST METHODS: AASHTO 27/AASHTO 11

          BASE COURSE

          Nominal size 20

          Sieve size(mm)

          Trial one

          Trial two

          Final Result

          Wt. of sample retained(g)

          %ge retained

          %ge pass

          Wt. of sample retained(g)

          %ge retained

          %ge pass

          Average

          %ge retained

          Average

          %ge pass

          Lower limit

          Upper Limit

          50

          0

          100

          0

          100

          0

          100

          37.5

          0

          100

          0

          100

          0

          100

          28

          29

          0.37

          99.63

          264

          100

          0.19

          99.81

          100

          100

          20

          1988

          25.22

          74.4

          1760

          28.74

          71

          26.98

          72.83

          90

          100

          10

          3238

          41.09

          33.31

          2197

          35.87

          35

          38.48

          34.35

          60

          75

          5

          1030

          13.07

          20.24

          688

          11.24

          24

          12.15

          22.2

          40

          60

          2.36

          281

          3.57

          16.67

          186

          3.04

          21

          3.31

          18.89

          30

          45

          0.425

          244

          3.1

          13.57

          462

          7.55

          14

          5.32

          13.57

          13

          27

          0.075

          734

          9.31

          4.26

          316

          5.16

          8

          7.24

          6.33

          5

          12

          Pan

          336

          4.26

          251

          4.1

          Total

          7880

          6125

          Figure 5: Gradation result graph for base course material at station 41+000-41+200

          Likewise, the gradation results for all remained stations are approximately the same with above station result except at station 9+600-9+800.

      3. Soil Classification for Subgrade

        In this study the AASHTO Soil Classification System was used. For this system the sieve sizes of 2mm, 425-m, and 75- m were used to determine the categories of soil. The table below shows the subgrade soil classification for the station interval 1+600-1+800 according to AASHTO Classification System.

        Table 13: Results of subgrade soils classification

        Section

        Station

        PCR

        Subgrade Soil

        AASHTO Soil Classification

        LL

        PL

        PI

        %pass 0.075mm sieve

        Alaba- Adilo

        1+600-1+800

        Failed

        46

        25

        21

        66

        A-7-6

        9+600-9+800

        Very good

        43

        22

        21

        65

        A-7-6

        10+200-10+400

        Very poor

        39

        23

        16

        62

        A-6

        Shone- Buge

        27+900-28+100

        poor

        23

        N/A

        NP

        65

        A-5

        32+100-32+300

        poor

        40

        21

        19

        59

        A-6

        Buge- Boditi

        33+600-33+800

        Very good

        50

        24

        26

        51

        A-7-6

        41+000-41+200

        Failed

        40

        16

        24

        71

        A-6

        47+400-47+600

        Very poor

        39

        18

        21

        54

        A-6

        The above tables all stations of subgrade materials are on group of clayey soils and they were classified by ASSHTO which shows that general rating of a soil fair to poor as a sub-grade material.

        As per Atterberg test, the base course materials satisfied the requirements only at station of 10+200-10+400, 33+600-33+800 and 32+100-32+300 but at remaining stations it does not, which have some plastic behavior.

        Additionally, the sieve analysis of the all base course materials do not satisfied the minimum requirements of ERA specification except at station of 9+600-9+800.

        Table 14; Compaction Test Results for Subgrade, Subbase and Base course materials

        Section

        Station

        PCR

        Subgrade Soil

        Subbase

        Base Course

        MDD

        g/c3

        OMC %

        MDD

        g/c3

        OMC %

        MDD g/c3

        OMC %

        Alaba- Adilo

        1+600-1+800

        Failed

        1.61

        16.2

        1.84

        16

        2.3

        4

        9+600-9+800

        Very good

        1.56

        19.5

        1.91

        11.5

        2.4

        3.6

        10+200-10+400

        Very poor

        1.78

        15

        1.88

        12

        2.2

        4.1

        Shone- Buge

        27+900-28+100

        poor

        1.73

        13.5

        1.95

        10.5

        2.3

        5.8

        32+100-32+300

        poor

        1.65

        14.5

        1.79

        14

        2.23

        5.3

        Buge- Boditi

        33+600-33+800

        Very good

        1.45

        21.5

        1.82

        13

        2.4

        3.8

        41+000-41+200

        Failed

        1.6

        21

        1.82

        12

        2.26

        4.2

        47+400-47+600

        Very poor

        1.55

        21

        1.81

        12.7

        2.22

        6.8

        These results for subgrade, subbase and base course materials that were tested with modified proctor test and their samples compacted in five layers in a mold by a hammer in accordance with specified nominal compaction energy. So the dry density was determined based on the moisture content and the unit weight of compacted soil. The water content at which this dry density occurs was termed as the optimum moisture content (OMC). They also used the graph of moisture content verses dry density to determine their maximum values by graph reading.

      4. CBR test with discussion

        Table 15: CBR test for all stations of subgrade, Subbase and Base course materials

        Section

        Station

        PCR

        Subgrade Soil

        Subbase

        Base Course

        CBR

        Value

        % Swell

        CBR

        Value

        % Swell

        CBR Value

        % Swell

        Alaba- Adilo

        1+600-1+800

        Failed

        8.00

        1.40

        28.00

        1.10

        104.00

        0.10

        9+600-9+800

        Very good

        12.00

        1.20

        38.00

        0.65

        108.00

        0.07

        10+200-10+400

        Very poor

        13.00

        1.20

        39.00

        0.90

        80.00

        0.12

        Shone- Buge

        27+900-28+100

        poor

        15.00

        1.30

        34.00

        1.10

        87.00

        0.17

        32+100-32+300

        poor

        12.00

        1.22

        27.00

        1.50

        77.00

        0.23

        Buge- Boditi

        33+600-33+800

        Very good

        7.00

        1.35

        21.00

        1.40

        105.00

        0.06

        41+000-41+200

        Failed

        10.50

        1.30

        24.00

        1.40

        96.00

        0.10

        47+400-47+600

        Very poor

        13.00

        1.25

        25.00

        0.90

        90.00

        0.30

        And also, the CBR test results fail to satisfy the minimum requirements of specifications at five stations of 10+200-10+400, 27+900-28+100, 32+100-32+300, 47+400-47+600 and 41+000-41+200 and at the remaining three stations it satisfied. This also implies the materials which are not uniformly graded. In conclusion, the base course material is not in a good performance and also it can be possible cause for distresses.

        The results of the CBR tests for subgrade soils in Table 15 show that samples from all stations have CBR value of greater than 5%. Based on the ERA specification, these samples indicate as good subgrade materials. The percent swell test results also are below 2% which is an indication of less expansiveness of the soil, which is a good subgrade material. All values satisfied the minimum requirement, but variations of CBR result for different conditions indicate the surface layer is affected by some other factors. For instance, for the sub section of Buge-Boditi, the CBR value each station for very Good condition is 7, for failed condition it is 10.5 and for very poor condition it is 13. Here for all surface conditions the CBR value satisfied but the surface is distressed. Therefore, the cause for the distress is not the subgrade material but affected by other cause.

        In ERA standard, the minimum soaked CBR for sub base material shall be 30% when determined in accordance with the requirements of AASHTO T-193. Subbase material results of stations 1+600-1+800, 32+100-31+300, 33+600-33+800, 41+000- 41+200, and 47+400-47+600 shows the results of the CBR value of less than the minimum requirement of ERA standard for subbase materials (30%) and the remaining stations satisfy the ERA requirement. But for different condition the value of CBR vary accordingly. For example, for sub section of Buge-Boditi, the Sub base CBR value for failed condition (24) is greater than the CBR value (21) of very good condition. For both conditions the sub base material do not satisfied the requirement but the CBR value for the failed condition is relatively good. This implies that the surface layer is failed not only by the material quality but due other cause.

        Base course material results of stations 10+200-10+400, 27+900-28+100, 32+100-32+300, 41+000-41+200 and 47+400- 47+600 shows that the results of the CBR value of less than the minimum requirement of ERA standard for base course materials (100%) And the remaining stations satisfy the ERA requirement. However, for example, for the sub section of Alaba-Adilo, the CBR value of the base course material satisfied the requirement at station 1+600-1+800 and 9+600-9+800 but the surface is failed. This indicates that the surface layer is getting failed due to the material by itself and the surface layer affected by other unknown cause.

      5. Aggregate Test Results with Discussion for Base Course

        Table 16: Result of ACV AIV, LAA and FI test for all base course materials

        Section

        Station

        PCR

        Aggregate Tests for Base Course

        ACV

        AIV

        LAA

        FI

        Alaba-Adilo

        1+600-1+800

        Failed

        16.90

        18.40

        18.60

        20.70

        9+600-9+800

        Very good

        16.70

        17.20

        15.69

        16.40

        10+200-10+400

        Very poor

        15.80

        17.30

        20.01

        19.60

        Shone-Buge

        27+900-28+100

        poor

        17.40

        15.60

        21.06

        21.60

        32+100-32+300

        poor

        18.20

        18.40

        23.48

        22.10

        Buge-Boditi

        33+600-33+800

        Very good

        18.50

        19.90

        16.94

        18.90

        41+000-41+200

        Failed

        16.40

        16.80

        19.74

        21.50

        47+400-47+600

        Very poor

        16.00

        17.30

        17.65

        13.50

        According to ERA FPDM, a maximum value of ACV shall be 25 as per BS 812-110, 1990 standard, a maximum value of aggregate flakiness index shall be 35 as per ERA Specification Manual, the Los Angeles abrasion value shall not exceed 45% when determined in accordance with the requirements of AASHTO T-96(99) standard and AIV is shall not be greater than 30 % as per BS 812-112, 1990 standard. Therefore, aggregate test values show in above Table 16 fulfilled the requirement of the above mentioned specification or standards and coarse aggregate particle are in a good condition based on the objectives of the above tests.

      6. Maintenance options for Pavement Distresses

    The pavement maintenance in general consists of all the routine repair tasks necessary to keep the pavement, under normal conditions of traffic and normal forces of nature, as nearly as possible in its as-constructed condition. Department of the Army (TM-5-624), 1995 suggests maintenance options for different distress types with respect to their severity level.

    The following table shows maintenance option for cracking, surface deformation, disintegration, and surface defects with their severity level.

    Table 17: Maintenance suggestion for Cracking

    Pavement distress

    Severity level

    Maintenance Option

    Alligator cracking

    Low

    Seal Coat

    Medium

    Seal coat or Patching

    High

    Thin hot-mix Overlay

    Block cracking

    Medium

    Chip seal, seal coat or Thin hot-mix Overlay

    Edge cracking

    Low

    Seal coat

    Medium

    Patching

    High

    Patching

    Longitudinal and transversal cracking

    Low

    Clean and Seal

    Medium

    Clean and Seal or Full-depth crack Repair

    High

    Full-depth crack Repair

    Table 18: Maintenance suggestion for surface deformation

    Pavement distress

    Severity level

    Maintenance Option

    Shoving

    Medium

    Thin hot-mix Overlay

    High

    Thin hot-mix Overlay

    Depression

    Low

    Patching

    Rutting

    Low

    Slurry Seal, Patching

    Medium

    Slurry seal, Patching, or Thin hot-mix overlay

    High

    Patching, or Thin hot-mix overlay

    Swell

    Low

    Thin hot-mix overlay

    Medium

    Thin hot-mix overlay

    Table 19: Maintenance suggestion for disintegration

    Pavement distress

    Severity level

    Maintenance option

    Potholes

    Low

    Patching

    Medium

    Patching

    High

    Patching

    Raveling

    Low

    Crack sealing/ chip sealing

    Medium

    Thin overlay

    High

    Thin overlay

  7. CONCLUSION AND RECOMMENDATION

    1. Conclusion

      The pavement condition survey along the selected road sections showed that the different failure types such as alligator cracking, rutting, edge cracking, potholes, slippage cracking, block cracking, weathering and raveling, shoving, lane/ shoulder drop off, and depression were existing.

      The alligator cracking and rutting types of distress were dominating types of distress along the stretch. Based on the pavement condition survey 1.41% of road section was with PCR of Excellent, 5.63% of Very Good, 14.08% Good, 23.94% of fair, 28.17% of poor, 21.13% of very poor and 5.63% failed. The average PCI values for Alaba-Adilo, Shone- Buge, and Buge-Boditi were 36.27%, 50.32% and 34.35% respectively.

      The laboratory test results show that only the subgrade material satisfied but subbase material at some stations did not fulfill the strength test (CBR) requirement and the base course material did not satisfy the sieve and CBR requirements as per ERA, AASHTO and BS standards and these could also be one of the causes for the distress.

      The road section were full of distresses dominantly alligator cracks, surface rutting, and depressions. Therefore, these failures can be maintained by observing level of severity as per maintenance option already maintained.

    2. Recommendation

Finally, the following points are recommended:

Accordingly, existing road needs maintenances by the Ethiopian road Authority or any concerned entity. The pavement condition ratings should be updated every year and Routine as well as periodic pavement maintenance practices should be employed to reduce premature pavement failure

The surface course, which is a mixture of aggregate and asphalt, should be considered in order to know causes of distresses in full confidence. So aggregate tests and bitumen tests for this layer should be conducted.

Further study is recommended that is related with other expected causes of failures such as moisture variation within subgrade and pavement materials in order to select the most effective maintenance and/ or rehabilitation techniques.

REFERENCES

  1. AASHTO. (1986). Standard Specification for Transportation materials and methods of sampling and testing Part 1 (21st edition).

  2. AASHTO. (1993). Guide for design of pavement structures, American Association of State Highway and Transportation Officials.

  3. Abebe Dinku, (2002). Construction Materials Laboratory Manual, Addis Ababa University, Ethiopia

  4. Ali Mohamed Z. (2011), Evaluation pavement distress using pavement condition index, Master thesis, Diponegoro University, Semarang.

  5. Asphalt Institute. (1967). Asphalt in Pavement Maintenance, Manual Series No.16 (MS-16), USA.

  6. Associated Engineering Consultants, Engineering Design Report for Alaba to Sodo Road Project, 2006.

  7. ASTM D 6433 (1999), Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys, U.S. America.

  8. ASTM D 6433 (2007), Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys, U.S. America.

  9. Barry et al. (2006), Geotechnical Aspects of Pavements; Manual, U.S Department of Transport Federal Highway Administration, publication No.FHWA NHI-05-037, U.S America.

  10. Bashir, M. (2006). Effect of thermal stressesand environmental condition on asphalt Pavement, Master Dissertation, Al-mergheb University, Khoms.????

  11. Departments of the Army, Technical Manual (TM-5-624); Maintenance and Repair of Surface Areas, 1995.

  12. Donald, Walker. (2013). Pavement Surface Evaluation and RatingManual, University of Wisconsin-Madison.

  13. Ethiopian Roads Authority. (2013). Flexible Pavement Design Manual, Volume1, Addis Ababa, Ethiopia.

  14. Ethiopian Roads Authority. (2013). Pavement Rehabilitation and Asphalt Overlay Manual, Addis Ababa, Ethiopia.

  15. Ethiopian Roads Authority. (2013). Site Investigation Manual, Addis Ababa, Ethiopia.

  16. Ethiopian Roads Authority. (2014). Standard Technical Specifications And Methods of Measurement for Road works, Addis Ababa, Ethiopia.

  17. Fiker, A. (2005). Pavement Distresses on Addis Ababa City Arterial Roads, Causes and Maintenance Options. Master thesis, Addis Ababa University, Ethiopia.

  18. Luo, Z. (2005). Flexible Pavement Condition Model Using Clusterwise Regression and Mechanistic-Empirical Procedure for Fatigue Cracking Modeling, Ph.D Dissertation, The University of Toledo.

  19. SABA Engineering PLC. (2002). Central Material Testing Laboratory Manual of Soil Testing Part 1, Ethipia, Addis Ababa.

  20. SABA Engineering PLC, (2002).Central Material Testing Laboratory Manual of Soil Testing, Part 2, Ethiopia, Addis Ababa.

  21. Sharad.S.Adlinge,Prof.A.K.Gupta (2014).Pavement Deterioration and its Causes.Journal of Mechanical & Civil Engineering.Shivaji University, India,13

  22. The United Republic of Tanzania Ministry of works. (2000), Laboratory Testing Manual, Novum Grafiks AS, Skjetten Norway.

  23. Weil, R. (2009).State of the Streets in San Carlos, Report to Council, California.

  24. Yang H.Huang, (2004). Pavement Analysis and Design, (2nd edition), United States of America, Pearson Education ,Inc

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