Standard Definitions for Common Types of Pavement Cracking (2020)

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Standard Definitions for Common Types of Pavement Cracking 26 C H A P T E R 3 Role of Cracking Data in Decision- Making Processes Introduction This chapter reviews the SHAs applications of cracking data for both network and project level decision-making, and also the acceptable levels of precision and bias for collected data based upon the relative impact of a crack type. This includes how cracking data are used for pavement management, and which crack type(s) are commonly used in the decision-making process. Knowing this information will aid in determining acceptance criteria for tracking crack development and deterioration and developing standard definitions for comparing cracking data results. Network Level Decision-Making The primary results of network level analysis include maintenance and rehabilitation needs, funding allocations, forecasted future impacts for various funding options considered, and prioritized list of candidate projects needing repair (Smith 1998). The network level pavement management decision-making can be supported by pavement condition information via condition indices, which are frequently used in pavement management to report and compare pavement conditions, predict changes in pavement condition, identify pavement sections experiencing deterioration, and recommend the appropriate treatment type and timing (AASHTO 2012). Composite and individual indices are commonly used in pavement management. Composite indices aggregate multiple types of condition data into a single index representing the overall pavement condition. Individual indices are typically calculated for a single type of pavement deterioration (e.g., structural cracking) or single distress type (e.g., alligator or fatigue cracking). Composite pavement condition indices include PCI and present serviceability index (PSI). PCI is defined in the ASTM D6433 and includes a series of pavement distresses (Table 7). Cracking information plays a significant role in determining the pavement PCI number.

Standard Definitions for Common Types of Pavement Cracking 27 Table 7 Pavement Distresses Identified in ASTM D6433 (ASTM 2018) Asphalt Pavements Jointed Concrete Pavements 1. Alligator Cracking 2. Bleeding 3. Block Cracking 4. Bumps and Cags 5. Corrugation 6. Depression 7. Edge Cracking 8. Joint Reflection Cracking 9. Lane/Shoulder Drop Off 10. Longitudinal and Transverse Cracking 11. Patching and Utility Cut Patching 12. Polished Aggregate 13. Potholes 14. Railroad Crossing 15. Rutting 16. Shoving 17. Slippage Cracking 18. Swell 19. Weathering/Raveling 1. Blow up/Buckling 2. Corner Break 3. Divided Slab 4. “D” Cracking 5. Faulting 6. Joint Seal 7. Lane/Shoulder 8. Linear Cracking 9. Patching (Large) 10. Patching (Small) 11. Polished Aggregate 12. Popouts 13. Pumping 14. Punchout 15. Railroad Crossing 16. Scaling 17. Shrinkage 18. Spalling Corner 19. Spalling Joint The PSI was developed as part of the AASHO Road Test in order to objectively measure pavement serviceability (Carey and Irick 1962, Papagiannakis and Masad 2012). The PSI was developed to incorporate measures of smoothness, rutting, cracking, and patching. PSI is calculated as shown in Equations 1 and 2 for asphalt and concrete pavements, respectively. PSI (asphalt) = 5.03 – 1.91log(1 + SV) – 1.38(RD)2 – 0.01(C + P)1/2 (1) PSI (concrete) = 5.41 – 1.80log(1 + SV) – 0.09(C + P)1/2 (2) where, SV = Slope variance in the two wheel paths of a section from CHLOE profilometer. RD = Mean rut depth (in.) of asphalt pavements. C = Cracking information (ft2/1000 ft2). P = Patching information (ft2/1000 ft2). Some agencies calculate individual condition indices to assist in identifying the most appropriate type of maintenance and repair strategies. For example, by using a structural distress index, a non-structural distress index, and a roughness index, pavement managers can quickly determine whether a section requires a structural repair or whether a functional improvement would be more appropriate (AASHTO 2012). The structure/fatigue index and non-structural cracking index are the two most commonly reported individual indices (Wolters and Zimmerman 2010). An example calculation of the Structural Index for asphalt pavements is shown in Equation 3. Structural Index = 100 – Minimum[(Fatigue 1 DV + Fatigue 2 DV + Fatigue 3 DV), 100] (3)

Standard Definitions for Common Types of Pavement Cracking 28 where, Fatigue 1 DV = Low Severity Fatigue Deduct Value. Fatigue 2 DV = Medium Severity Fatigue Deduct Value. Fatigue 3 DV = High Severity Fatigue Deduct Value. For jointed concrete pavements, the Slab Cracking Index is calculated by taking deducts for corner breaks, longitudinal cracking, broken slabs, patching, and transverse cracking from the value of 99. Project Level Decision-Making Project level pavement management provides a systematic procedure for selecting the most cost- effective and feasible maintenance, rehabilitation, and reconstruction strategy for a selected pavement section within available funds and other constraints (Horton 1990, Haas et. al. 1994, AASHTO 2012). The cause and extent of pavement deterioration are required for determining treatment type and timing and should be determined based on pavement distress data. Pavement evaluation requires a systematic approach to adequately quantify and analyzing distresses that influence the selection of the appropriate maintenance and rehabilitation treatments and strategies. Ideally, when the percent of cracking reaches a certain threshold level, interventions should be implemented to prevent further deterioration. For instance, a thin overlay over non-load related cracked pavements is applied to stop or slow crack progress, or a thick overlay or reconstruction alternative is considered to address reflective or load related cracking. On the other hand, surface treatments (e.g., chip seal, microsurfacing) are not recommended for structural failures, severe thermal cracking, or pavement deterioration. The prescribed treatments vary by agency depending on the type, severity, and extent of cracking. Therefore, detailed quantification of different cracking categories is desired for decision-making at the project level. In addition, cracking data can also be used to support the planning and programming of pavement preservation, rehabilitation, and reconstruction activities, warranty enforcement, and capital improvement. For example, the extent of cracking affects the cost of crack filling/sealing (NDOR 2002, Santos and Ferreira 2013). Lee et al. (2008), based on a life-cycle cost analysis using an alligator cracking performance model, found that it is more cost-effective to apply pavement preservation treatments than rely only on rehabilitation treatments. Precision and Bias Levels Many SHAs are developing procedures and guidelines for managing the quality of pavement data collection to ensure collected data meets pavement management needs (Flintsch and McGhee 2009, Ong et al. 2010, Pierce et al. 2013). Pavement data quality is receiving increased attention because: (1) data quality has a critical effect on the pavement management business decisions, (2) data collection and processing is the costliest components of operating a PMS, and (3) quality management is necessary to ensure the collected data meets the requirements of the PMS. The recent National Performance Management Measures includes specific requirements for data quality (FHWA 23 CFR 490). The main techniques used for pavement data quality management include: (1) calibration and verification of equipment and/or analysis criteria before

Standard Definitions for Common Types of Pavement Cracking 29 data collection, (2) testing of known control or verification sites before and during data collection, and (3) software routines for checking the reasonableness, consistency, and completeness of the data (McGhee 2004, Flintsch and McGhee 2009, Pierce et al. 2013). A variety of methods are available for ensuring the continued collection of satisfactory quality data. Network level data checks often include statistical testing of the differences between the mean values (of the parameters being evaluated) for the quality control or acceptance samples and the production surveys for the same sections (Flintsch and McGhee 2009). The differences are computed for each sampling unit and the mean difference is tested via paired t-test against the null value using a pre-selected level of confidence (typically 95%). For project level data checks, the mean comparison may not be applicable because some individual differences can exceed the acceptable range due to limitations and the production data collection and processing technology (Selezneva et al. 2004). Statistical tests based on individual measurement ratings may be more appropriate to verify the quality of collected data at the project level. These tests involve selecting a sample from a dataset, rating each individual observation within this sample using established pass-fail criteria for minimum acceptable quality, and concluding whether the whole dataset satisfies criteria for minimum acceptable quality based on the number of “failed” observations in the sample. An important aspect of the quality acceptance plan includes the establishment of acceptance criteria in terms of data accuracy and precision. The acceptance criteria establish how much variation is allowed between the ground truth (or reference value) and data measured during the distress survey. For example, the Virginia DOT defines precision (±12%) and bias (±5%) criteria for PCI (Flintsch and McGhee 2009) and the Quebec Ministry of Transportation bias requirements for cracking data include: (1) Cracking index – the computed index must be within ±15% of the ministry measured index; (2) Longitudinal cracking – ±32.8 ft./328 ft. (±10 m/100 m) in 100% of the cases and ±16.4 ft./328 ft. (±5 m/100 m) in 80% of the case; (3) Transverse cracking – ±5 cracks/328 ft. (±5 cracks/100 m) in 100% of the cases and ±3 cracks/328 ft. (±3 cracks/100 m) in 80% of the cases (Ong et al. 2010). The Pennsylvania DOT established acceptance criteria for cracking data as shown in Table 8. Table 8 Proposed Initial Pavement Condition Acceptance Criteria (Ganesan et al. 2006) Reported Value Initial Criteria Percent Within Limits (PWL) Recommended Action if Criteria Not Met Individual Distress Severity Combination  30 percent 90 percent Feedback on potential bias or drift in ratings. Retrain on definitions. Total Fatigue Cracking  20 percent 90 percent Reject deliverable. Total Non-fatigue Cracking  2 percent 90 percent Reject deliverable. Transverse Cracking, JPCP  20 percent 90 percent Reject deliverable.

Standard Definitions for Common Types of Pavement Cracking 30 Summary This chapter reviews SHAs applications of cracking data for both network and project level decision-making, and practices of using levels of precision and bias for acceptance of collected data. The key findings of the review include:  Cracking data is important to calculate pavement condition indices for network level decision-making.  Pavement cracking condition affects the selection of maintenance and rehabilitation treatments and strategies at project level.  SHAs develop procedures and guidelines to ensure collected cracking data meets acceptance criteria. It is clear from the review that proper cracking definitions are the basis to effectively apply cracking data in various pavement engineering practices.