Improving Ontario Pavement Management t 1 hrough …docs.trb.org/prp/12-2474.pdf · ·...

1 Hamdi, Alyami ,Zhou, Tighe

Improving Ontario Pavement Management through Long Term Monitoring 1

2

Amin S. Hamdi, MASc, 3

PhD Candidate 4

Department of Civil and Environmental Engineering 5

University of Waterloo 6

200 University Avenue West 7

Waterloo, ON, Canada N2L 3G1 8

Telephone: (519) 888-4567 ext. 33872 9

[email protected] 10

11

Zaid Alyami, BASc, 12

MASc. Candidate 13 Department of Civil and Environmental Engineering 14

University of Waterloo 15 200 University Avenue West 16

Waterloo, Ontario, Canada N2L 3G1 17

Telephone: (519) 721 2444 18 [email protected] 19

20

Tracy (Bingqian) Zhou, BASC Candidate 21





Telephone: (519) 888-4567 ext. 33872 26


28

Susan L. Tighe, PhD, PEng 29

Professor and Canada Research Chair in Pavement and Infrastructure Management 30

Director Centre of Pavement and Transportation for Technology 31





Telephone: (519) 888-4567 ext. 33152 36

Fax: (519) 888-4300 37


39

Corresponding Author: Amin S.Hamdi 40 Word count = 3980+ (5*250) + (6*250) =6730 words41

TRB 2012 Annual Meeting Paper revised from original submittal.


ABSTRACT 42 43 This paper presents performance models that have been developed for the Ministry of 44 Transportation of Ontario (MTO) using data from their Pavement Management System (PMS2). 45

This study is in partnership with the Centre for Pavement and Transportation Technology 46 (CPATT), at the University of Waterloo, and the MTO under the Highway Infrastructure 47 Innovation Funding Program (HIIFP). This research includes analysis of historical data from the 48 MTO PMS2. The project involved analyzing 870 sections and over 17,000 pavement treatment 49 cycles for a 20 year cycle. The research involved development of a robust framework for sorting 50

the extensive data and grouping them into categories that reflect typical pavement factors. 51 Performance models were then calibrated, and validated. In the analysis of the historical data, the 52 data was sorted, classified according to pavement type, equivalent total thickness, traffic volume, 53 soil type, and climate zone. In the development of the performance curves 75% of the data was 54

used to calibrate the performance curves, which is described by the predicted pavement condition 55 index (PCI) and as a function of pavement age. The remaining 25% of the data was used to 56

validate the various performance models using various statistical tools. The analysis determines 57 what factors have the greatest influence over performance of the various pavement treatment 58

types. 59 This paper provides a framework for analysis using several statistical tools. It also 60

involves development of expected service lives for various typical pavement treatments under a 61

series of varying conditions in Ontario. This research is important for MTO for validation of 62 existing performance and incorporation for future PMS strategies. 63

64

INTRODUCTION 65

66 Roads deteriorate throughout the life cycle, traffic loading, and environment loading have a huge 67

impact on the performance. As roads deteriorate, they lose the ability to meet the needs of the 68 users. Performance models are not only important for monitoring Level of Service (LOS) but 69 also for selecting the most effective pavement preservation and pavement maintenance 70

intervention throughout the life cycle. They also assist in determining the end of service life 71 when rehabilitation or reconstruction is required. In addition, performance models allow 72

engineers and managers the ability to properly allocate resources through effective use of 73 pavement management systems (PMS) [1]. PMS is divided in to two main levels, project level 74

and network level. The pavement performance models are calibrated and validated using at the 75 network level with project level data. 76

Basic pavement performance models according to researchers vary from simple linear 77 regression models to complicated Markov Chain models by using empirical, mechanistic, or 78 mechanistic-empirical approaches [2]. 79

In this paper, performance curves are developed for the Ministry of Transportation of 80 Ontario (MTO), using regression analysis on over 870 pavement sections. The main parameter 81

variables in this research are pavement age in years and the Pavement Condition Index (PCI) 82 which was calculated based on the International Roughness Index (IRI) and Distress 83 Manifestation Index (DMI).The models are then used to predict the service lives of several 84 typical treatments in province of Ontario. 85

86 87



SCOPE AND OBJECTIVE 88

89

The objective of this paper is to analyze various typical Ontario pavement treatments and 90 validate whether the current PMS2 performance models are consistent with those observed. 91 Several design scenarios are examined through the analysis of 870 sections or 17,868 treatment 92 cycles using a 20 year data set. 93

94

BACKGROUND 95 96 Pavement performance indicators are important in the management of road networks. The 97 pavement condition index is a numerical rating that shows the pavement condition and it ranges 98 from 0 (failed) to 100 (excellent). Realistically, a road in the poor range would be impassable. 99 Figure 1 shows atypical pavement performance index with a PCI rating. 100

101

102 FIGURE 1 Typical pavement performance curve. [3] 103

Performance models are divided into two types: deterministic, which use single point value 104

estimator, and probabilistic, which consider variability through the use of probabilities. There are 105 several modeling tools that can be applied including Markov chain, Bayesian, and Artificial 106

Neural Networks models [2] based on the activity or the maintenance and rehabilitation program 107 (M&R). In Ontario, Markov chain and Bayesian models have been used to examine low-volume 108 roads [4]. In 2001 the available deterministic and probabilistic models were used to optimize the 109 annual investment for M&R program in network level [5]. 110

111

DATA DESCRIPTION 112 113 The data provided was collected from 1990 to 2010. This data was divided according to the 114 available historical and pavement survey data. Historical data included: equivalent total 115

thickness, subgrade type, climate zone, and pavement type, while the survey data includes 116 Average Annual Daily Traffic (AADT), and Equivalent Single Axle Load (ESAL), IRI 117

measurement, PCI and DMI [6]. 118 The treatment cycles analyzed in this research and all sections come from the MTO 119

PMS2. There are a total of 870 sections; however, when sections are broken down into treatment 120 cycles (i.e. pavement treatment to next pavement treatment) it results in 17,868 cycles. The 121 evaluation focuses on asphalt, concrete, and composite (i.e. asphalt over concrete) roads over a 122



time frame of 20 years. Each pavement section varies in terms of pavement type, equivalent total 123

thickness, subgrade type, and climate zone [7] are categorized and then evaluated. 124 MTO has well established protocols to collect pavement distress data [8]. They also have been 125 leaders in using PMS as noted the PMS2 is a second generation program. PMS2 distress data 126

was categorized into the variance sections. Table 1 shows a sample of the data that was used in 127 this research. As noted the column headings are provided in the notes following the table. 128 129



TABLE 1 Sample of Typical PMS Data 130 SEC DIREC KM start KM end Year Rehab AGE PCI IRI DMI AADT TYPE THICK ESAL GRADE Enviro- True Activity

1 E 0.23 4.658 2009 1996 13 69 1.48 7.38 20442 AC 101 378283 Sandy si SO Mil+Ovly2F











1 E 0.23 4.658 1998 1996 2 96 0.99 10 20442 AC 101 317097 Sandy si SO Mil+Ovly2F

1 E 0.23 4.658 1997 1996 1 96 0.93 10 20442 AC 101 317097 Sandy si SO Mil+Ovly2F

1 E 0.23 4.658 1996 1996 10 54 0 5.94 20442 AC 101 317097 Sandy si SO Mil+Ovly2F

9 E 56.669 72.946 2009 2009 13 69 1.14 7.18 90318 AC 307 1065447 Sandy si SO Recon AC5F









9 E 56.669 72.946 2000 2009 4 95 0 10 90318 AC 307 1065447 Sandy si SO Recon AC5F

9 E 56.669 72.946 1999 2009 3 89 0 9.42 90318 AC 307 1065447 Sandy si SO Recon AC5F

9 E 56.669 72.946 1998 2009 2 94 0 9.78 90318 AC 307 1065447 Sandy si SO Recon AC5F

9 E 56.669 72.946 1997 2009 1 97 0 10 90318 AC 307 1065447 Sandy si SO Recon AC5F

131

Notes: Sec= section number, Direct=Direction (North N, South S, East E, West W, Both B), KM start= Section start at KM, KM end = 132

Section end at KM, Rehab. = Most Recent Rehabilitation year. PCI= Pavement Condition Index (0-100), IRI= International 133 Roughness Index (mm/m), DMI= Distress Manifestation Index (0-10), AADT= Average Annual Daily Traffic (total number), 134 Type=Pavement Type (AC =asphalt, PC = Portland cement, CO = composite, ST = Surface Treatment), Thick. = Equivalent Total 135 Thickness (mm), ESAL= Equivalent Single Axle Load, Grade = Subgrade Type, Enviro = Climate Zone (Southern Ontario SO or 136 Northern Ontario NO), True Activity = see Table 3 137



The 870 sections were classified according to pavement type, equivalent total thickness, ESAL 1

class, subgrade type, and Climate zone as summarized in Table 2. As noted within each class the 2 total adds up to 870 [9]. 3 4

TABLE 2 Distributions of Pavement Type and Corresponding Levels 5 6

Influence Factors Corresponding Levels Number of

sections

Pavement type

(AC) Asphalt 651

(PC) Portland cement 6

(CO) Composite 26

(ST)Surface Treatment 187

Equivalent Total Thickness

L(Low) (>500 mm) 846

M(Medium) (<=500-750mm) 19

H(High) (<=750 mm) 5

ESAL

Class1 (> 500,000) 423

Class 2 (50,000 - 500,000) 339

Class 3(< 50,000) 108

Subgrade Type

(SS) sandy silt 645

(GM) Granular Material 114

(LC) Lacustrine Clay 93

(VC) Varved Clay 18

Climate Zone Southern 496

Northern 374

7

As noted, the majority of data that was available in this analysis are asphalt roads. The low 8

pavement thickness is also the most prevalent with the sandy silt subgrade being the dominant 9 subgrade in the available data set. The ESAL categories are more weighted on class 1 and 2 10

although. There are still 108 sections in Class 3. There are slightly more sections from southern 11 Ontario, although still a good portion in northern Ontario for analysis purposes. It is easy to 12 apply and maintained, with low base thickness and sandy silt subgrade in the southern part of the 13

province. 14 15

Methodology 16 17 Multiple regression analysis was carried out to assess performance of the various treatments. 18 Regression analysis was selected given the large amount of data available. As shown in the Data 19 base analysis the most dominant influence factors are: asphalt roads, low pavement thickness, 20

sandy silt subgrade, and ESAL class 1 and 2 therefore theses influence factors were consider in 21 the analysis. Table 3 provides a summary of the various pavement treatments that were assessed 22

for each pavement type 3 activities were assessed. These models and activities were selected due 23 to the limitation of this paper and the importance of these activates. The data was sorted and 24 filtered according to the pavement type, equivalent total thickness (mm), soil type, ESAL, and 25 Climate zone. Approximately, 290 categories were found within the database. However, in order 26 to develop models that were statistically valid, a minimum of 30 treatment cycles within each 27 category was required to carry out the analysis. Thus, any category that had less than 30 data 28



points was removed. As well any section or treatment cycle has a PCI value less than 50, 1

equivalent total thickness less than 30 mm was also removed. Overall, 10 models were 2 developed as shown in Table 4. Also, although surface treated pavement type was included in the 3 database, it was removed based on the fact there was not enough data in any of the categories. 4

Pavement age was selected as the independent variable. 5

TABLE 3 Summary of Treatments Types Analyzed 6 7

TABLE 4 Summary of Category Types Analyzed 8

Number Activity Pavement

Type

Equivalent

Total

Thickness

ESAL Subgrade

Type Climate

Zone

Number

of. Sections

in Model

Number of.

Sections for

Calibration

Number of

Sections for

Validation

1 101

AC L CLASS 1 SS NO 190 143 47

2 AC L CLASS 2 SS NO 88 66 22

3 102

AC L CLASS 1 SS NO 57 43 14 4 AC L CLASS 2 SS NO 128 96 32

5 107

AC L CLASS 1 SS NO 184 138 42 6 AC L CLASS2 SS NO 54 40 14

7 101

AC L CLASS1 SS SO 94 71 23

8 AC L CLASS2 SS SO 120 90 30 9

107 AC L CLASS1 SS SO 193 145 48

10 AC L CLASS2 SS SO 330 248 82

9

The models contain the number of sections as function of soil type, pavement type, equivalent 10 total thickness, climate zone and ESAL. Each model is calibrated using 75% of the data. Models 11 were fitted to polynomial function, and the coefficient of determination (R

2) is determined for 12

each model and is a measure of error explained by the equation. For example an R2 of 0.85 13

means that the model explains 85% of the error. Thus, it is desirable to have a high R2 value. The 14

remaining 25% of the data in each respective category was used to validate the models by 15 estimating the Average Absolute Error (AE). A small AE represents that the model is valid. The 16 average error was used to determine the validity of each of the developed models. If the AE was 17 less than 15%, the model was considered to be an acceptable model. Also the models slope were 18

calculated base on the derivative of the first term in the polynomial equations, the slope helped in 19 comparing two models to show witch model have faster deterioration rate. As well as the 20 pavement service life was obtained from the prediction model that helps in the estimating of 21

remaining pavement life of the roads and what type on maintenance will be adequate to apply on 22 the section is suitable [10]. 23

𝑨𝒗𝒆𝒓𝒂𝒈𝒆 𝑨𝒃𝒔𝒐𝒍𝒖𝒕𝒆 𝑬𝒓𝒓𝒐𝒓 𝑨𝑬 = 𝟏

𝑵

𝑶𝒊 − 𝑷𝒊

𝑷𝒊

𝑵

𝒊=𝟏

Activity code Activity Description

101 Hot Mix Overlay 1 Lift

102 Mill and Hot Mix Overlay 1 Lift

107 Full Depth Reclamation (FDR) and Hot Mix Overlay 2 Lift



Where: 1

Oi = Observed Value 2 Pi = Predicted Value 3 N= Number of Validating points 4

5 Analysis of Results 6 7 Table 5 presents the results for the predicted models as function of pavement type equivalent 8 total thickness, ESAL, and subgrade type and treatment types as presented in Table 3 and 4. 9 10 Figure 2a and 2b presents a sample of developed performance model for Northern Ontario 11

asphalt concrete, low equivalent total thickness, class 1 ESALs, on sandy silt activity used is 101. 12

As can be seen, the deterioration is faster due to the additional traffic load and expected service 13

of 17 years with higher traffic versus 25 years. 14

On the other hand, Figure 3a performance models as those presented in Figure 2a and 2b 15

with the only exception is treatment with activity (102). Figure 3b shows the same conditions with the 16

exception that the ESAL is class2. As noted the pavement deteriorates faster with higher ESALs and 17

lower expected service life of 17 as opposed to 30 years. On the figure 3a it can be noticed that the 18

performance curve does not fallows the pavement deterioration hypothesis due to the subjectivity of 19

visual inspection of pavement distresses and it could be missing updating the PMS 2 with the 20

maintenance activity that was done for that section. 21

Figure 4a and 4b present the same conditions for models in Figure 2, and 3, except the 22

treatment used is 107. As observed, the increase in ESALs resulted in faster deterioration and 23

expected life of 18 years versus 25 years for the lower traffic load. 24

In addition, it can be noted that using activity 101, 102 and 107 for with higher traffic 25

resulted in a similar expected service life; however, treatment with activity 102 resulted in higher 26 service life for class 1 of 30 years while 101 and 107 resulted in 25 years. 27 28

Figure 5a and 5b are similar to figure 2a and 2b with the only exception in the climate 29

zone where we can notice due to the freeze thaw in the northern zone affected the service life of 30

17 year versus 27 in the southern zone. 31

Figure 6a and 6b presents performance models with the same conditions presented in 32

figure 4a and 4b respectively with the exception that the climate zone is for Sothern Ontario. As 33

can be seen, the performance is similar for both climate zones with slightly higher service life in 34

Sothern Ontario. 35



TABLE 5 Summary of Performance Model Analysis 36

No. Treatment Pavement

Type

Base

Thickness ESAL

Subgade

Type Environmental Model R2

AE slope Service

Life

1

101

AC L CLASS 1 SS NO PCI= 0.033*Age^2-2.688*Age+96.02

0.77 0.13 0.07 25

2 AC L CLASS 2 SS NO

PCI=0.062*Age^2-3.39*Age+91.86 0.81 0.05 0.12 16

3 102

AC L CLASS 1 SS NO PCI= 0.123*Age^2-3.465*Age+95.48

0.51 0.01 0.25 14


PCI= -0.032*Age^2-1.173*Age+83.35 0.62 0.11 -0.06 19

5

107

AC L CLASS 1 SS NO PCI= -0.035*Age^2-0.915*Age+92.96

0.75 0.03 -0.07 24


PCI= -0.023*Age^2-1.686*Age+94.27 0.72 0.03 -0.46 21

7

101

AC L CLASS 1 SS SO PCI= 0.022*Age^2-2.463*Age+97.48

0.78 0.06 0.04 24

8 AC L CLASS 2 SS SO PCI= 0.017*Age^2-2.061*Age+93.6 0.7 0.04 0.03 27

9

107

AC L CLASS 1 SS SO PCI= 0.04*Age^2-2.098*Age+93.13 0.5 0.01 0.08 26

10 AC L CLASS 2 SS SO PCI= -0.083*Age^2-0.275*Age+89.19 0.51 0.08 -0.17 20

37

Where: 38

PCI =Pavement Condition Index (0-100) 39

Age= Age of Pavement (yrs) 40



Asphalt pavement (AC) 41

42 FIGURE 2a Performance model for AC Northern Ontario performance model, low 43

thickness, class 1 ESALs, sandy silt subgrade. Activity (101) 44

45 FIGURE 2b Performance model for AC Northern Ontario performance model, low 46 thickness, class 2 ESALs, sandy silt subgrade. Activity (101) 47

R² = 0.77

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30

PC

I

AGE

R² = 0.81

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20

PC

I

AGE



48 FIGURE 3a Performance model for Northern Ontario asphalt concrete, low pavement 49

thickness, class 1 ESALs, sandy silt subgrade, Activity (102) 50

51 FIGURE 3b Performance model for Northern Ontario asphalt concrete, low pavement 52 thickness, class 2ESALssandy silt subgrade. Activity (102) 53 54

55

R² = 0.51

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20

PC

I

AGE

R² = 0.62

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25

PC

I

AGE



56 FIGURE 4a Performance model for AC Northern Ontario performance model, low 57


59

FIGURE 4b Performance model for AC Northern Ontario performance model, low 60


62

R² = 0.75

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30

PC

I

Age



63

FIGURE 5a Performance model for AC Southern Ontario performance model, low 64

thickness, class 1 ESALs, sandy silt subgrade. Activity (101) 65 66

67

FIGURE 5b Performance model for AC Southern Ontario performance model, low 68 thickness, class 2 ESALs, sandy silt subgrade.Activity (101) 69

70

R² = 0.78

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25

PC

I

AGE

R² = 0.70

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14 16 18

PC

I

AGE



71

FIGURE 6a Performance model for AC southern Ontario performance model, low 72


74

FIGURE 6B Performance model for AC southern Ontario performance model, low 75


77

R² = 0.5

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14 16

PC

I

AGE

R² = 0.51

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25

PC

I

AGE



CONCLUSION AND RECOMMENDATIONS 78

79 Many factors effect performance. PCI is used as the key performance indicator in the 80 development of performance models from 870 sections and over 17,000 treatment cycles for 81

different types of Ontario pavements over a 20year period. The factors effecting PCI value 82 include rutting, longitudinal cracking, transfers cracking, and roughness. 83 84

All structural factors are affected by environmental impact, traffic loads and mechanical 85 property of the pavement material. 86

87 The developed performance models can be used to improve the current PMS2 prediction 88

of pavement models simulating deterioration of certain category of traffic, equivalent total 89 thickness, traffic volume, subgrade type, and environmental can be implementers to design 90

appropriate maintenance and rehabilitation program (M&R) based on the predated deterioration. 91 92

The MTO database should be updated as there were some identified gaps in the database. 93 The most dominant type of subgrade soil was sandy silt, and asphalt concrete was the most 94

dominant pavement type in the available data. 95 96 From an analysis point of view the coefficients of determination (R

2) for the models vary 97

from 0.81 to 0.5. These variations are due to the number of combinations in each group. 98 However, the values of the R

2 seem to be reasonable, indicating that the models are an accurate 99

representation of the observed data. Not all the data was used to develop the deterioration models 100 for the previously defined eight groups, the rest of the data was left for the model validation. 101

. 102

The analysis of performance models for the same group of Northern climate zone, sandy 103

silt subgrade, low thickness pavement with variation of ESALs class 1 and class 2 to study the 104 performance and expected service life of different treatments was conducted. As anticipated, 105 increase in traffic loading resulted in faster deterioration and lower service life. In addition, it 106

was found that all three treatments activities (101, 102, and 107) resulted in similar expected 107 service life for the higher traffic load. On the other hand, treatment 102 resulted in higher service 108

life for Class 1 ESALs while treatment 101 and 107 resulted in similar service life. Finally, 109 further study of other treatments and other pavement section groups were not possible due to the 110

limited of this paper will be done. 111 112

ACKNOWLEDGEMENTS 113

The authors gratefully acknowledge the support of the Ministry of Transportation of Ontario 114 (MTO) Special appreciation is also extended to Li Ningyuan, and Becca Lane from the MTO. 115 We also appreciate the effort of Alex Szot. BASc Candidate,University of Toronto. 116

117



118

REFERENCES 119 120

[1] AASHTO. 1985. AASHTO guide for the design of pavement structures. American 121

Association of State Highway and Transportation Officials, Washington, D.C., Vol. 2, 122

Appendix EE. 123

124

[2] Li, N., R. Haas and W.Xie, Development of a New Asphalt Pavement Performance 125 Prediction Model, National Research Council, Ottawa, ON,1997,Vol. 24 pp.547-559. 126

127

[3] U.S Department of Transportation, Federal Highway Administration, 128

www.fhwa.dot.gov/pavement/preservation/ppc0621.cfm.Accessed June 20, 2011. 129

130

[4] Ningyuan, L., T.Kazmierowski, and B. Lane. Long-Term Monitoring of Low-Volume Road 131

Performance in Ontario, at the Annual Conference of the Transportation Association of 132

Canada, Charlottetown, Prince Edward Island, 2006. 133

134

[5] Tighe, S., R. Haas, and N. Li. Overlay Performance in Canadian Strategic Highway 135

Research Program’s Long-term Pavement Performance Study. In Transportation 136

Research Record: Journal of the Transportation Research Board, No. 1778, 137

Transportation Research Board of the National Academics, Washington, D.C., 1989. Pp. 138

191-200. 139

140

[6] Transportation Association of Canada (TAC). Pavement Design and Management Guide, 141

1993, Ottawa, ON: TAC. 142

143

[7] Isa, L., D. Ma’some, and L. Hwa. Pavement Performance Model for Federal Roads. 144

Proceedings for the Eastern Asia Society for Transportation Studies, Malaysia, 2005, 145

Vol.5, pp. 428-440. 146

147 [8] Chamorro,A., Tighe,S., Ningyuan, L, and Kazmierowski,T.Transportation Research Record: 148

Journal of theTransportation Research Board, No. 2153, Transportation Research Board of the 149 NationalAcademies, Washington, D.C., 2010, pp. 49–57 150

151

[9] Ningyuan, L., T.Kazmierowski,Tighe, S and R. Haas. Integrating Dynamic Performance 152

Prediction Models into Pavement Management Maintenance and Rehabilitation 153

Programs.5th International Conference on Managing Pavements, 2001. 154

155

[10] El-Assaly,A, S.T. Ariaratnam, and L.Hempsey, Development of Deterioration Models for 156 the Primary Highway Network in Alberta Canada. Annual Conference of the Canadian 157 Society for Civil Engineering, Montréal, Québec, June 5-8, 2002. 158

159


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