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Qureshi, W.S.;  Hassan, S.I.;  Mckeever, S.;  Power, D.;  Mulry, B.;  Feighan, K.;  O’sullivan, D. Pavement Surface Types and Distress Assessment Indicators. Encyclopedia. Available online: https://encyclopedia.pub/entry/36836 (accessed on 12 April 2024).
Qureshi WS,  Hassan SI,  Mckeever S,  Power D,  Mulry B,  Feighan K, et al. Pavement Surface Types and Distress Assessment Indicators. Encyclopedia. Available at: https://encyclopedia.pub/entry/36836. Accessed April 12, 2024.
Qureshi, Waqar S., Syed Ibrahim Hassan, Susan Mckeever, David Power, Brian Mulry, Kieran Feighan, Dympna O’sullivan. "Pavement Surface Types and Distress Assessment Indicators" Encyclopedia, https://encyclopedia.pub/entry/36836 (accessed April 12, 2024).
Qureshi, W.S.,  Hassan, S.I.,  Mckeever, S.,  Power, D.,  Mulry, B.,  Feighan, K., & O’sullivan, D. (2022, November 28). Pavement Surface Types and Distress Assessment Indicators. In Encyclopedia. https://encyclopedia.pub/entry/36836
Qureshi, Waqar S., et al. "Pavement Surface Types and Distress Assessment Indicators." Encyclopedia. Web. 28 November, 2022.
Pavement Surface Types and Distress Assessment Indicators
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Pavement or road surfaces can be categorized into four general classes, i.e., asphalt, concrete, gravel, and brick and block. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses.

deep learning image segmentation pavement surface condition index

1. Introduction

Two vital elements of road pavement management are inventory management and periodic condition evaluation; both are used to set future priorities for pavement construction management and maintenance. Pavement refers to hard surfaces used for motor vehicles. A complete pavement management system consists of inventory data collection (i.e., width, length, shoulder, and pavement type) and pavement characteristic assessment, i.e., (roughness (ride), surface condition (distresses), surface skid resistance, pavement strength, and deflection). The current pavement networks, including motorways across a country, are developed and modernized over centuries. The construction, width, and length of a pavement depend on the traffic it will carry and the type of connection it will make. They are classified into different categories; for example, in Ireland, they are classified as motorways, national primary, national secondary, regional roads, and local roads [1]. A common way to periodically evaluate surface condition, including distresses on a pavement network, is for the civil authority to conduct a visual surface condition assessment and a ride smoothness test. Surface condition is assessed through visual surveying and usually consists of three steps: (1) pavement condition data collection, (2) distress identification and quantification, and (3) assigning a pavement rating index to a stretch of a pavement using a standard rating scale (e.g., pavement surface evaluation rating-PASER [2]) that is typically localized to a specific geographical region [3]. Figure 1 gives a complete picture of the three-step process. The data collection is followed by distress occurrence, severity measurement, and pavement condition rating decisions.
Figure 1. Pavement condition rating process.
Data collection, the first step of surface visual assessment, is usually carried out by specially adapted vehicles (or, more recently, on devices such as smartphones [4] or unmanned aerial vehicles) for visual surface surveying. The vehicle is fitted with a computer, Global Positioning System (GPS) sensor, and an imaging sensor. In step 2, pavement distresses are identified and quantified using their shape, size, and texture. Due to environmental and geographical conditions and the actual pavement construction process, pavement distresses may vary in shape, size, and texture. Variations can also be caused by different image capture technologies and the placement of sensors in specialized vehicles used to collect pavement data. In step 3, a rating is assigned to a stretch of pavement based on distress identification and quantification from step 2. A rating is applied to an initial stretch after inspection and then will be adjusted along the road if the pavement surface changes noticeably. The length of the stretch of road typically ranges from 50 m to 200 m, while the width of the stretch ranges from 4 m to the entire width of the road. The rating is performed directly by civil authority staff or subcontracted to private companies. Civil authorities use this condition rating to estimate pavement service life and treatment measures to improve the condition.
Maintenance and improvement of pavements are expensive. For example, Ireland’s government spent 850 million Euros in 2021 to improve and maintain local, regional, and national primary and secondary roads [5]. There are 5413 km of national highways (primary, secondary, and motorways), 13,124 km of regional roads, and 81,300 km of local roads in Ireland. It totalled 99,830 Km of road network in 2018 in Ireland, meaning 95% of the road network in Ireland consists of regional and local roads [5].

2. Pavement Surface Types and Distress Assessment Indicators

2.1. Pavement Surface and Distress

Pavement or road surfaces can be categorized into four general classes, i.e., asphalt, concrete, gravel, and brick and block [6]. Asphalt, also known as flexible pavement, is widely used to construct national, regional, or local roads across the road network and has different sub-categories depending on its construction. Over 90% of the total European road network has an asphalt surface. Concrete surfaces are usually used in urban environments and can be subdivided into joined cement concrete and continuously reinforced concrete surfaces [7]. Concrete pavements are expensive and time-consuming to construct, but they are typically more potent and durable than asphalt roadways. They are more common in the USA; for example, approximately 60 percent of the interstate system in the USA is concrete. Pavement condition assessment considers several pavement characteristics, i.e., roughness, surface condition (distress detection), surface skid resistance, and pavement strength. Surface condition plays a significant role in pavement assessment, which requires pavement distress detection and quantification. Pavement surface distresses that occur in different geographical regions can be divided into six groups, i.e., cracks, surface openings, surface deformation, surface defects, joint deficiencies, and miscellaneous distress [7][8] (see Table 1).
Table 1. A comprehensive list of distresses in asphalt rural flexible, asphalt, urban flexible, joined Portland concrete, continuously concrete reinforced roads, and segregation in six main groups and their sub types [6][7].
Most of these distresses can be detected generally through visual inspection (standard practice) of pavement surfaces, and their severity and quantity can be recorded using manual measurement tools [7]. Visual distresses appears on the surface due to wear and tear, which may indicate a fault in the construction. It may appear differently in rural and urban regions, depending on the surface type, the severity (low, medium, high) of the underlying problem, and other environmental conditions.

2.2. Pavement Assessment Indicators

Measuring different pavement characteristics is essential in long-term pavement performance incorporating all or a subset of pavement characteristics to conduct pavement assessments. These condition rating systems vary from country to country (or within a state in the USA), considering local variations, the characteristics of the pavements, and economic conditions.
Pavement characteristics that are generally separately measured include pavement roughness; a vital pavement characteristic measured on a rating index known as the International Roughness Index (IRI) [9]. It is estimated in a moving vehicle from a longitudinal pavement profile with sensors capable of measuring vertical movement [10][11]. Another essential characteristic is transverse deflection, also known as rut depth, measured manually or using sensors that generate transverse pavement profiles [12]. Visual pavement condition assessment requires distress detection and quantification to measure pavement conditions and is more reliable than other methods are. Engineers and professionals have proposed several standards for visual surface assessment, such as Pavement Surface Evaluation Rating (PASER) [2], Pavement Condition Index (PCI) [9][13], Pavement Surface Condition Index (PSCI) [14], and the Road Condition Indicator (RCI) [15]. Table 2 lists different pavement condition ratings used around the world. The standard ratings of various regions differ in scale granularity, formula to estimate a value on the rating scale, and data acquisition procedure.
Table 2. A summary of different pavement condition rating systems used by regional road transportation departments or proposed by academics.
The earliest work in creating a standardized condition assessment scale dates from the 1960s in the United States [16]. The scale used two pavement characteristics-pavement roughness and visual surface distress identification, to determine the Present Serviceability Index (PSI) ranges from zero (very poor) to five (very good condition). A roughness index was carried out by 3–5 individual raters trained to qualitatively estimate pavement roughness by driving a vehicle on the pavement. It was followed by visual inspection for cracks, patches, and potholes. These two were then combined mathematically to calculate the PSI score (0–5) [16].
Over the years, data acquisition techniques have evolved; different pavement condition assessment ratings have been proposed that mainly focus on assessing the different types of pavement characteristics, their quantity, and their effect on the overall condition of the pavement. PASER is a direct rating on a scale of 10–1 (9–10 is excellent condition, while 2–1 is extremely poor). On the other hand, the ASTM standard for pavement is PCI, a rating on a scale of 100–0 (85–100 is a good condition, while 0–10 is completely deteriorated. It is mathematically based on distress occurrence and severity level. The Irish PSCI [8][14][17] rating is on a scale from 1–10, similar to PASER, where index-1 is the lowest (surface completely worn out or failed), and index-10 (no distress, new pavement) is the highest. It covers flexible urban pavements, urban concrete pavements, and flexible rural pavement separately. PSCI ratings are given to continuous stretches of pavements with similar conditions, with 200 m being the minimum length to have their distinct rating [17]. In the United States, the Federal Land Transportation program recommends visual distress detection based on PASER for direct pavement condition evaluation [18]. Some transportation departments (or road authorities) that use scales similar to PCI use a subset of the visual distresses and roughness index to calculate the PCI rating. For example, the New Zealand Road Assessment and Maintenance Management System (RAMM) assigns a CI (Condition Index) from 0–100 (0–Excellent—100–Failed); it includes a visual inspection of not only the pavement but the surface water channels along the pavement [19]. China uses the Chinese Pavement Condition Index (CPCI), a scale similar to PCI, and considers cracking, raveling, potholes, rutting, and roughness. Japan used the Maintenance Control Index (MCI) until 2005, a function of cracking, rutting, and roughness, on a scale of 10 to 0 [20]. After 2005, the Ministry of Transportation Japan has used RRI, which is a function of cracking ratio, rutting depth, and International Roughtness Index [20]. A similar index is used in Tajikistan under Japan International Cooperation Agency [21].
The RCI is a rating from 1–4 (with 1 meaning no physical deterioration, while 4 is severe deterioration), adopted in England, Wales, Scotland, and Northern Ireland, and fuses visual condition and gauging parameters of pavement condition [15]. In Germany, the RMA (Road Monitoring and Assessment) protocol rate the pavement into four categories based on visual distresses [22]. Some states use four classes in the USA, i.e., Good, Fair, Poor, Very-Poor, as a condition scale based on the original PSI rating. In some countries, such as India and Brazil, a visible pavement distress condition rating on a scale of 0 to 3 is used [23]. Ratings are based on cracking, rutting, raveling, patching, and potholes, while roughness is not considered [3]. Pavement condition surveys of national and local roads are commonly conducted annually, every two years, or every five years in different regions across the world (for example, in Ireland, they are conducted every two years, while in Florida, state highway surveys are completed annually [24]). Therefore, these survey methods should be quick, fast, reliable, and economical.
In summary, different regions have different ways of performing pavement condition rating; some take roughness and visual condition combined to assign a rating from a standard scale (e.g., China, Japan, and some states in the USA), while others rate only a subset of visual distress (e.g., UK, Ireland, Brazil, Germany, New Zealand, India, and some states in the USA). Some of these indices are very granular (1 to 100) such as PCI in some parts of USA versus that (0 to 3 scale) used in Brazil/India. The choice of scale has evolved with economic prosperity and maturity of the road network.

References

  1. Roads in Ireland—Wikipedia. Available online: https://en.wikipedia.org/wiki/Roads_in_Ireland (accessed on 16 September 2022).
  2. PASER Asphalt Roads Pavement Surface Evaluation and Rating PASER Manual Asphalt Roads. 2002. Available online: http://tic.engr.wisc.edu (accessed on 3 February 2022).
  3. Peraka, N.S.P.; Biligiri, K.P. Pavement asset management systems and technologies: A review. Autom. Constr. 2020, 119, 103336.
  4. Sholevar, N.; Golroo, A.; Esfahani, S.R. Machine learning techniques for pavement condition evaluation. Autom. Constr. 2022, 136, 104190.
  5. Government of Ireland. Local Authority Budgets 2021. 2021. Available online: https://assets.gov.ie/139273/8554c7e7-d87c-4185-8cc1-32c8bf51c5c3.pdf (accessed on 20 October 2022).
  6. Road Pavement Surface Types. Available online: https://interpro.wisc.edu/tic/?csis-search-options=site-search&s=paser&submit=Search (accessed on 3 February 2022).
  7. Miller, J.S.; Bellinger, W.Y. Distress Identification Manual for the Long-Term Pavement performance Program. Georgetown Pike, May 2014. Available online: https://highways.dot.gov/sites/fhwa.dot.gov/files/docs/research/long-term-pavement-performance/products/1401/distress-identification-manual-13092.pdf (accessed on 24 March 2022).
  8. Mulry, B.; McCarthy, J. A Simplified System for Assessing the Condition of Irish Regional and Local Roads. Civ. Eng. Res. Irel. 2016, 2016, 1–7. Available online: https://ceri2016.exordo.com/files/papers/97/final_draft/097.pdf (accessed on 14 March 2022).
  9. Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys. Available online: https://www.astm.org/d6433-09.html (accessed on 16 February 2022).
  10. Gandhi, J.R.; Jaliya, U.K.; Thakore, D.G. A Review Paper on Pothole Detection Methods. Int. J. Comput. Sci. Eng. 2019, 7, 379–383.
  11. Prasad, J.R.; Kanuganti, S.; Bhanegaonkar, P.N.; Sarkar, A.K.; Arkatkar, S. Development of Relationship between Roughness (IRI) and Visible Surface Distresses: A Study on PMGSY Roads. Procedia Soc. Behav. Sci. 2013, 104, 322–331.
  12. Ragnoli, A.; De Blasiis, M.R.; Benedetto, A. Di Pavement Distress Detection Methods: A Review. Infrastructures 2018, 8, 14531–14544.
  13. ASTM D5340-12; Standard Test Method for Airport Pavement Condition Index Surveys. ASTM: West Conshohocken, PA, USA, 2020; Volume 4, pp. 1–55.
  14. Mccarthy, J.; Fitzgerald, L.; Mclaughlin, J.; Mulry, B.; O’brien, D.; Dowling, K. Rural Flexible Roads Manual—Pavement Surface Condition Index; Department of Transport, Toursim and Sports: Dublin, Ireland, 2014; Volume 4.
  15. Network Condition & Geography Statistics Branch Department for Transport, Technical Note: Road Condition and Maintenance, London, November 2021. Available online: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1032372/technical-guide-to-road-conditions.pdf (accessed on 4 March 2022).
  16. Carey, W.N.; Irick, P.E. Highway Research Board, The Pavement Serviceability-Performance Concept. 1960. Available online: http://onlinepubs.trb.org/Onlinepubs/hrbbulletin/250/250-003.pdf (accessed on 24 March 2022).
  17. Mulry, B.; Feighan, K.; McCarthy, J. Development and Implementation of a Simplified System for Assessing the Condition of Irish Regional and Local Roads. In Proceedings of the 9th International Conference on Managing Pavement Assets, Washington, DC, USA, 18–21 May 2015; pp. 1–17. Available online: https://vtechworks.lib.vt.edu/handle/10919/56413 (accessed on 14 March 2022).
  18. Federal Lands Transportation Program Instructions for FY 2019-2020 Investment Strategy (Competition). 2018. Available online: http://fltp-2019-2020-investment-strategy-guidance-2018.pdf (accessed on 20 October 2022).
  19. New Zealand, T. RAMM road condition rating and roughness manual (Manual No. PFM6). Available online: https://nzta.govt.nz/ (accessed on 20 October 2022).
  20. Kazuyuki, K. Pavement Maintenance in Japan. Available online: https://www.road.or.jp/international/pdf/32_AM6.pdf (accessed on 15 November 2022).
  21. Japan International Cooperation Agency, Pavement Inspection Guideline. Available online: https://openjicareport.jica.go.jp/pdf/12286001_01.pdf (accessed on 15 November 2022).
  22. Eisenbach, M.; Stricker, R.; Seichter, D.; Amende, K.; Debes, K.; Sesselmann, M.; Ebersbach, D.; Stoeckert, U.; Gross, H.M. How to get pavement distress detection ready for deep learning? A systematic approach. In Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 14–19 May 2017; pp. 2039–2047.
  23. Rateke, T.; Justen, K.A.; Von Wangenheim, A. Road Surface Classification with Images Captured from Low-cost Camera-Road Traversing Knowledge (RTK) Dataset. Revista De Informática Teórica E Aplicada 2019, 26, 50–64.
  24. Laurent, J.; Laurent, J. Pavemetrics LCMS-Laser Crack Measurement System. Available online: https://www.pavemetrics.com/applications/road-inspection/lcms2-en/ (accessed on 20 October 2022).
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