Pavement performance modeling
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Pavement performance modeling or pavement deterioration modeling is the study of pavement deterioration throughout its life-cycle.[1][2] The health of pavement is assessed using different performance indicators. Some of the most well-known performance indicators are Pavement Condition Index (PCI), International Roughness Index (IRI) and Present Serviceability Index (PSI).[3][4] Among the most frequently used methods for pavement performance modeling are mechanistic, mechanistic-empirical models,[5][6] survival curves and Markov models. Recently, machine learning algorithms have been used for this purpose as well.[7][8]
History
The study of pavement performance goes back to the first half of 20th century. The first efforts in pavement performance modeling were based on mechanistic models. Later researchers also developed empirical models, which were not based on the structure of the pavement. Since the beginning of 1990s mechanistic-empirical models became popular. These models combined both mechanistic and empirical features via linear regression. In North America, AASHTO developed a guideline based on mechanistic-empirical methods.[5]
Development of such models required data. Therefore, in North America, organizations such as AASHTO and FHWA collected large amounts of data about pavement conditions. Examples of these databases, which are used for pavement design and performance measurement, are the LTPP and AASHO Road Test.[9]
Pavement deterioration
The deterioration of roads is a complex phenomenon and is influenced by many factors. These factors can be classified into a few categories: design and construction, material type, environmental conditions, managerial and operational factors.[1] Among the most environmental factors are freeze-thaw cycles, maximum and minimum temperature and precipitation. The traffic count and the type of traffic are among the important attributes as well.[8]
References
- ^ a b Ford, K., Arman, M., Labi, S., Sinha, K.C., Thompson, P.D., Shirole, A.M., and Li, Z. 2012. NCHRP Report 713 : Estimating life expectancies of highway assets. In Transportation Research Board, National Academy of Sciences, Washington, DC. Transportation Research Board, Washington DC.
- ^ "Piryonesi, S. M. (2019). The Application of Data Analytics to Asset Management: Deterioration and Climate Change Adaptation in Ontario Roads (Doctoral dissertation)".
{{cite web}}: CS1 maint: url-status (link) - ^ Piryonesi, S. M.; El-Diraby, T. E. (2020) [Published online: December 21, 2019]. "Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index". Journal of Infrastructure Systems. 26 (1). doi:10.1061/(ASCE)IS.1943-555X.0000512.
{{cite journal}}: CS1 maint: url-status (link) - ^ Way, N.C., Beach, P., and Materials, P. 2015. ASTM D 6433–07: Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys.
- ^ a b AASHTO. 2008. Mechanistic-empirical pavement design guide: A manual of practice.
- ^ Belay, Abraham; OBrien, Eugene; Kroese, Dirk (April 2008). "Truck fleet model for design and assessment of flexible pavements". Journal of Sound and Vibration. 311 (3–5): 1161–1174. doi:10.1016/j.jsv.2007.10.019. hdl:10197/2336.
- ^ Piryonesi, S. M.; El-Diraby, T. E. (2020) [Published online: December 21, 2019]. "Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index". Journal of Infrastructure Systems. 26 (1). doi:10.1061/(ASCE)IS.1943-555X.0000512.
{{cite journal}}: CS1 maint: url-status (link) - ^ a b "Piryonesi, S. M., & El-Diraby, T. (2018). Using Data Analytics for Cost-Effective Prediction of Road Conditions: Case of The Pavement Condition Index:[summary report] (No. FHWA-HRT-18-065). United States. Federal Highway Administration. Office of Research, Development, and Technology".
- ^ "FHWA: A Look at the History of the Federal Highway Administration".