Development of Distress Guidelines and Condition Rating to Improve ...

Development of Distress Guidelines and Condition Rating to Improve Network Management in Ontario, Canada Alondra Chamorro, Susan L. Tighe, Ningyuan Li, and Thomas J. Kazmierowski concrete (AC), portland cement concrete (PCC), composite (COM), and surface-treated (ST) pavements. A five-level system is used to quantify both the severity and the density of distresses associated with AC, PCC, and COM pavements; and a three-level system is used to determine both the severity and the density of distresses associated with ST pavements. Guidelines for the performance of manual evaluations of network pavements (SP-021, SP-022, SP-024, and SP-026) are defined in condition rating manuals developed by MTO. MTO developed a condition rating, the distress manifestation index (DMI), to assess the overall pavement surface condition. DMI is a subjective indicator that provides a summary of the pavement distresses observed on given pavement sections. The distresses considered for each pavement type are collected according to the guidelines of the MTO condition rating manuals. Like other condition indicators, the main scope of DMI is to identify and report the overall condition of the network for management purposes and not to specify rehabilitation and maintenance requirements. For the last 4 years, the Centre for Pavement and Transportation Technology at the University of Waterloo and MTO have been studying the suitability of applying digital technologies to evaluations of pavement distresses in the province of Ontario. A first study finalized in 2006 evaluated the performance of automated and semiautomated pavement survey methods available at the time and the feasibility of replacing or supplementing manual data collection techniques with automated and semiautomated digital data collection techniques for network-level use (2). The study recommended the development of proper guidelines and the use of quality control and quality assurance (QC/QA) for surveying pavement distresses at the network level with automated and semiautomated technologies. In light of this, the study Improving Network Level Pavement Management Through Rationalization and Utilization of Automated/ Semi-Automated Technologies was carried out (3).

In 2006, the Ministry of Transportation of Ontario, Canada (MTO), completed a study with the Centre for Pavement and Transportation Technology at the University of Waterloo to evaluate the performance of automated and semiautomated technologies that collect pavement distress data. From that study it was recommended that MTO define concise guidelines for surveying pavement distresses at the network level by using automated collection technologies and semiautomated distress analysis and for the guidelines to give special attention to quality assurance. In light of that recommendation, the study detailed in this paper presents the development of pavement distress guidelines and a distress manifestation index (DMI) for network-level (DMINL) evaluations by using automated collection technologies and semiautomated distress analysis. To define and validate DMINL, sections evaluated in the previous study were considered. The relative effect of each distress was obtained by linear regression and statistical analysis. The principle used to define the weighting factors was that the distresses considered by the new guidelines should quantify with a minimum error the DMI estimated by the MTO traditional method.

Pavement surface distresses are indicators of pavement performance over time. Distresses are caused by the joint effects of traffic loads, environmental factors, and aging. Surface distress evaluations are commonly performed manually; nowadays, however, several agencies have replaced manual evaluations with evaluations that use automated and semiautomated devices for the collection of distress data. These are typically digital imaging-based technologies that identify cracks and surface defects and laser or ultrasonic technologies that measure transverse and longitudinal profile deformations. The Ministry of Transportation of Ontario, Canada (MTO), currently uses a custom-designed pavement management system (PMS2) to assist senior management with the process of making decisions about pavement maintenance and rehabilitation programming and corridor investment planning (1). One of the data sets in PMS2 is pavement distress evaluation and condition, ranked annually for all pavement sections in the provincial highway network. Between 12 and 15 pavement surface distress types are collected for asphalt

OBJECTIVES AND METHODOLOGY The main objective of the study described here involves the development of pavement distress guidelines and a DMI at the network level (DMINL) for the use of automated collection technologies and semiautomated distress analysis. The study methodology is presented in Figure 1. After an extensive literature review and evaluation of the available technologies, network-level distress guidelines were developed for the evaluation of distress type, density, and severity by the use of automated collection technologies and semiautomated distress analysis. DMINL was defined by considering the outcomes of the network-level guidelines and the

A. Chamorro and S. L. Tighe, Department of Civil Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada. N. Li and T. J. Kazmierowski, Materials Engineering and Research Office, Ministry of Transportation of Ontario, 1201 Wilson Avenue, Downsview, Ontario M3M 1J8, Canada. Corresponding author: A. Chamorro, [email protected]. Transportation Research Record: Journal of the Transportation Research Board, No. 2093, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 128–135. DOI: 10.3141/2093-15

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1. Literature Review 2. Comparative Evaluation of Survey Techniques and Condition Ratings

3. Development of Network Level Distress Guidelines

4. Definition of Distress Manifestation Index at Network Level (DMINL)

5. Analysis of Distress Data 6. Statistical Analysis: Design of DMINL and Validation 7. Adjustments to Guidelines and Condition Rating 8. QC/QA Recommendations

9. Recommendations for the Application of Network Level Guidelines and DMINL FIGURE 1

Assessment methodology.

previous study (2). Both the guidelines and DMINL were validated by considering data collected in the previous study. Finally, recommendations for the application of distress guidelines and DMINL were made. QC/QA recommendations were considered for the automated and semiautomated evaluation of road networks.

AUTOMATED AND SEMIAUTOMATED DISTRESS EVALUATION TECHNOLOGIES AND PRACTICES The techniques used to collect distress data should ensure the collection of reliable information, efficient data collection and processing, and, primarily, a safe pavement evaluation. Three techniques are used to record pavement distresses: manual evaluation techniques, techniques that use imaging technologies, and techniques that use profilers. Imaging technologies and profilers are considered automated collection technologies, and the data collected may be analyzed with semiautomated or automated software. Manual techniques are based on the visual observation of distresses in the field by surveyors and then recording of the data on paper or by the use of some form of computerized technology. Imaging techniques use analogue and digital technologies. Pictures of the pavement surface are taken either discretely or continuously. The images are then analyzed with the aid of software to report the pavement distress. Profilers typically use laser or acoustic technologies to measure the transverse and longitudinal profile deformations of the pavement surface, like rut depths and faulting. Many agencies are replacing manual surveys with techniques that use digital technologies and profilers, given their higher levels of efficiency and improved safety, particularly in high-traffic urban areas. In addition, service providers have researched how they may improve their automated collection technologies (4, 5).

A questionnaire was prepared and sent to five pavement distress evaluation service providers to gather detailed information about their automated and semiautomated distress evaluation technologies and practices. The information provided was completed and updated after the companies were visited. The main findings obtained from the questionnaires and meetings with companies include the following: • Service providers primarily perform a semiautomated analysis of digital images using proprietary software, and the software programs that the different service providers use have relatively similar characteristics. • Semiautomated analysis software is flexible, can be adapted to the specific requirements of clients, and can evaluate any surface distress quantifiable through digital images. • QC is provided in accordance with client requirements. In general, QC is carried out during and after image analysis. Limited QC is applied before and during data collection. • Repeatability and reproducibility checks are applied only by some companies. • QA of image analysis could easily be applied, as the results of the analysis are stored graphically and as data reports. QA should be defined and applied by the clients; however, in most cases the clients do not apply it. • Illumination systems vary depending on whether they use a line scan or an area scan technology. Among the line scan technologies, substantial differences between lighting system technologies and efficiency may be observed according to their homogeneities and intensities. • The digital technologies currently available collect images with precisions of 2 to 3 mm, depending on the type of pavement, the surface texture, and the level of distress.

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• Clients typically contract an annual survey of 50% to 100% of their network, depending on the importance of the network. In most cases, 100% of the data collected are analyzed. • Companies do not recommend to their clients the application of sampling methods to analyze the data collected. This is because of the complexity of performing image analysis, a lack of representativeness, and difficulties with future data applications, such as preventive maintenance policies.

PROPOSAL OF MTO PAVEMENT DISTRESS GUIDELINES FOR NETWORK-LEVEL EVALUATIONS Need for Development of New Distress Guidelines The need to modify the current guidelines is funded by the conclusions of the previous study, findings obtained from a literature review, and the experience of the research team. In the previous study, some relevant variability was observed when the findings of semiautomated analysis were compared with those of manual surveys, and some relevant variability was observed between companies (2). Variability was particularly apparent when the extent of the distresses observed from digital images was compared with that observed by MTO evaluators. To reduce this variability when semiautomated analysis is applied at the network level, it was recommended that proper guidelines and specifications for surveying pavement distresses at the network level with automated and semiautomated technologies be provided. In particular, a clear and objective condition rating should be defined, and recommendations for QC/QA for the automated and semiautomated evaluation of road networks should be provided. In addition, MTO guidelines cover a large number of distresses and have high levels of detail. A high level of detail is not required at the network level, as it affects the efficiency and performance of the survey. An increased number of distresses tend to affect the variability and consistency between evaluators and technologies.

• Distresses in the vertical dimension are not measured by digital images; therefore, guidelines for automated and semiautomated digital distress data collection should focus only on distresses captured in a plan view. Rutting in flexible pavements and faulting in rigid pavements should be assessed at the network level with a laser or ultrasound technology. • The extent or density of distress per severity level should be registered objectively as a percentage of the total pavement surface. For this, 1 m of cracking is equivalent to a 1-m2 damaged area for distresses measured linearly. Almost all distresses presented as counts in PCC and COM pavements affect 25% of the slab surface; the exceptions were crack and joint spalling, which affect 15% of the slab surface. • Distress severity levels should be evaluated objectively and classified on a three-level scale: low, moderate, and severe.

Distresses Considered in Distress Guidelines for Network-Level Evaluations The definition and description of distresses follow the well-defined MTO guidelines. However, the extent and the severity of distresses were adjusted as described previously. Descriptions of each distress type and the extent and severity level are presented in detail in the final report of the study (3). Table 1 presents a summary of the distresses being considered for AC, ST, COM, and PCC pavements. Evaluations should be developed with digital technologies, with the exception of rutting and faulting, which should be assessed with laser or ultrasound technology at the network level. It must be noted that although raveling is difficult to measure with digital technologies, it is an important distress to be monitored. Given that two of the three service providers collected raveling data and that raveling is difficult to identify with digital images, the equations were defined without considering raveling. However, when the equations are validated in the field, raveling should be considered. For this, technologies complementary to digital images, such as video log images and windshield surveys, should be considered to include raveling in the equations.

Distress Guideline Definition The following criteria were considered for the development of the new guidelines: • Occasional or isolated distresses should be avoided when evaluations are performed at the network level. Only distresses associated with severe structural and drainage problems, such as potholes, should be considered at the network level.

TABLE 1

MTO DMINL Definition of DMINL Given the modifications to the distress guidelines, it was necessary to redefine DMI for its application at the network level. Three main adjustments were introduced into the traditional DMI methodology.

Summary of Distresses Considered for Each Pavement Type

Asphalt Concrete

Surface Treated

Composite

Portland Cement Concrete

1. Alligator cracking 2. Longitudinal wheel-track crack 3. Non-wheel-track longitudinal crack 4. Transverse crack 5. Potholes 6. Flushing 7. Rutting

1. Alligator cracking 2. Longitudinal wheel-track crack 3. Non-wheel-track longitudinal crack 4. Transverse crack 5. Potholes 6. Flushing 7. Rutting 8. Raveling

1. Alligator cracking 2. Diagonal and corner crack 3. Longitudinal crack 4. Transverse and reflection crack 5. Potholes 6. Flushing 7. Rutting 8. Raveling

1. Diagonal and corner crack 2. Transverse crack 3. Longitudinal and meandering crack 4. Joint and crack spalling 5. “D” crack 6. Faulting

8. Raveling

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First, the number of distresses considered per pavement type was reduced to six or seven. Second, the extent of the distresses was considered as a percentage of the damaged surface. Third, severity was accounted for consistently for all distress types and was classified from objective measures. DMI expresses the relative effect of each distress over the pavement condition in terms of weighting factors per distress type or parameter (βi). The principle used to define the weighting factors was the fact that the distresses considered by the new guidelines should quantify, with minimum error, the DMI estimated by the MTO traditional method. As presented in Equation 1, this is done by minimizing the error (⑀) produced by the reduction of distresses (distressi, in percent) and the modifications to severity and extent measures. n

DMI = DMI NL + ⑀ = ∑ β i ⴱ distressi + ⑀

(1)

i =1

Multiple linear regression analysis was used to evaluate the effect of reducing distress types in the calculation of DMI and to estimate weighting factors (βi). The analysis should be done separately for each pavement type, resulting in different DMINL equations for AC, ST, COM, and PCC pavements. To develop and validate DMINL, it was proposed that the same sections evaluated in the previous study be used. These included digital evaluations of 37 sections by three service providers, and four pavement types were considered: AC, ST, COM, and PCC pavements. Seventy percent of the data from the previous study was used to define the DMINL equations, and 30% was used for validation purposes. The sections covered a variety of pavement distresses, traffic volumes, and road geometries. Only main-line pavements were selected. Pavements from other road assets such as bridges, interchanges, and ramps were omitted. The section lengths varied from 500 m to 4 km. Most of the sections were located in southern Ontario, mainly in the township of Woolwich. Given that the data from the previous study were evaluated by the use of different criteria by the different service providers, distress densities and units were converted to areas. With this information, the distress units considered in the analysis could easily be converted to percent, as it is considered in the proposed distress guidelines for network-level evaluations. The following assumptions were made: • 1 m of cracking is equivalent to a 1-m2 area of distress; • A single pothole affects a 1-m2 area; • Transverse distresses (e.g., transverse cracking and joint sealant loss) span the entire lane width, a distance of 3.5 m; and • Distresses presented as counts in PCC and COM pavements, excluding transverse distresses and potholes, affect 25% of the slab, an area of 6 m2.

MTO DMINL, and the validation and final adjustment process. A flow diagram of the analysis performed is presented in Figure 2.

Step 1. Significance of Distress Elimination A linear regression analysis was performed to test the significance of eliminating distresses in the DMI methodology. The dependent variables were the DMI values for each pavement section calculated by the MTO traditional methodology by considering all distresses evaluated by each service provider. The independent variables were the sum of the severity and the extent rates for each section by considering the distresses included in the pavement distress guidelines for network-level evaluations (3). The analysis was done separately for each pavement type. Regression statistics were estimated by considering the multiple coefficient of correlation, the coefficient of determination (R2), and the typical error. Analysis of variance (ANOVA) was applied to test the overall significance of the regression. Hypothesis testing was carried out to evaluate the significance of each independent variable in the regression. From the ANOVA, it was concluded that the overall significance of the regression was acceptable for all pavement types. The best correlations were obtained for PCC and ST pavements, which presented R2 values of 96% and 92%, respectively. Almost all distresses were significant in PCC pavements; the exception was faulting. In ST pavements, alligator cracking and potholes presented levels of low significance. Flushing was found to be positively correlated with DMI (increasing). It is expected that all parameters and distresses should be negatively correlated with DMI. The distresses presented low levels of significance because all of these were present at a small extent and had a low level of severity in the data. An extreme case was flushing, for which a very small extent was identified in the test sections; this resulted in a contradictory performance, given that its effect over the DMI could be even smaller than the variance induced by the elimination of distresses in the regression. The correlations for AC and COM pavements were relatively low, presenting R2 values of 67% and 66%, respectively. This is explained by the fact that in both types of pavements, distresses present at considerable extents were eliminated. The low correlation was also reflected in the hypothesis testing of the significance of parameters. Four distresses presented a low level of significance in AC pavements, and five distresses presented a low level of significance in COM pavements. Given the lower correlations presented in AC and COM pavements, the DMIs were recalculated by considering only the distresses included in the pavement distress guidelines for network-level evaluations (3). In the following steps, both the initial and the recalculated DMI values were considered in the analysis.

Step 2. Design of MTO DMINL With all distress densities presented as areas, it was possible to compute the percentage of the total area of the section affected by each distress on the basis of the section length provided and an assumed section width of 3.5 m.

Data Analysis The analysis methodology applied to define DMINL considered three steps: the significance of distress elimination, the design of the

A second linear regression analysis was performed to design MTO DMINL guidelines. The dependent variable was the DMIs, and the independent variables were the extent of distress and its severity determined by consideration of objective measures, as proposed in the new DMINL methodology. The data from each service provider were incorporated into the regression analysis, as an independent section, given that the DMI calculation method should not be dependent on the collection technology. Regression statistics were determined by considering the

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STEP 1: SIGNIFICANCE OF DISTRESS ELIMINATION

Regression Analysis: DMI vs. DMI with Reduced Distresses

YES

Recalculate DMI with Reduced Distresses

STEP 2: DESIGN OF DMINL

Significant?

NO

Estimate Distress Extent and Severity with DMINL Regression Analysis (70% Data): DMI vs. DMINL Analysis of Results: ANOVA, Significance of Parameters, Analysis of Residuals

YES

Adjustment Required? NO

STEP 3: VALIDATION AND FINAL ADJUSTMENTS

Validation (30% Data): Test t of Difference in Means

Final Adjustments to Weighing Factors (Intercept Effect) FIGURE 2

Analysis methodology.

multiple coefficient of correlation, R2, and the typical error. ANOVA was applied to test the overall significance of the regression (F-test). Hypothesis tests (two-tailed t-tests) were carried out to evaluate the significance of each independent variable in the regression. Finally, for the independent variables that presented a low level of significance in a regression, an analysis of residuals was considered. The regression for the AC pavements was performed for the original DMI and the recalculated DMI, as described earlier. From the analysis, the best correlation was obtained with the recalculated DMIs. An R2 value of 83% was obtained. This value was higher than that obtained in the previous step. From ANOVA the result for the overall regression was significant. All distresses presented a negative correlation with DMI, as expected. However, flushing, transverse cracks, and alligator cracks presented low levels of significance. From the analysis of residuals, it was observed that the small extents for these distresses were present in AC pavements. The t-test statistic observed for flushing was very low; therefore, it was eliminated from the analysis and a new regression was performed. The regression for ST pavements was performed by using DMIs and considering all distresses. An R2 value of 68% was obtained. From ANOVA the result for the overall regression was found to be significant. Rutting presented a positive correlation with DMI; this was caused by the modification in the method used to calculate the extent of distress. It was therefore decided that rutting would be eliminated and a new regression would be performed. From the new regression, it was observed that the R2 value increased to 77%, and it was statistically significant.

For COM pavements, the best correlation was obtained with the original DMIs as dependent variables, which presented an R2 value of 85%. Almost all distresses presented a negative correlation with DMI; the exception was potholes. This is explained by the fact that potholes were observed to a very small extent and only in a few sections. Given this, potholes were eliminated and a new regression was performed. Longitudinal cracks, alligator cracks, and flushing presented low level of significance. Alligator cracks presented a small extent and a low level of severity and were observed in only two sections. Flushing was observed in only one section. The regression for the PCC pavements was performed from DMIs by considering all distresses from the previous study and estimating the extent of pavement distress by the new methodology. The regressions obtained had low levels of significance and low coefficients of correlation. This was caused by the fact that the assumption considered for the conversion of distress counts to the percentage of the damaged surface was not accurate. Because of this it was decided to estimate regressions by considering the extent (in percent) and severity by the traditional methodology. For this, the extents of the distress and their severities were classified on a percentage scale by converting the ratings of 0 to 4 to percentages. For this, all severities and extents were divided by 8, given that that is the maximum rating (four extents plus four severities) by the traditional DMI method. The R2 value obtained from the regression analysis was 97%. From ANOVA the result of the overall regression was found to be significant. Almost all distresses presented a negative correlation with DMI; the exception was faulting. Given this, it was decided that faulting would be eliminated and a new regression would be performed.

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From the new regression, it was observed that only transverse cracks presented a low level of significance; however, their significance increased after the elimination of faulting. The elimination of faulting did not affect the R2 value of the regression, which is evidence of the low level of significance of the distress in the regression.

Step 3. Validation and Final Adjustments Validation was carried out by comparing the observed DMIs with the calculated DMIs by use of the 30% of the data not considered in the regressions. The statistical method applied was the t-test for differences in means. As a result, all regressions were successfully validated and presented very small differences in the means for both samples. A final adjustment was required to adapt the regression to a scale that ranged from 10 to 0. Given that the intercepts in none of the four equations was 10, all parameters were corrected by multiplying them by the ratio 10/intercept.

Final Equations for DMINL The general expression developed in this research for the MTO DMINL is presented in Equation 2. The weighting factors (βi) for each pavement type are presented in Table 2. TABLE 2 Weighting Factor for Each Distress Type

Distress Type AC Rutting Longitudinal (no WP) Longitudinal (WP) Transverse Alligator Pothole COM Flushing–bleeding Rutting Longitudinal (WP and no WP) Transverse Alligator

Weighting Factor (βi)

−1.63 −1.56 −1.45 −2.13 −3.56 −217.07 −92.89 −2.76 −1.07 −64.91 −869.71

ST Flushing–bleeding Longitudinal (WP and no WP) Transverse Alligator

−3.42 −0.94 −144.07 −9.55

Pothole

−629.48

PCC Joint–crack spalling Long meander crack Trans crack

−2.67 −0.88 −0.10

Corner cracks

−1.82

NOTE: WP = wheelpath.

n

DMI NL = 10 − ∑ β i ⴱ distressi

(2)

i =1

From Table 2 it is observed that the magnitude of some parameters is very high. This is observed in distresses present sporadically and at a small extent. Given this, when the equations under the regression analysis based on DMI weighting factors are developed, the parameters tend to be high, given the small extent but the high level of importance of the distress.

RECOMMENDATIONS FOR APPLICATION OF QC/QA TO SEMIAUTOMATED AND AUTOMATED DIGITAL SURVEYS The application of an efficient QC/QA system is strongly recommended when pavement distresses at the network level are evaluated by using semiautomated and automated technologies. Various studies have demonstrated that the application of quality control before, during, and after data collection reduces the variability between evaluations (6–10). In particular, when images are analyzed with semiautomated software, repeatability within an operator and reproducibility between operators can be reduced significantly when the images are of good quality. A QC/QA system should include at least four main activities: QC before data collection, QC during data collection, QC during data analysis, and QA after data analysis.

QC Before Data Collection • Service providers should have a detailed QC plan before they survey the network. • As part of the QC plan, service providers performing semiautomated and automated evaluations should have a training system and qualification procedure for operators using a digital collection technology and operators working with semiautomatic image analysis software. • Control sites should be tested before a survey campaign to check the repeatability of the data collection technology. • DMI should be calibrated before the start of digital image collection. This should be performed periodically and should be performed before a survey. • The correct positioning of camera and lighting systems should be checked. The camera and lighting system beams should be properly mounted to obtain measurements. • Light homogeneity and intensity should be controlled before a survey. For each specific lighting system, typical mean, maximum, and minimum luminance values should be controlled in different points of the image by using a lux-measuring device. • Image dimensions should be checked before data collection. Image quality should be checked with a monitor before data collection. Special caution should be taken to avoid shadows in the image, in particular, those produced by the survey vehicle.

QC During Data Collection • The measurement parameters (such as the focal distance, speed, and exposure time) should be controlled during surveys. Service

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providers should consider these parameters according to the capabilities of their technologies. To avoid blurring problems, parameters may vary between pavement types. • Periodic section length controls should be made during data collection. It is recommended that the data be stored and checked every 10 to 20 km. • Wandering and erratic vehicle movements should be continuously controlled and avoided. • Survey speeds should be within the range of device capabilities. Line scan cameras may present problems at low speeds because of line superposition, whereas area scans may present blurring problems at high speeds.

QC During Data Analysis • Service providers should periodically check the consistency between operators using semiautomatic image analysis software. To check the consistency of evaluators, repeatability checks for each operator (evaluator consistency) and the reproducibility between operators (interevaluator consistency) should be performed at the same control site. • Service providers performing automated analysis should calibrate image parameters during a setup phase. Distress detection parameters should be set to minimize the occurrence of false-positive detection and false-negative detection. The repeatability of crack maps should be checked during the analysis, and the parameters should be adjusted when required.

QA After Data Analysis The accuracy of evaluations for each service provider should be monitored during and after surveys by using blind monitoring sites randomly selected throughout the network. It is recommended that this procedure be performed each survey year. As part of QA, this process should be encouraged by agencies and companies contracting the evaluations of service providers.

CONCLUSIONS AND RECOMMENDATIONS Conclusions The following conclusions were drawn from the study: • New guidelines for the evaluation of pavement distresses at the network level were developed. MTO pavement distress guidelines for network-level evaluations (3) were defined by considering the future use of automated and semiautomated technologies for the collection and analysis of pavement distresses. The main adjustments to the MTO guidelines were – Reduction of the types of distresses being collected; – Reduction of the number of severity levels from five to three; and – Definition and adjustment of the extent of distress measured, which is intended to be objective and clear. • The MTO DMINL was designed by considering – Its suitability for the application of semiautomated and automated technologies by consideration of digital devices and profilers,

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– Inclusion of distresses presented in MTO pavement distress guidelines for network-level evaluations (3), and – Consideration of the extent of distress and severities evaluated from objective measures. • The following distresses were excluded from the analysis, as they were not available from data collected in the 2006 MTO Highway Infrastructure Innovation Funding Program study: D-cracking in PCC pavements, diagonal or corner cracks in COM pavements, and longitudinal cracks accounted separately for the wheelpath and the nonwheelpath in ST pavements. • The evaluation of significance of distress elimination revealed that overall regressions were significant after the elimination of distresses from the DMI traditional methodology. However, AC and COM pavements presented low R2 values: 67% and 66%, respectively. • DMINL equations were designed with 70% of the data in the database. From the analysis, it was observed that PCC presented low correlations for the new measurement method. This was mainly caused by the fact that distresses were collected in dissimilar ways by different service providers and that the distresses measured in counts varied significantly in their extent between methodologies. • From the regression analysis, some distresses were eliminated from the DMINL expressions. These were flushing and bleeding in AC pavements, rutting in ST pavements, potholes in COM pavements, and faulting in PCC pavements. • The methodology was validated with the remaining 30% of the data. All equations were successfully validated.

Recommendations The following recommendations are proposed: • The application of the proposed QC/QA methodology is recommended when pavement distresses are evaluated at the network level by using semiautomated and automated technologies. This will improve the repeatability and reproducibility of the analyses performed by evaluators. • Caution should be taken when DMINL equations are used, as they were based on distresses collected by the use of different criteria between service providers, the distresses varied within narrow ranges, and little QC was applied during the measurements. • Given the information presented above and the fact that new technologies are available, it is highly recommended that a field validation of the pavement distress guidelines for network-level evaluations (3) and DMINL be developed. • During the field validation, distresses eliminated from the equations after the statistical analysis should be assessed and included when appropriate. In particular, the need to consider raveling and flushing in AC pavements and faulting in PCC pavements should be evaluated. • The parameters from Table 2 should be evaluated and adjusted, when appropriate, as a result of the field validation. • The field validation should include a statistical analysis that compares the DMIs obtained from manual surveys and the DMINL values obtained manually and by semiautomated and automated analyses. With this, the effects of the transition from DMI and DMINL will be identified and, when possible, corrected. • Pavement distress guidelines for network-level evaluations and DMINL were developed for mainline pavements; other assets, such as ramps and interchanges, were not considered. It is therefore recommended that the findings of the study be incorporated and adapted for the evaluation of other pavement assets.

Chamorro, Tighe, Li, and Kazmierowski

ACKNOWLEDGMENTS The research team gratefully acknowledges the service providers, Applied Research Associates, Roadware, and Stantec, for their time and resources during the study. In particular, the authors thank Khaled Helali, Harry Sturm, Richard Fox-Ivey, Michael Nieminen, Alanna Zhang, Lyndsay Kawamoto, D. J. Swan, David Hein, Glenn Smith, and Darel Mesher for their support. The funding provided under the Ministry of Transportation of Ontario Highway Infrastructure Innovation Funding Program to support this work is gratefully appreciated.

REFERENCES 1. Apply PMS2 in Corridor Investment Planning. Pavements & Foundations Section, Materials Engineering and Research Office, Ministry of Transportation of Ontario, Downsview, Ontario, Canada, 2007. 2. Tighe, S., R. Capuruco, and A. Jeffray. Evaluation of Semi Automated/ Automated Pavement Condition Surveys: An Ontario Field Study. University of Waterloo, Waterloo, Ontario, Canada, 2006. 3. Chamorro, A., S. Tighe, and A. Marcon. Final Report HIIFP 2007 Project: Improving Network Level Pavement Management Through Rationalization and Utilization of Automated/Semi-Automated Technologies. University of Waterloo, Waterloo, Ontario, Canada, May 2008.

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4. Bennett, C., A. Chamorro, C. Chen, H. de Solminihac, and G. Flintsch. Data Collection Technologies for Road Management, Version 2.0. East Asia Pacific Transport Unit, World Bank, Washington, D.C., 2007. 5. McGhee, K. H. NCHRP Synthesis of Highway Practice 334: Automated Pavement Distress Collection Techniques. Transportation Research Board of the National Academies, Washington, D.C., 2004. 6. Rada, G., A. Simpson, and J. Hunt. QC/QA Processes for LTPP Photographic Distress Data Collection and Interpretation. Presented at 83rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2004. 7. Rada, G. Study of LTPP Distress Data Variability, Vol. I and II. Report FHWA-RD-99-074 and -075. FHWA, U.S. Department of Transportation, 1999. 8. Peggar, H., O. Selezneva, G. Mladenovic, and J. Kennedy. Automated Data Integrity Quality Assurance of Automated Pavement Distress Data. Presented at 83rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2004. 9. Chamorro, A. Implementación y Validación de una Tecnología Digital para la Inspección Visual de Pavimentos en Chile. MS thesis. Pontificia Universidad Católica de Chile, Santiago, Chile, 2004. 10. Chamorro, A., H. de Solminihac, and A. Caroca. Development and Validation of Semi-Automated Software for the Analysis of Pavement Distresses. Presented at 7th International Conference on Managing Pavement Assets, Calgary, Alberta, Canada, June 2008. The Pavement Monitoring and Evaluation Committee sponsored publication of this paper.