Pavement management

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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.

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It is also applied to airport runways and ocean freight terminals. In effect, every highway superintendent does pavement management. [1]

Pavement management incorporates life cycle costs into a more systematic approach to minor and major road maintenance and reconstruction projects. The needs of the entire network as well as budget projections are considered before projects are executed, [2] as the cost of data collection can change significantly. [3] [4] Pavement management encompasses the many aspects and tasks needed to maintain a quality pavement inventory, and ensure that the overall condition of the road network can be sustained at desired levels. [5] While pavement management covers the entire lifecycle of pavement from planning to maintenance in any transport infrastructure, road asset management and road maintenance planning target more specifically road infrastructure.

In the United States, the introduction of the Governmental Accounting Standards Board’s (GASB’s) Statement 34 [6] is having a dramatic impact on the financial reporting requirements of state and local governments. Introduced in June 1999, this provision recommends that governmental agencies report the value of their infrastructure assets in their financial statements. GASB recommends that government agencies use a historical cost approach for capitalizing long-lived capital assets; however, if historical information is not available, guidance is provided for an alternate approach based on the current replacement cost of the assets. A method of representing the costs associated with the use of the assets must also be selected, and two methods are allowed by GASB. One approach is to depreciate the assets over time. The modified approach, on the other hand, provides an agency more flexibility in reporting the value of its assets based upon the use of a systematic, defensible approach that accounts for the preservation of the asset. [7] Pavement management and pavement management systems provide agencies with the tools necessary to evaluate their pavement assets and meet the GASB34 requirements under the modified depreciation approach.

Pavement management systems

A pavement management system (PMS) is a planning tool used to aid pavement management decisions. PMS software programs model future pavement deterioration due to traffic and weather, and recommend maintenance and repairs to the road's pavement based on the type and age of the pavement and various measures of existing pavement quality. Measurements can be made by persons on the ground, visually from a moving vehicle, or using automated sensors mounted to a vehicle. PMS software often helps the user create composite pavement quality rankings based on pavement quality measures on roads or road sections. Recommendations are usually biased towards predictive maintenance, rather than allowing a road to deteriorate until it needs more extensive reconstruction.

Typical tasks performed by pavement management systems include:

  1. Inventory pavement conditions, identifying good, fair and poor pavements.
  2. Assign importance ratings for road segments, based on traffic volumes, road functional class, and community demand.
  3. Schedule maintenance of good roads to keep them in good condition. [8]
  4. Schedule repairs of poor and fair pavements as remaining available funding allows. [9]

Research has shown that it is far less expensive to keep a road in good condition than it is to repair it once it has deteriorated. This is why pavement management systems place the priority on preventive maintenance of roads in good condition, rather than reconstructing roads in poor condition. In terms of lifetime cost and long term pavement conditions, this will result in better system performance. Agencies that concentrate on restoring their bad roads often find that by the time they've repaired them all, the roads that were in good condition have deteriorated. [10]

The State of California was among the first to adopt a (PMS) in 1979. Like others of its era, the first PMS was based in a mainframe computer and contained provisions for an extensive database. [11] It can be used to determine long-term maintenance funding requirements and to examine the consequences on network condition if insufficient funding is available.

Management approach

The pavement management process has been incorporated into several pavement management systems including SirWay. [12] The following management approach evolved over the last 30 years as part of the development of the PAVER management system (U.S. Army COE, Construction Engineering Research Laboratory, Micro PAVER 2004).

The approach is a process that consists of the following steps: [13]

  1. Inventory Definition
  2. Pavement Inspection
  3. Condition Assessment
  4. Condition Prediction
  5. Condition Analysis
  6. Work Planning

Inventory Definition

Typically, pavement management requires road inventory to be created and tied to an Asset Location Referencing System (ALRS). Road inventory includes road location using both coordinate and linear referencing systems, road width, road length and pavement type.

Condition Assessment

Pavement condition can be divided into structural and functional condition with various condition variables. Functional condition can be divided into roughness, texture and skid resistance while structural condition includes mechanical properties and pavement distresses. [14] To measure such indices, costly laser-based tools are used extensively while development of cost effective tools such as RGB-D sensors significantly reduces the cost of data collection. [4]

Condition Prediction

Pavement condition prediction is often referred to pavement deterioration modeling, which can be based on mechanical or empirical models. Also, hybrid parameterized models are popular. More recently other methods based on Markov models and machine learning have been proposed that outperform their former counterparts. [15] [16] [3] Pavement deterioration is caused by traffic and weather conditions. Also, material and construction choices affect the deterioration process. It has been shown that empirical models outperform the mechanical and hybrid models in condition prediction. [17]

Work Planning

Work planning is essentially road maintenance planning in which the maintenance works are assigned both spatially and temporally according to the desired criteria such as minimal costs to the society.

Related Research Articles

<span class="mw-page-title-main">Road</span> Land route for travel by vehicles

A road is a linear way for the conveyance of traffic that mostly has an improved surface for use by vehicles and pedestrians. Unlike streets, the main function of roads is transportation.

<span class="mw-page-title-main">Highway engineering</span> Civil engineering of roads, bridges, and tunnels

Highway engineering is a professional engineering discipline branching from the civil engineering subdiscipline of transportation engineering that involves the planning, design, construction, operation, and maintenance of roads, highways, streets, bridges, and tunnels to ensure safe and effective transportation of people and goods. Highway engineering became prominent towards the latter half of the 20th century after World War II. Standards of highway engineering are continuously being improved. Highway engineers must take into account future traffic flows, design of highway intersections/interchanges, geometric alignment and design, highway pavement materials and design, structural design of pavement thickness, and pavement maintenance.

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

A road surface (English), or pavement (American), 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.

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">Predictive maintenance</span> Method to predict when equipment should be maintained

Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted. Thus, it is regarded as condition-based maintenance carried out as suggested by estimations of the degradation state of an item.

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">Bridge management system</span>

A bridge management system (BMS) is a set of methodologies and procedures for managing information about bridges. Such system is capable of document and process data along the entire life cycle of the structure steps: project design, construction, monitoring, maintenance and end of operation.

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

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An asset management plan (AMP) is a tactical plan for managing an organisation's infrastructure and other assets to deliver an agreed standard of service. Typically, an asset management plan will cover more than a single asset, taking a system approach - especially where a number of assets are co-dependent and are required to work together to deliver an agreed standard of service.

<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.

The facility condition index (FCI) is used in facilities management to provide a benchmark to compare the relative condition of a group of facilities. The FCI is primarily used to support asset management initiatives of federal, state, and local government facilities organizations. This would also include universities, housing and transportation authorities, and primary and secondary school systems.

The PASER scale is a 1-10 rating system for road pavement condition developed by the University of Wisconsin-Madison Transportation Information Center. PASER uses visual inspection to evaluate pavement surface conditions. When assessed correctly, PASER ratings provide a basis for comparing the quality of roadway segments. The PASER assessment method does not require measurements of individual distresses, and thus PASER ratings cannot be disaggregated into measurements of specific distress types. The advantage to this method is that roads may be assessed quickly, possibly even by "windshield survey." A primary disadvantage is that because PASER ratings cannot be disaggregated into component distress data, the metric cannot be used in mechanistic-empirical transportation asset management programs.

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">Pavement performance modeling</span>

Pavement performance modeling or pavement deterioration modeling is the study of pavement deterioration throughout its life-cycle. 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), but sometimes a single distress such as rutting or the extent of crack is used. Among the most frequently used methods for pavement performance modeling are mechanistic models, mechanistic-empirical models, survival curves and Markov models. Recently, machine learning algorithms have been used for this purpose as well. Most studies on pavement performance modeling are based on IRI.

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

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. ↑ Pavement Management - A Manual for Communities, U. S. Department of Transportation, Metropolitan Area Planning Council, Boston MA., 1986
  2. ↑ Pavement Management for Airport, Roads, and Parking Lots, 2nd Edition, M.Y. Shahin, Springer Science+Business Media, LLC, 2002
  3. 1 2 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. 1 2 Mahmoudzadeh, A.; Firoozi Yeganeh, S.; Golroo, A. (2015-12-11). "Kinect, A Novel Cutting Edge Tool in Pavement Data Collection". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XL-1–W5: 425–431. Bibcode:2015ISPArXL15..425M. doi: 10.5194/isprsarchives-xl-1-w5-425-2015 . ISSN   2194-9034.
  5. ↑ Hillsborough County Pavement Management Strategy, Hillsborough County Fl, Ch.1 - Introduction, pg1., R. Cox, P.E. 2006
  6. ↑ GASB Welcome Page
  7. ↑ Proceedings of the 2003 Mid-Continent Transportation Research Symposium, Ames, Iowa, August 2003. © 2003 by Iowa State University.
  8. ↑ 'Saha, P., & Ksaibati, K. (2015). 'A Risk-based Optimization Methodology for Managing County Paved Roads', In Transportation Research Board 94th Annual Meeting (No. 15-1916), http://docs.trb.org/prp/15-1916.pdf
  9. ↑ Pavement Management System Summer Intern Program, Nuggets and Nibbles Volume XXX Number 3, Cornell Local Roads Program, Summer 2011, page 4, http://www.clrp.cornell.edu/nuggets_and_nibbles/index.htm
  10. ↑ "Pavement Management Primer" (PDF). Federal Highway Administration, U.S Department of Transportation. Retrieved May 11, 2012.
  11. ↑ U.S. Department of Transportation Federal Highway Administration, California Division, November 13, 2003)
  12. ↑ https://www.sirway.info/assets/pdf/Sirway-RMS.pdf [ bare URL PDF ]
  13. ↑ Pavement Management for Airport, roads, and Parking Lots, 2nd Edition, M.Y. Shahin, Springer Science+Business Media, LLC, 2002
  14. ↑ Bennett, C. R., de Solminihac, H. and Chamorro, A. Data Collection Technologies for Road Management, Transport Note No. 30, Roads and Rural Transport Thematic Group, The World Bank, Washington D.C., 2007.
  15. ↑ 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]". United States. Federal Highway Administration. Office of Research, Development, and Technology. FHWA-HRT-18-065. Archived from the original on 2019-02-02 – via National Transportation Library Repository & Open Science Access Portal.
  16. ↑ 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.
  17. ↑ Sirvio, Konsta (2017) Advances in predictive maintenance planning of roads by empirical models. Aalto University publication series DOCTORAL DISSERTATIONS, 166/2017. (https://www.researchgate.net/publication/319998419_Advances_in_predictive_maintenance_planning_of_roads_by_empirical_models)
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