Pavement Management System Database Research Articles

Lifetime Performance of Polymer-Modified Bitumen in Swedish Roads Evaluated With Survival Analysis

This study uses survival analysis to estimate the lifetime of Swedish asphalt paved roads. The roads were separated by binder type, such as unmodified bitumen and polymer-modified bitumen (PMB). Road pavements with PMB were compared to equivalent roads with regard to annual average daily traffic, heavy vehicles, road type, road width, speed limit, construction year, region, bearing capacity, pavement type, stone size, and rut bottom distance. The analysis was based on almost 500,000 records of pavement maintenance and road condition measurements from the Swedish Transport Administration’s Pavement Management System database, dating back as far as the 1970s. The results showed that PMB in the surface layer was expected to extend road lifetimes by 15% compared with bitumen. PMB in the underlying layer gave an 8% increase in life expectancy compared with unmodified bitumen. PMB in both layers gave a 24% increase in expected road lifetime compared with unmodified bitumen in both layers.

Machine-Learning-Based Framework for Prediction of the Long-Term Field Performance of Asphalt Concrete Overlays in a Hot and Humid Climate

Pavement performance prediction models are used by state agencies to determine pavement maintenance and rehabilitation strategies. However, most performance prediction models are based on a limited number of parameters and a maximum prediction period of five years. With the ever-increasing amount of available pavement performance data, machine-learning techniques have become a promising alternative to traditional performance prediction models. The objective of this study was to develop a machine-learning-based framework for states with a hot and humid climate that can predict the long-term field performance (up to 11 years) of asphalt concrete (AC) overlays on asphalt pavements based on key project conditions. The pavement condition index (PCI) was used as the pavement performance indicator. Two machine-learning algorithms, namely, random forest (RF) and CatBoost, were examined. A total of 892 log-miles of AC overlay data were obtained from the Louisiana Department of Transportation and Development Pavement Management System database. Based on the collected data, six models were trained (for each algorithm) and validated to predict the PCI of AC overlays for up to 11 years. The results indicated that the RF algorithm yielded higher accuracy than the CatBoost algorithm. Therefore, the RF-based models were considered in the proposed decision-making framework.

Comparative Study of Different Condition Indices Using Michigan Department of Transportation’s Flexible Distress Data

The condition of a country’s roadway infrastructure significantly affects its economic development. Highway agencies that manage pavements often communicate their road network condition by using single or multiple composite condition indices. Each agency relies on its condition index to make maintenance decisions and perform budget allocation. Generally, these condition indices follow a similar level of the rating scale (100 to 0, where 100 denotes perfect condition). However, because of different numbers of distress inputs with different units, different severity levels, and different deduct points and mathematical formulas, condition indices are often incomparable. Consequently, research focused on comparing different distress indices using a highway agency’s pavement distress data is lacking. The objective of this study is to provide a comparative analysis of different condition indices using Michigan Department of Transportation’s (MDOT) flexible distress data, as part of an effort to develop a new index for Michigan. In this process, 2,081 flexible pavement sections were identified from MDOT’s Pavement Management System database, and five different condition indices were evaluated and compared against MDOT’s Distress Index. Comparison results were presented through scatter plots and statistical analysis results. MDOT’s maintenance records were also used to compare the ranges of magnitudes of various indices computed at the time when various types of maintenance activities took place. Finally, this study resulted in recommendations for selecting a new condition index and calibration needs of deduct points and equations.

A Feasibility Study for the Prediction of Concrete Pavement Condition Index (CPCI) Based on Machine Learning

In South Korea, various attempts have been made to utilize the Pavement Management System database (PMS DB) more efficiently as a basis for preventive maintenance. Data for the PMS DB is extensively collected every year. This study aims to predict future pavement conditions by introducing the concept of machine learning instead of regression modeling. We selected 469 sections that satisfied the analysis conditions and used them for analysis. We used particle filtering, a machine learning technique, to predict future pavement conditions. We found that the function of the particle filtering technique itself increases the prediction accuracy for the target section analyzed as the number of particles increases. Furthermore, the number of time series points in one section had a higher impact on the improvement of prediction accuracy than the number of sections analyzed. Finally, the relative error by each predicted age for the same section decreased as the number of time series points increased. These findings indicate that the rate of decrease in the performance index can be quantitatively presented in the future, and the method developed in this study could be used by pavement managers during the decision-making process.

Performance Evaluation of Studded Tire Ruts for Asphalt Mix Designs in a Cold Region Environment

Studded tire wear is one of the most important contributing factors that govern pavement life on high traffic volume roads in south-central Alaska. It has been estimated that the annual cost to repair damage caused by studded tires in Alaska is approximately $13.7 million. Polymer modified asphalt binders and hard aggregate are commonly used in mix designs in the State of Alaska Department of Transportation and Public Facilities projects in an attempt to resist studded tire wear. This research provides a field performance-based analysis of the use of hard aggregate and polymer modified binders related to the reduction of the impact of studded tire wear. To analyze the performance of modification in asphalt mix designs, roadway data was gathered from the Alaska Department of Transportation Pavement Management System Database and roads were grouped into deterioration families based on mix design properties. Relationships of each deterioration are presented to show cumulative average annual daily traffic versus total rut depth, comparing mix designs with and without hard aggregate and polymer modification. Findings indicated that both polymer modification and hard aggregate can be used to reduce the impact of studded tire wear. The benefits of polymer modification and hard aggregate proved more significant as cumulative traffic volumes increase. Recommendations from the performance analysis included continuing the use of hard aggregate and polymer modified binders on Alaskan roadways with continuous research on the use of more highly modified asphalt binders to further reduce the impact of studded tire wear.

Drivability life of pavement: a new numeric in pavement management system

ABSTRACT In recent years, the Colorado Department of Transportation (CDOT) developed Drivability Life (DL) index to estimate useable pavement service life for roadway segments. The DL process is similar in concept to the Remaining Service Life (RSL). The DL of pavement is a measure, in years, of how long a roadway segment will have acceptable driving conditions. A preliminary experimental study conducted by CDOT concluded that the DL can be efficiently used in Pavement Management System (PMS). For detail analysis, CDOT incorporated DL in their 2013 and 2014 PMS database. This paper presents the results from the experimental study and performed a preliminary analysis on the low volume roads in the State of Colorado. Analysis confirmed that the RSL can be replaced by DL, and may help representing more accurate pavement condition of a roadway segment, thus help maintaining the roadway networks efficiently.

Effects of the International Roughness Index and Rut Depth on Crash Rates

Pavement distresses directly affect ride quality, and indirectly contribute to driver distraction, vehicle operation, and accidents. In this study, analysis was performed on highways in the states of Arizona, North Carolina, and Maryland to investigate the relationship between accident rate and pavement ride quality (roughness) and rut depth. Two main types of data were collected: crash data from the accident records and International Roughness Index (IRI) and rut depth data from the pavement management system database in each state. Crash rates were calculated using the U.S. Department of Transportation method, which is the number of accidents per 100 million vehicle-miles of travel. Sigmoidal function regression analysis was performed to study the relationship between crash rate and both IRI and rut depth. In all cases, the crash rate did not show substantial increases until an IRI value of 210 inches/mile or a critical rut depth of 0.4 inches. When the IRI or rut depth increased above these values the crash rate increased. This is a key conclusion that provides empirically derived thresholds for IRI and rut depth to reducing the accident rate.

Estimation of Pavement Serviceability Index Through Android-Based Smartphone Application for Local Roads

The Wyoming Technology Transfer Center is in the process of developing a pavement management system (PMS) for county paved roads in Wyoming. This PMS uses the present serviceability index (PSI) as a main pavement performance parameter. This PMS depends on pavement condition index, international roughness index, and pavement rutting as explanatory variables to estimate PSI. This study researched new explanatory variables measured by using smartphones’ sensors to estimate PSI. It was found that the variance of the signals (time series acceleration data) acquired by smartphones’ accelerometers could work as a very good explanatory variable to estimate PSI. Two models were developed with high significance ( R2 higher than .9) to predict PSI using the variance of smartphone signals. The initial validation results suggested that using these models could predict, with high certainty, the actual PSI values. The difference between the predicted and the actual PSI values was not statistically different. The study was performed on 20 roadway segments extracted from the Wyoming county roads’ PMS database. In addition, the selected segments had various lengths and geometric features reflecting various roadway segments under any PMS. The proposed methodology is intended to lower the cost of measuring county roads’ pavement conditions by estimating PSI directly without the reliance on the direct measurement of pavement condition parameters.

Mechanistic-empirical methodology for the selection of cost-effective rehabilitation strategy for flexible pavements

A well-planned rehabilitation approach helps agencies to optimise the allocation of annual investment in pavement rehabilitation programs. Currently, many agencies are struggling with the selection of an optimal time-based and cost-effective rehabilitation solution to address the long-term needs of pavements. This study offers the use of a mechanistic-empirical methodology to develop a series of time-based rehabilitation strategies for high traffic volume flexible pavements located in Oklahoma. Six different pavement family groups are identified in the state, and comprehensive evaluation of existing pavements are conducted through analysis of falling weight deflectometer data and performance measures available in Oklahoma Pavement Management System database. The inadequacy of performance measures to fully characterise the condition of existing pavements are indicated, and damage factor determined from FWD data are suggested as trigger factor to select rehabilitation candidates. Three levels of rehabilitation activities including light, medium and heavy are considered as potential alternatives for rehabilitation candidates. A mechanistic-empirical methodology is employed to obtain an estimate of the performance of rehabilitation and extension in service lives of pavements. Also, an assessment output matrix is developed, which can be served as a supplemental tool to help the decision-makers in the highway agency with the rehabilitation related decision-making process. Cost-effectiveness of rehabilitation alternatives is determined through life cycle cost analysis, and three time-based renewal solutions are developed for pavement family groups that are in need of rehabilitation.

Research Implementation of AASHTOWare Pavement ME Design in Louisiana

Since the release of the first research version of Mechanistic–Empirical Pavement Design Guide software, the State of Louisiana has conducted a series of studies in preparation for adopting the new AASHTO design guide. The objective of this study was to develop an implementation guideline for the full implementation of the AASHTOWare Pavement ME Design program (Pavement ME) in Louisiana. For this purpose, a Louisiana default input strategy that reflected state and local conditions and practices was first developed. Design reliability and performance criteria were established on the basis of a consensus survey and pavement management system (PMS) distress triggers. A total of 162 projects were selected from the existing Louisiana highway network, including flexible pavements with five types of base, rigid pavements with three types of base, and rehabilitation with asphalt overlays. Performance data were retrieved from the PMS database and validated through additional field performance surveys. Through comparison of the performance predicted by the Pavement ME with the measured performance recorded in the PMS database, distress models were evaluated and calibrated according to local Louisiana conditions. Subsequently, a Louisiana Pavement ME Design Guideline that contains all necessary design input information and calibration coefficients was developed. Finally, the practical and economic benefits of the full implementation of the Pavement ME for Louisiana pavement design were demonstrated by a direct comparison with the current 1993 AASHTO design method through a group of thickness design examples and life-cycle cost analyses.

Pavement diagnosis accuracy with controlled application of artificial neural network

Results of research studies, the amount of input data available in pavement management system databases, and artificial intelligence methods serve as versatile tools, well-suited for the analysis conducted as a part of pavement management system. The key source of new and to be employed knowledge is provided. In terms of e.g. assessing thickness of bituminous pavement layers, the default solution is pavement drilling, but for the purposes of pavement management it is prohibitively expensive. This paper attempts to test the original concept of employing an empirical relationship in an algorithm verifying results produced by the artificial neural network method. The assumed multistage asphalt pavement layer thickness identification control process boils down to evaluating test results of the road section built using both, reinforced and non-reinforced pavement structure. By default, the artificial neural network training set has not included the reinforced pavement sections. Hence, it has been possible to identify “perturbations” in assumptions underlying the training set. Pavement test section points’ results are indicated in the automated manner, which, in line with implemented methods, is not generated by perturbations caused by divergence between actual pavement structure and assumptions taken for purposes of building pavement management system database, and the artificial neural network learning dataset is based on.

Utilizing Asphaltic Concrete Overlay to Mitigate the Detrimental Effects of Alkali Carbonate Reactions in Portland cement Concrete Pavement

In 1990, the Louisiana Department of Transportation and Development (LADOTD) constructed approximately 9.96 km of portland cement concrete (PCC) pavement on Interstate 20. In 1994, the PCC began to show signs of distress in the form of surface cracks, joint spalling, pop outs, and efflorescence. Alkali carbonate reactions (ACR) in the PCC were discovered upon analyzing cores taken from the PCC in 1996. In 1997 and 1998, LADOTD placed a 102-mm AC overlay on the PCC pavement on the east- and west-bound lanes, respectively. Sawing and sealing the AC over the PCC joints was performed to mitigate reflective cracking. The AC overlay added structure to the pavement section reducing stress on the PCC, created a barrier preventing surface moisture from entering into the PCC, and created a thermally insulated pavement reducing the temperature within the PCC. Pavement surface distress data was obtained by data mining the LADOTD Pavement Management system data base and structural testing the composite pavement was conducted with the Falling Weight Deflectometer. Cores were obtained from the PCC and tested in accordance with a modified ASTM C1567-13 in October 2012. Approximately 15 years after the placement of the AC overlay, only minimal transverse cracking, longitudinal cracking, and patching were discovered and the roadway had a smooth ride. Results from the structural testing indicated that the PCC pavement was distressed with the overall structural number of the pavement being approximately 5.5. When tested in accordance with a modified ASTM C1567-13, significant volumetric strains up to 0.8 percent were measured on the PCC cores. Volumetric strains of that magnitude will cause detrimental effects on the PCC, eventually leading to its destruction.

Using Decision Tree for Data Mining of Pavement Maintenance and Management

In recent years advancements in the Information Technology (IT), have enabled automation of pavement measurement data. A large amount of data can be saved for a pavement management system. The study of pavement maintenance and management has include many methods, such as expert system, decision support analysis and data mining (DM) . In this study we use decision tree for data mining algorithm C5.0 has been used in this analysis. After acceptance of the decision tree, we make use of algorithms and computing for classification. This method is used to check the pavement management system database and make a comparison of all data. The result shown a correct classification of about 61% its still improved space. According to this result we discuss three analysis results included: 1.Database information is correct or not 2.Road pavement never homogenization 3.Milling process never remove human factor. Finally useful pavement information and ways can improve system integrity and correctly.

Performance of Superpave Projects in Louisiana

The Louisiana Department of Transportation and Development (Louisiana DOTD) initiated the implementation of the Superpave® system by the end of the past decade. This study evaluated 8 to 10 years of field performance of 21 Louisiana Superpave projects, which included five Interstates, four U.S. routes, and 12 Louisiana routes. The evaluation was limited to the field performance of projects relative to age and did not consider factors associated with materials or construction. The criteria used to evaluate performance were confined to rutting, the international roughness index (IRI), alligator cracking, and random cracking. Superpave distress data were collected and stored in the pavement management system database of the Louisiana DOTD every 2 years. The collected data sets were analyzed and compared with Louisiana performance prediction models. These models were developed in the late 1990s by an expert panel from the Louisiana DOTD to predict the deterioration of pavement near the end of its design life. The original performance prediction models were reevaluated in 2002, and roughness models were implemented in 2003. The analysis indicated that after 8 to 10 years of service, most of the projects on Interstate and state highways were in good condition relative to the distress factors evaluated. Superpave seemed to control the early rutting and cracking in hot-mix construction while maintaining good IRI over the years. Points of concern, however, were the few issues of rutting on U.S. highways and cracking on Louisiana routes. In general, the rate of deterioration for rutting and IRI was lower than the prediction from the performance models.

Necessary Assessment of Use of State Pavement Management System Data in Mechanistic–Empirical Pavement Design Guide Calibration Process

Calibration and validation of the Mechanistic–Empirical Pavement Design Guide (MEPDG) for state department of transportation (DOT) networks requires detailed information for a variety of pavement inputs. The biggest database resource that most states possess is their pavement management system (PMS). It is understandable for any DOT to maximize the use of this resource to the greatest extent possible. However, before any state uses PMS data in MEPDG calibration, it is important that a careful comparison be made of the equality of the measurements, data, and so forth in the PMS database with national Long-Term Pavement Performance (LTPP) values. The purpose of this paper is to review possible differences between several key data found in a state PMS database and the LTPP database. Arizona Department of Transportation (ADOT) PMS data are used for this purpose. The specific variables examined in this paper deal with distress type (rutting, cracking, and roughness) and nondestructive deflection testing (NDT) backcalculated moduli to predict in situ pavement layer properties. It was concluded that significant differences did exist between the ADOT PMS values and LTPP measurements. Differences were found between ADOT's NDT measurements and LTPP data in rut measurements, asphalt cracking, international roughness index, and all layer backcalculated moduli. Consequently, ADOT may incur significant expense to calibrate the MEPDG to Arizona conditions. The paper discusses several possible reasons for differences between the PMS data of a specific agency and those in the LTPP database.

Use of Pavement Management System Data to Monitor Performance of Pavements under Warranty

Pavement warranty is an innovative contracting procedure increasingly adopted by highway agencies. Many states view implementing warranties as a way to protect their investment in pavement construction. The major benefit of pavement warranty is enhanced pavement performance. However, establishing warranty criteria and monitoring the performance of warranted pavement are two technical issues that must be resolved. This paper presents the discoveries of a research project supported by FHWA to investigate the effectiveness of using the pavement management system (PMS) to monitor the performance of pavement under warranty. It has been discovered that most states establish their warranty specifications on the basis of performance data from their PMS database by using statistical analyses, expert opinions, and adaptation of other states’ specifications. They also use the PMS data to monitor the performance of both warranty and nonwarranty pavements because the essential data, like ride, cracking, and rutting, are available in the database and are convenient for use. This paper also documents the unique features of the warranty specifications, including the warranty period, performance indicators, data collection methods, performance thresholds, remedial actions, and how the PMS database can be used to track the performance of pavements under warranty in five states: Indiana, Wisconsin, Ohio, Florida, and Michigan. Some comparisons of the performances of the warranted pavements and those of nonwarranted pavements of similar conditions are also presented. The survey indicated that an integrated PMS that links the materials and traffic database would offer better efficiency to monitor and analyze the performance of both warranty and nonwarranty pavements.

Use of Pavement Management System Data to Monitor Performance of Pavements Under Warranty

Pavement warranty is an innovative contracting procedure increasingly adopted by highway agencies. Many states view implementing warranties as a way to protect their investment in pavement construction. The major benefit of pavement warranty is enhanced pavement performance. However, establishing warranty criteria and monitoring the performance of warranted pavement are two technical issues that must be resolved. This paper presents the discoveries of a research project supported by FHWA to investigate the effectiveness of using the pavement management system (PMS) to monitor the performance of pavement under warranty. It has been discovered that most states establish their warranty specifications on the basis of performance data from their PMS database by using statistical analyses, expert opinions, and adaptation of other states' specifications. They also use the PMS data to monitor the performance of both warranty and nonwarranty pavements because the essential data, like ride, cracking, and rutting, a...

Rough Set Application to Data Mining Principles in Pavement Management Database

This paper presents a new approach to rough set theory in pavement rehabilitation and maintenance decision support systems. A rough set-based analysis acts like a knowledge engineer who sits between data and the user. The rough set concept is an effective tool for analysis of information systems in a pavement management system (PMS) database gained by both objective and subjective methods. The rough set approach in a PMS database enables pavement engineers to discover minimal subsets of condition attributes in assessing and describing the pavement conditions, and to derive decision rules in rehabilitation and maintenance decision making. The approach appears to capture information on uncertainty, imprecision, and ambiguity along with precise values in a PMS database.

Prediction of Pavement Remaining Life

The evaluation of remaining life is necessary to make optimal use of the structural capacity of in-service pavements. It simply represents the useful life left in the pavement until a failure condition is reached. Knowledge of remaining life facilitates decision making in regard to strategies for reconstruction-rehabilitation of roads, thereby leading to the efficient use of existing resources. Several methods proposed or used by various agencies to estimate the remaining lives of pavements are reviewed. They are classified under two categories: functional and structural. Making use of the Mississippi Department of Transportation pavement management system data base, survivor curves are developed for seven classes of flexible pavements with from thin to thick structures. By using these survivor curves a novel method for estimating remaining life is proposed. The reasonableness of the selected methods is examined by putting them to use in calculating the remaining lives of each of eight rigid and flexible ...

Prediction of Pavement Remaining Life

The evaluation of remaining life is necessary to make optimal use of the structural capacity of in-service pavements. It simply represents the useful life left in the pavement until a failure condition is reached. Knowledge of remaining life facilitates decision making in regard to strategies for reconstruction-rehabilitation of roads, thereby leading to the efficient use of existing resources. Several methods proposed or used by various agencies to estimate the remaining lives of pavements are reviewed. They are classified under two categories: functional and structural. Making use of the Mississippi Department of Transportation pavement management system data base, survivor curves are developed for seven classes of flexible pavements with from thin to thick structures. By using these survivor curves a novel method for estimating remaining life is proposed. The reasonableness of the selected methods is examined by putting them to use in calculating the remaining lives of each of eight rigid and flexible pavement sections, all of them from the Mississippi global positioning system sections of the Strategic Highway Research Program–Long-Term Pavement Performance project (LTPP). With the structural details, falling weight deflectometer deflection data, and the distress information compiled from the LTTP information management system data base, the authors use two and four methods for rigid and flexible pavements, respectively, to determine the remaining lives. The remaining lives calculated by two methods for rigid pavements are comparable. Three of four methods for flexible pavements also generated comparable remaining lives. The authors were encouraged by the results and recommend that the survivor curve approach be explored further for network-level remaining life calculations. The reliabilities of various techniques currently available for the remaining life calculation are discussed.