Assessing Pavement Crash Susceptibility for Consideration in Pavement Management Decisions

The quality of pavement condition data is important not only in assessing the current condition of the network but also in predicting its future condition and planning future maintenance and rehabilitation (M&R) activities. This paper provides a quantitative assessment of the impact of error magnitude and type (systematic and random) in pavement condition data on the accuracy of pavement management system (PMS) outputs (i.e., forecasted needed budget and M&R activities in a multiyear planning period). The process developed to simulate the propagation of pavement condition errors to PMS output consisted of five components: condition data generation, error perturbation, condition prediction, M&R prioritization, and output generation. This process was applied to the 2011 pavement condition data set of the Bryan District of the Texas Department of Transportation. In 2011, the roadway network of the Bryan District consisted of approximately 3,200 roadbed centerline miles. The study results show that both systematic and random errors can highly distort some PMS output parameters, even in error ranges that may be considered acceptable in practice. These effects tend to persist throughout the planning period. The findings of this study can help highway agencies optimize processes for collecting pavement condition data by focusing on error levels and types that cause the greatest impact on PMS output.

In Ukraine a significant part of motor roads with flexible pavement, especially local ones, serves with cracks in asphalt concrete layers during long period. Asphalt concrete spalling in the zone of open crack edges is a further stage of pavement layer deterioration during its long period of service. Costs for repair of these defects are much higher than for crack sealing. Monitoring of actual condition of flexible pavements in various regions of Ukraine indicates that cracks in asphalt concrete pavement appear in different locations, have different direction and pattern, width, depth and length. Number of cracks, their frequency of location on the pavement, length and width of opening at low temperatures depend on duration of the pavement exploitation. Today there are no exact regulations on structural and technological decisions in terms of crack preparation and sealing with regard to their width and activity in normative and technical documents. The topic of the paper is development of structural and technological decisions for crack preparation and sealing in asphalt concrete pavements with regard to their width and activity. It is noted that hot-applied bituminous sealing materials are the most popular in Ukraine for works on crack sealing in asphalt concrete road pavements. Based on the results of scientific and technical supervision of various crack sealing techniques in asphalt concrete road pavements it is shown that crack sealing can be performed with preliminary routing (rectangular rout) in zone of crack or without routing. It is noted that cracks with preliminary routing can be sealed with overband or without overband of sealing material on the surface of asphalt pavement. Technique of crack sealing by filling of the created rout with bitumen-polymer mastic or hot-applied sealant with overband on asphalt pavement surface provides the highest watertightness and durability of the sealed crack, in comparison with sealing by filling of the created rout flush with asphalt pavement, or without routing. Classification of cracks in asphalt concrete pavements is proposed and structural and technological decisions on crack preparation and sealing are described.

Microsurfacing is used as a surface seal to establish skid resistance on pavements, to fill ruts in asphalt concrete pavements and chain-wear depressions on portland cement concrete pavements, to reduce weathering and raveling on asphalt concrete pavements, and to surface bridge decks. Usage guidelines for applying microsurfacing on asphalt concrete pavements were developed while working on a project for the Texas Department of Transportation (TxDOT). These guidelines are based on information collected from published literature, personnel experienced in using microsurfacing, TxDOT personnel, industry personnel, and the project staff’s experience developed during the project. Special attention was directed at identifying those pavement types, conditions, and problems that normally could be addressed with microsurfacing. Provided is the following information: a short description of microsurfacing, its use as a surface treatment, its use as a rut filler, the types of pavement problems for which the treatment would be appropriate, the requirements of the pavement for treatment with microsurfacing, recommended analysis procedures to decide if microsurfacing is appropriate for a selected section of pavement, layer thicknesses, and the time of opening to traffic.

The Georgia Department of Transportation (GDOT) has adopted and been actively using Geographic Information Systems (GIS) technology to improve its 18,000-centerline-mile (28,962-km) system's highway pavement management for the past few years. An Oracle client/server and GIS-based pavement management module were developed by Georgia Tech and has been successfully implemented by GDOT since 2000. This client/server-based GIS module was designed to integrate seamlessly with the Oracle central database in which the pavement condition survey data reside. Once the pavement condition data surveyed by 60 field engineers had been uploaded on the central database, the managers were able to visualize statewide pavement conditions, including the project ratings and various distresses, such as load cracking, block cracking, rutting, etc., in real time. Managers were able and can now perform spatial analyses by aggregating features, such as working districts and congressional districts. The successful implementation of the GIS module in GDOT has greatly enhanced pavement management capability by generating data that had been previously unavailable and by obtaining pavement condition data in seconds instead of days. In this paper, examples are used to demonstrate the actual benefits of implementing the client/server-based GIS module. Also, future research about using GIS in pavement management is discussed.

Numerical Simulation of Tire-Pavement Interaction

Determination of seal coat deterioration using image processing methods

Effectiveness evaluation and influencing factor analysis of pavement seal coat treatments using random forests

Parametric Model for Accident Prediction Along Ado Ekiti – Ikole Ekiti Road, Ekiti State, Nigeria

Highway agencies collect pavement condition data over time to manage their network in a cost-effective manner. However, there exist differences among highway agencies for data collection monitoring frequency. While most agencies collect sensor-based data (roughness, rut depth, and faulting) on a yearly basis, image-based data (cracking and other pavement defects) are collected every other year or on a three-year basis. The main reason for the differences is the relative difficulties (cost, resources, and technology) in collecting and processing imaging data. While monitoring frequency has an impact on performance prediction at project-level, it can also affect pavement management system (PMS) decisions at the network-level. Such decisions include identification of project boundaries, optimum timing of pavement fixes, and selection of the most cost-effective pavement preservation strategy. In this paper, PMS data from the state of Washington and MnRoad for flexible and rigid pavements were analyzed to determine the impact of monitoring frequency on the PMS decisions. Results of the analyses show that monitoring frequency may affect the identification of project boundaries, optimum fix type, and timing. These effects have implications on the cost effectiveness of the pavement management strategy.

The use of Airport Pavement Management Systems (APMS) in the United States began in the early 1980s. By the mid 1990s, the use of APMS was prevalent throughout the country, and today, more than 80 percent of one segment of the industry—state aviation agencies—use APMS tools to manage their airports’ pavements. When applied properly, an APMS provides agencies with indispensable information to make cost-effective decisions about the preservation and rehabilitation of the pavement network. Further, an APMS allows agencies to evaluate the long-term impacts of decisions made about their pavement infrastructure. Although state aviation agencies have used APMS tools for over 20 years to store and evaluate pavement inventory, condition, and performance data, no one is reporting on these data at the national level. For example, very limited documentation can be found on overall pavement inventory, condition, and performance at airports in the United States. This is unfortunate because pavement management data compiled at the national level could yield very interesting and useful information about a variety of topics, including the status of overall airport pavement conditions in the United States and the typical performance of different surface types in different climatic regions. To address this lack of information at the national level, the paper collected airport pavement management data from 22 states, representing 1,360 airports, 20,000 pavement sections, and approximately 2 billon square feet of pavement. Most of the 22 states do not tract the airport classification, such as general aviation, reliever, commercial, and so on. Therefore, it is not possible to break out the results by airport type. However, it is likely that at least 75 percent of the airports are general aviation, with most of the rest being reliever airports. The data represents six states in the Western and Northern Atlantic regions, seven states in the Central region, and three states in the Southern region. All the data were stored in a Micro PAVER database, which facilitated the analysis of the data. The collected data were analyzed to identify national and regional trends. Specifically, conditions at the national and regional level, broken out by pavement use and surface type, were evaluated. Further, pavement performance models were developed to identify typical performance trends of different pavement types, pavement uses and in different climatic regions. This paper presents the results of this analysis.

Present article reviews the early and recent developments in skid resistance measurement techniques at asphalt concrete pavement. Suitability and limitations of various methods used for capturing skid characteristics of the surface has been outlined. The harmonization of laboratory and field-testing methods has been summarized briefly. Harmonization is agreement of test outcomes reported from field as well as laboratory. Correlation analysis of various in-situ and laboratory test data has been made to maintain the better harmony of measurement either in field or in laboratory. Measurement of skid resistance in field is time consuming, labor intensive and requires use of costly, advanced and sophisticated instruments. Lab measurement lags in accuracy and still needs improvement in design and working methodology which compelled researchers for looking into modelling as an alternative option. In the subsequent sections, progress in modelling approach (analytical to numerical) has been discussed in brief. Analytical and numerical solutions of asphalt concrete pavement for modelling skid resistance have been reviewed and emphasis for choosing a particular solution has been justified on the basis of accuracy as well as efficacy of the output model. Critical factors affecting skid resistance model like hydroplaning, tire related parameters, temperature, surface texture has been highlighted in this manuscript. Skid data obtained from any model whether it is analytical or numerical, needs to be validated against field measurement or some established data. Validation of skid resistance model using field test and experimentally derived equations in various research studies has been summarized in later section of this article. Few key research directions have been suggested as scope of future study so as to predict a more reliable skid resistance model (1) For predicting dynamic frictional properties with higher accuracy at tire pavement interface, tire pavement interaction modelling still needs improvement considering all field constraints (2) Influence of surface texture, hydroplaning, temperature and pavement ageing needs to be studied in depth for modelling skid resistance as these are critical parameters & contributes significantly.

Le xington, in central Kentucky, is the second largest city in the state. Local government is by a mayor and council of elected representatives from twelve districts and three at-large representatives. The Department of Public Works headed by a Commissioner, who reports to the mayor and city-county council, administers the streets and roads system. The street and road network in Lexington consists of approximately 720 centerline miles. Approximately 100 miles are rural in nature and 620 miles are urban. Approximately 95 percent are bituminous surfaced roads; a majority of the remainder is constructed of portland cement concrete. Increasing costs for constructing and maintaining the street and road network was a stimulus to implement a systematic pavement and road surface management system. Available alternatives were reviewed, and it was decided to maximize in-house resources of the Lexington-Fayette Urban County Government. Meetings with public works officials were used to establish criteria for implementation of a management program: (a) program for visual condition survey of pavements, (b) procedure(s) to determine structural deficiencies for critical pavements identified in the visual condition survey, and (c) utilization of the above information to estimate rehabilitation strategies and their associated costs. The paper describes and documents three phases of implementation of a pavement and road surface management system. Phase I included development of a system­ wide inventory of pavements and a pavement condition rating system for both flexible and rigid pavements. Phase II dealt with the modifications to a microcomputer program to process pavement condition ratings for ranking pavements on the basis of need and also to estimate pavement maintenance and rehabilitation costs. Phase III included administrative and training activities necessary for implementation of the pavement management program. Training generally addressed three areas: (1) collection of pavement condition and distress information, (2) operation of the microcomputer program for data processing and analyses, and (3) interpretation and utilization of results of the analyses, Finally, the paper describes current research and development relating to refinements and modifications for the Lexington situation.

Pavement performance is influenced by man factors such as climate and environmental conditions, traffic and operational conditions and type of pavement. These factors cause a pavement deterioration what leads to the restriction of the pavement serviceability or pavement efficiency. The pavement serviceability is the ability of the pavement to fulfil the service function represented by the actual values of variable parameters such as pavement surface roughness, surface evenness, pavement surface condition. The state of the pavement is assessed using different performance indicators when International Roughness Index (IRI) is most used. This approach allows to classify the state of the pavement in the pavement management system as a most used indexing, generalizes the pavement surface to the response of the testing car tire and the pavement. Laser scanning presented in this paper is able to bring the knowledge about the real pavement surface considering the accuracy of the method and equipment. Realized laser scanning proved the applicability of this method for the measurement of the pavement surface. Because of the complex knowledge of the pavement surface morphology, we can evaluate the pavement serviceability in terms of roughness, surface evenness or even pavement surface condition (rutting or cracks).

The quality of roads directly impacts the services they provide. Different indices are used to determine the pavement condition. Based on these indices, the pavement management system provides an estimation of the future costs of roadways and the ways of optimizing them. One of the indices used in the specification of the pavement condition is the Pavement Condition Index (PCI). To determine the value of this index, considering the amount and the severity of each sample's distress, the deduct values should first be calculated based on the experimental charts. In this study, the researchers try to present a model based on the Adaptive Neuro Fuzzy Inference System (ANFIS) to determine the deduct value used to calculate the PCI.

Skid resistance, or friction, on a road surface is a critical parameter in functional highway assessments, given its direct relationships with safety and accident frequency. Therefore, road administrations must collect friction data across their road networks to ensure safe roads for users. In addition, having a predictive model of skid resistance for each road section is essential for an efficient pavement management system (PMS). Traditionally, road authorities disregard rural roads, since they are more focused on freeways and traffic-intense roads. This study develops a model for predicting minimum-available skid resistance, which occurs in summer, measured using the Sideway-force Coefficient Routine Investigation Machine (SCRIM), on bituminous pavements in the single-carriageway road network of the Province of Gipuzkoa, Spain. To this end, traffic volume data available in the PMS of the Provincial Council of Gipuzkoa, such as the annual average daily traffic (AADT) and the AADT of heavy vehicles (AADT.HV), were uniquely used to forecast skid-resistance values collected in summer. Additionally, a methodology for eliminating outliers is proposed. Despite the simplicity of the model, which does not include information about the materials at the surface layer, a coefficient of determination (R2) of 0.439 was achieved. This model can help road authorities identify the roads for which lower skid-resistance values are most likely to occur, allowing them to focus their attention and efforts on these roads, which are key infrastructure in rural areas.