Pavement performance modeling

Last updated
Pavement performance models could be developed to predict a single distress such as a crack or the aggregate pavement condition index. Pavement cracks (15736896886).jpg
Pavement performance models could be developed to predict a single distress such as a crack or the aggregate pavement condition index.
Schematic deterioration of the condition of a road over time Schematic deterioration of an asset over time.png
Schematic deterioration of the condition of a road over time
The increase in the IRI of a road in Texas. The blue dots on the curve represent maintenance actions. IRI progression.png
The increase in the IRI of a road in Texas. The blue dots on the curve represent maintenance actions.

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] but sometimes a single distress such as rutting or the extent of crack is used. [2] [5] Among the most frequently used methods for pavement performance modeling are mechanistic models, mechanistic-empirical models, [6] survival curves and Markov models. Recently, machine learning algorithms have been used for this purpose as well. [3] [7] Most studies on pavement performance modeling are based on IRI. [8]

Contents

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 (M-E) models have become popular. These models combined both mechanistic and empirical features via linear regression. In North America, AASHTO developed a guideline based on mechanistic-empirical methods. [6]

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]

Causes of 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, and managerial and operational factors. [1]

Climate and environmental conditions

Among the most significant environmental factors are freeze-thaw cycles, maximum and minimum temperature and precipitation. [2] It is reported that on average roads in a wet climate with freeze cycles deteriorate up to two times more than roads in dry and no-freeze regions. [8] So, roads exposed to larger number of freeze-thaw cycles and higher precipitation levels deteriorate faster. On the other hand, roads in dry and no freeze climates last longer. [1] [3] A very high temperature can be detrimental to asphalt pavement too and cause distresses such as bleeding. Considering this, climate change could pose a threat to the well-being of roads. Its impact, however, varies based on regions. While it can be highly detrimental to roads in a certain area it might alleviate the deterioration of roads in another area. [2]

Traffic and operational conditions

Resurfacing of a granular race track following an auto race. John Deere 624G Wheel Loader.jpg
Resurfacing of a granular race track following an auto race.

The traffic count and the type of traffic are among the important operational attributes. [7] Usually larger volumes of traffic and heavier vehicles such as trucks are correlated with faster pavement degradation. Also managerial approaches can have an important influence on deterioration patterns. Examples of the factors directly related to management are the type and frequency of maintenance [3] or cleaning and deicing approaches in the winter. [2] [10] Using too much of deicing salt can exacerbate the corrosion problem especially in concrete pavement. [10]

Type of pavement

The type of pavement is one of the most important factors affecting pavement deterioration. [3] Generally concrete pavements are more durable in warmer climates, and asphalt pavements are more resilient against cold weather. The joints in concrete pavement is another source of issue. In a certain type of road (concrete, asphalt or gravel), the thickness of layers and type of materials used in base, sub-base and pavement layer matters. Sometimes these attributes are expressed via an aggregated measure called granular base equivalence (GBE). [2] [3]

Related Research Articles

The Shell pavement design method was used in many countries for the design of new pavements made of asphalt. First published in 1963, it was the first mechanistic design method, providing a procedure that was no longer based on codification of historic experience but instead that permitted computation of strain levels at key positions in the pavement. By analyzing different proposed constructions, the procedure allowed a designer to keep the tensile strain at the bottom of the asphalt at a level less than a critical value and to keep the vertical strain at the top of the subgrade less than another critical value. With these two strains kept, respectively, within the design limits, premature fatigue failure in the asphalt and rutting of the pavement would be precluded. Relationships linking strain values to fatigue and rutting permitted a user to design a pavement able to carry almost any desired number of transits of standard wheel loads.

<span class="mw-page-title-main">Road surface</span> Road covered with durable surface material

A road surface or pavement is the durable surface material laid down on an area intended to sustain vehicular or foot traffic, such as a road or walkway. In the past, gravel road surfaces, macadam, hoggin, cobblestone and granite setts were extensively used, but these have mostly been replaced by asphalt or concrete laid on a compacted base course. Asphalt mixtures have been used in pavement construction since the beginning of the 20th century and are of two types: metalled (hard-surfaced) and unmetalled roads. Metalled roadways are made to sustain vehicular load and so are usually made on frequently used roads. Unmetalled roads, also known as gravel roads, are rough and can sustain less weight. Road surfaces are frequently marked to guide traffic.

<span class="mw-page-title-main">Asphalt concrete</span> Composite material used for paving

Asphalt concrete is a composite material commonly used to surface roads, parking lots, airports, and the core of embankment dams. Asphalt mixtures have been used in pavement construction since the beginning of the twentieth century. It consists of mineral aggregate bound together with bitumen, laid in layers, and compacted.

<span class="mw-page-title-main">Permeable paving</span> Roads built with water-pervious materials

Permeable paving surfaces are made of either a porous material that enables stormwater to flow through it or nonporous blocks spaced so that water can flow between the gaps. Permeable paving can also include a variety of surfacing techniques for roads, parking lots, and pedestrian walkways. Permeable pavement surfaces may be composed of; pervious concrete, porous asphalt, paving stones, or interlocking pavers. Unlike traditional impervious paving materials such as concrete and asphalt, permeable paving systems allow stormwater to percolate and infiltrate through the pavement and into the aggregate layers and/or soil below. In addition to reducing surface runoff, permeable paving systems can trap suspended solids, thereby filtering pollutants from stormwater.

In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one; in unsupervised learning it is usually called a matching matrix.

<span class="mw-page-title-main">Rumble strip</span> Road safety feature

Rumble strips are a road safety feature to alert inattentive drivers of potential danger, by causing a tactile vibration and audible rumbling transmitted through the wheels into the vehicle interior. A rumble strip is applied along the direction of travel following an edgeline or centerline, to alert drivers when they drift from their lane. Rumble strips may also be installed in a series across the direction of travel, to warn drivers of a stop or slowdown ahead, or of an approaching danger spot.

<span class="mw-page-title-main">AASHO Road Test</span> AASHO experiment for studying highway pavements deterioration

The AASHO Road Test was a series of experiments carried out by the American Association of State Highway and Transportation Officials (AASHTO), to determine how traffic contributed to the deterioration of highway pavements.

The pavement condition index (PCI) is a numerical index between 0 and 100, which is used to indicate the general condition of a pavement section. The PCI is widely used in transportation civil engineering and asset management, and many municipalities use it to measure the performance of their road infrastructure and their levels of service. It is a statistical measure and requires manual survey of the pavement. This index was originally developed by the United States Army Corps of Engineers as an airfield pavement rating system, but later modified for roadway pavements and standardized by the ASTM. The surveying processes and calculation methods have been documented and standardized by ASTM for both roads and airport pavements:

<span class="mw-page-title-main">Types of road</span>

A road is a thoroughfare, route, or way on land between two places that has been surfaced or otherwise improved to allow travel by foot or some form of conveyance, including a motor vehicle, cart, bicycle, or horse. Roads have been adapted to a large range of structures and types in order to achieve a common goal of transportation under a large and wide range of conditions. The specific purpose, mode of transport, material and location of a road determine the characteristics it must have in order to maximize its usefulness. Following is one classification scheme.

Pavement management is the process of planning the maintenance and repair of a network of roadways or other paved facilities in order to optimize pavement conditions over the entire network.

<span class="mw-page-title-main">Crocodile cracking</span> Distress in asphalt pavement

Crocodile cracking is a common type of distress in asphalt pavement. The following is more closely related to fatigue cracking which is characterized by interconnecting or interlaced cracking in the asphalt layer resembling the hide of a crocodile. Cell sizes can vary in size up to 300 millimetres (12 in) across, but are typically less than 150 millimetres (5.9 in) across. Fatigue cracking is generally a loading failure, but numerous factors can contribute to it. It is often a sign of sub-base failure, poor drainage, or repeated over-loadings. It is important to prevent fatigue cracking, and repair as soon as possible, as advanced cases can be very costly to repair and can lead to formation of potholes or premature pavement failure.

<span class="mw-page-title-main">International roughness index</span> Roughness index

The international roughness index (IRI) is the roughness index most commonly obtained from measured longitudinal road profiles. It is calculated using a quarter-car vehicle math model, whose response is accumulated to yield a roughness index with units of slope. Although a universal term, IRI is calculated per wheelpath, but can be expanded to a Mean Roughness Index (MRI) when both wheelpath profiles are collected. This performance measure has less stochasticity and subjectivity in comparison to other pavement performance indicators, such as PCI, but it is not completely devoid of randomness. The sources of variability in IRI data include the difference among the readings of different runs of the test vehicle and the difference between the readings of the right and left wheel paths. Despite these facts, since its introduction in 1986, the IRI has become the road roughness index most commonly used worldwide for evaluating and managing road systems.

<span class="mw-page-title-main">Infrastructure asset management</span> Maintenance of public infrastructure assets

Infrastructure asset management is the integrated, multidisciplinary set of strategies in sustaining public infrastructure assets such as water treatment facilities, sewer lines, roads, utility grids, bridges, and railways. Generally, the process focuses on the later stages of a facility's life cycle, specifically maintenance, rehabilitation, and replacement. Asset management specifically uses software tools to organize and implement these strategies with the fundamental goal to preserve and extend the service life of long-term infrastructure assets which are vital underlying components in maintaining the quality of life in society and efficiency in the economy. In the 21st century, climate change adaptation has become an important part of infrastructure asset management competence.

Bleeding or flushing is shiny, black surface film of asphalt on the road surface caused by upward movement of asphalt in the pavement surface. Common causes of bleeding are too much asphalt in asphalt concrete, hot weather, low space air void content and quality of asphalt. Bleeding is a safety concern since it results in a very smooth surface, without the texture required to prevent hydroplaning. Road performance measures such as IRI cannot capture the existence of bleeding as it does not increase the surface roughness. But other performance measures such as PCI do include bleeding.

<span class="mw-page-title-main">Long-Term Pavement Performance</span> Research project on road pavements

Long-Term Pavement Performance Program, known as LTPP, is a research project supported by Federal Highway Administration (FHWA) to collect and analyze pavement data in the United States and Canada. Currently, the LTPP acquires the largest road performance database.

<span class="mw-page-title-main">Deterioration modeling</span> Engineering formula

Deterioration modeling is the process of modeling and predicting the physical conditions of equipment, structures, infrastructure or any other physical assets. The condition of infrastructure is represented either using a deterministic index or the probability of failure. Examples of such performance measures are pavement condition index for roads or bridge condition index for bridges. For probabilistic measures, which are the focus of reliability theory, probability of failure or reliability index are used. Deterioration models are instrumental to infrastructure asset management and are the basis for maintenance and rehabilitation decision-making. The condition of all physical infrastructure degrade over time. A deterioration model can help decision-makers to understand how fast the condition drops or violates a certain threshold.

<span class="mw-page-title-main">Levels of service</span> Quality control assessment for numerous types of assets

Levels of service (LOS) is a term in asset management referring to the quality of a given service. Defining and measuring levels of service is a key activity in developing infrastructure asset management plans. Levels of service may be tied to physical performance of assets or be defined via customer expectation and satisfaction. The latter is more service-centric rather than asset-centric. For instance, when measuring the LOS of a road, it could be measured by a physical performance indicator such as Pavement Condition Index (PCI) or by a measure related to customer satisfaction such as the number of complaints per month about that certain road section. Or in the case of traffic level of service, it could be measured by the geometry of road or by travel time of the vehicles, which reflects the quality of traffic flow. So, levels of service can have multiple facets: customer satisfaction, environmental requirements and legal requirements.

<span class="mw-page-title-main">Pavement cracking</span>

Pavement crack refers to a variety of types of pavement distresses that occur on the surface of pavements. Different types of pavements develop different cracks. Type of cracking is also correlated with the type of climate and traffic. Sometimes the cracks are aggregated using an index such as Crack index, and sometimes they are merged with other distresses and are reported using Pavement Condition Index.

<span class="mw-page-title-main">Granular base equivalency</span> Measure of road pavement thickness

Granular base equivalency or granular base equivalence (GBE) is a measure of total pavement thickness. Since pavement is composed of multiple layers with different physical properties, its total thickness is measured by GBE. GBE translates the thickness of different road layers to a number using a set of coefficients. So, to calculate the GBE, the depth of each layer should be multiplied by the granular equivalency factor for the material in that layer. In the next step the sum of the converted layer thicknesses is calculated. This sum is called granular base equivalency, which is a popular and important measure in pavement design and pavement performance modeling.

The present serviceability index (PSI) is a pavement performance measure. Introduced by the American Association of State Highway and Transportation Officials (AASHTO), the PSI is one of the most widely used pavement performance indicators after pavement condition index (PCI) and international roughness index (IRI). This performance indicator ranges between 0 and 5, 0 representing a failed pavement and 5 an excellent one. Since the PSI entails slope variance, it is correlated with performance indicators related to roughness such as IRI.

References

  1. 1 2 3 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.
  2. 1 2 3 4 5 6 Piryonesi, Sayed Madeh (November 2019). Piryonesi, S. M. (2019). The Application of Data Analytics to Asset Management: Deterioration and Climate Change Adaptation in Ontario Roads (Doctoral dissertation) (Thesis).
  3. 1 2 3 4 5 6 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. S2CID   213782055.
  4. Way, N.C., Beach, P., and Materials, P. 2015. ASTM D 6433–07: Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys.
  5. Ens, A. (2012). Development of a flexible framework for deterioration modelling in infrastructure asset management.
  6. 1 2 AASHTO. 2008. Mechanistic-empirical pavement design guide: A manual of practice.
  7. 1 2 "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". Archived from the original on 2019-02-02.
  8. 1 2 Piryonesi S. Madeh; El-Diraby Tamer E. (2020-06-01). "Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems". Journal of Transportation Engineering, Part B: Pavements. 146 (2): 04020022. doi:10.1061/JPEODX.0000175. S2CID   216485629.
  9. "FHWA: A Look at the History of the Federal Highway Administration".
  10. 1 2 Hassan, Y., Abd El Halim, A.O., Razaqpur, A.G., Bekheet, W., and Farha, M.H. 2002. Effects of Runway Deicers on Pavement Materials and Mixes: Comparison with Road Salt. Journal of Transportation Engineering, 128(4): 385–391. doi:10.1061/(ASCE)0733-947X(2002)128:4(385). doi:10.1061/(ASCE)0733-947X(2002)128:4(385).