Pavement Management System Data Research Articles

Considering the Spatial Structure of the Road Network in Pavement Deterioration Modeling

Pavement deterioration modeling is important in providing information with respect to the future state of the road network and in determining the needs of preventive maintenance or rehabilitation treatments. This research incorporated the spatial dependence of the road network into pavement deterioration modeling through a graph neural network (GNN). The key motivation of using a GNN for pavement performance modeling is the ability to easily and directly exploit the rich structural information in the network. This paper explored if considering the spatial structure of the road network will improve the prediction performance of the deterioration models. The data used in this research comprises a large pavement condition dataset with more than a half million observations taken from the Pavement Management Information System maintained by the Texas Department of Transportation. The promising comparison results indicate that pavement deterioration prediction models perform better when the spatial relationship is considered.

Development of Pavement Performance and Remaining Service Life Prediction Tools for Iowa Jointed Plain Concrete Pavement Systems

Moving Ahead for Progress in the Twenty-First Century (MAP-21) requires US state highway agencies (SHA) to utilize performance-based approaches in their pavement management decision-making processes, and use of a remaining service life (RSL) model would be one of such performance-based approaches for facilitating the pavement management decision-making process for SHAs. In this study, statistical and artificial neural network (ANN)-based pavement performance and RSL models were developed for Iowa jointed plain concrete pavement systems (JPCP) using actual pavement structural, traffic, construction history, and pavement performance records obtained from the Iowa Department of Transportation pavement-management information system database. While both models were found to be potentially useful for project and network level performance and RSL predictions, statistical and ANN-based models were respectively found to be more suitable for project and network level analysis. Using these models, efficient Microsoft Excel-based automation tools were created to predict future performance of a JPCP section and estimate RSL values based on predicted future performance and threshold limits for the performance indicators. Consequence analysis was also conducted to investigate the impact of traffic and preservation treatment (diamond grinding) on the RSL of a JPCP. The tool, also capable of estimating realistic pavement pretreatment and posttreatment performance and RSL, could be successfully used as part of performance-based pavement management strategies and helping decision-makers make better-informed pavement management decisions to properly allocate agency resource expenditures. Moreover, this study provides a better understanding of RSL and the factors that influence both the project and network level RSL.

Field Performance Evaluation of High Polymer-Modified Asphalt Concrete Overlays

The objective of this paper was to assess the viability of using high polymer (HP) modified asphalt concrete (AC) mixtures in Virginia as a reflective crack mitigation technique or when deemed appropriate as a tool for increased crack resistance on higher volume facilities. This was achieved by compiling and evaluating routine distress survey data against pre-paving distress survey data for relevant in-service HP pavements constructed between 2015 and 2018 and comparing them with several control in-service conventional polymer-modified asphalt (PMA) pavements. This is the first effort in North America to provide a detailed field performance of HP AC mixtures. In general, none of the evaluated mixtures (HP or PMA) was able to prevent reflective cracking completely. The HP sections showed the most promising performance 5 years after construction regardless of traffic level and the pre-existing pavement conditions. The pavement management system data for the reviewed sections indicated a potential controlling effect of the joint condition of the underlying jointed concrete pavement layer regardless of the asphalt mixture type employed (PMA or HP). Moreover, performance evaluations using the network-level pavement management data were conducted to estimate the life expectancy of HP AC overlays. Two different approaches and three levels of analysis were undertaken. Overall, PMA and HP AC overlays had an average predicted service life of 6.2 and 8.3 years, respectively, indicating a 34% extension of performance life of the AC overlays with high polymer modification.

Local Calibration of Pavement Mechanistic-Empirical Faulting Reliability using Pavement Management Data

The Pavement ME transverse joint faulting model incorporates mechanistic theories that predict development of joint faulting in jointed plain concrete pavements (JPCP). The model is calibrated using the Long-Term Pavement Performance database. However, the Mechanistic-Empirical Pavement Design Guide (MEPDG) encourages transportation agencies, such as state departments of transportation, to perform local calibrations of the faulting model included in Pavement ME. Model calibration is a complicated and effort-intensive process that requires high-quality pavement design and performance data. Pavement management data—which is collected regularly and in large amounts—may present higher variability than is desired for faulting performance model calibration. The MEPDG performance prediction models predict pavement distresses with 50% reliability. JPCP are usually designed for high levels of faulting reliability to reduce likelihood of excessive faulting. For design, improving the faulting reliability model is as important as improving the faulting prediction model. This paper proposes a calibration of the Pavement ME reliability model using pavement management system (PMS) data. It illustrates the proposed approach using PMS data from Pennsylvania Department of Transportation. Results show an increase in accuracy for faulting predictions using the new reliability model with various design characteristics. Moreover, the new reliability model allows design of JPCP considering higher levels of traffic because of the less conservative predictions.

A weighted multi-output neural network model for the prediction of rigid pavement deterioration

ABSTRACT A novel weighted multi-output neural network (NN) model is proposed for predicting the deterioration of rigid pavements based on Iowa pavement management system data. This first-of-a-kind model simultaneously predicts four pavement condition metrics concerning rigid pavements, including IRI, faulting, longitudinal crack and transverse crack. It provides an opportunity to efficiently evaluate pavement conditions and to make treatment decisions based on multi-condition metrics, such as the pavement condition index (PCI) for budget allocation models. Compared to traditional single-output NN models, this multi-output model is capable of incorporating correlations among different condition metrics. During model training, each condition metric is assigned a weight to reflect its relative importance. When the weights equal to those in the formula for the multi-condition metric, the prediction performance for PCI is optimal (13% lower MSE than optimal, single-output models). The multi-output model improves on the prediction performance for three of the four individual condition metrics compared to optimal single-output models. Results show that the consideration of correlations could improve the prediction performance for single and multi-condition metrics. Finally, variable weighting is critical for achieving the optimal balance of prediction performance among the various metrics as dictated by the needs of the decisionmaker.

Analytical methods to determine effectiveness of slurry seals in wet/freeze climate using pavement management information systems

With increasing economic pressures worldwide, the amount of money spent on pavement preservation needs to become more effectively utilised. Historically, the Long-Term Pavement Performance (LTPP) study has led to data-driven performance expectations for treatments. Pavement management systems (PMS) allowed many to form analytical methods to evaluate pavement performance, from trend fitting to benefit analysis. Taking these methods and applying them to the Iowa Department of Transportation’s (DOT) pavement management information system (PMIS) database can determine preservation utility as well as provide expectations of pavement treatments. Thirteen slurry seal projects across the wet/freeze climate of Iowa, U.S.A., were analysed to determine the service life extensions and yearly benefit for their pavement condition, rutting, riding, and cracking indices. This study aims to provide a framework for future analysis of more preservation treatments and other PMS databases. Understanding local performance of various preservation methods leads to better pavement management and economically sound decisions.

Prediction of Indirect Tensile Strength of Intermediate Layer of Asphalt Pavements Using Artificial Neural Network Model

The repair method for pavements should be selected considering the structural capacity of sublayers, in addition to the conditions observed at the pavement surface, to reduce the recurrence of distress in the repaired area. However, it is practically impossible to include the structural capacity of sublayers in the database of the pavement management system (PMS) because this would require additional tests in all expressway sections. Therefore, an artificial neural network model for predicting the indirect tensile strength (ITS) of the intermediate layer of all asphalt pavement sections in an expressway was developed in this study, taking the international roughness index, rut depth, surface distress, and equivalent single axle load as independent variables. The ITS of specimens cored from target sections was measured in the laboratory, and the PMS data for the target sections were collected. The ITS was predicted by conducting a feedforward process prior to the training step. When the error between the predicted and measured ITSs exceeded the allowable error, the model was repetitively trained using the resilient backpropagation method until the error fell within the acceptable boundary. The model was validated by analyzing the correlations between the ITSs predicted from the data of the training and test sets. Finally, the model was complemented by the corresponding minimum and maximum values of the ITS measured at the target section.

Use of Deep Learning to Study Modeling Deterioration of Pavements a Case Study in Iowa

This paper describes the process and outcome of deterioration modeling for three different pavement types (asphalt, concrete, and composite) in the state of Iowa. Pavement condition data is collected by the Iowa Department of Transportation (DOT) and stored in a Pavement-Management Information System (PMIS). In the state of Iowa, the overall pavement condition is quantified using the Pavement Condition Index (PCI), which is a weighted average of indices representing different types of distress, roughness, and deflection. Deterioration models of PCI as a function of time were developed for the different pavement types using two modeling approaches. The first approach is the long/short-term memory (LSTM), a subset of a recurrent neural network. The second approach, used by the Iowa DOT, is developing individual regression models for each section of the different pavement types. A comparison is made between the two approaches to assess the accuracy of each model. The results show that the LSTM model achieved a higher prediction accuracy over time for all different pavement types.

Prediction of the road pavement condition index using stochastic models

Mathematical models for prediction of road network condition based on the so-called Markov chains are presented in this article. The data for calculation of elements of the transition matrix from one condition to another are taken from visual evaluation as well as from instrumental reading. It is recommended to prepare data sets in the form of pavement management system data tables based on a representative sample of measuring sections. Discrete time intervals – of one year – are used when constructing the model of transition matrices. The procedure of forming Markov transition matrix with partially complete data sets is proposed also in paper. The basis of this procedure is information on the previous condition of the structure and the results of the instrumental evaluation, which enables correction of the predicted values. The final matrix takes into account not only the probability, but also the speed of transition from one condition to another. It is also possible to work with the initial data using appropriate databases or other software.

Life Extension of Aged Jointed Plain Concrete Pavement through Remodeling Index-Based Analysis.

As jointed plain concrete pavements (JPCP) age in South Korea, the cost of pavement maintenance is increasing annually. To extend the life of jointed concrete pavements through preventive maintenance, this study used 2017 pavement management system data to analyze the effects of traffic volume, alkali–silica reaction (ASR) grade, age, smoothness, and damaged area on the remodeling index (RMI—a measure of expressway pavement condition). In addition, this study evaluates the final RMI as well as the corresponding pavement condition and change in RMI value after conducting preventive maintenance in lieu of resurfacing or overlaying. The results demonstrated that the effect of ASR grade increased as the RMI forecast year increased and that change in surface distress (△SD) increased with age (most intensively when the pavement was 15–20 years of age). Moreover, change in international roughness index (△IRI) increased with age and traffic volume (similarly within 15–20 years of pavement age). Hence, preventive maintenance is a must for sections with high traffic volume and age even if the RMI is low. Finally, performing repairs through preventive maintenance decreases the number of expressway sections requiring resurfacing and overlaying, thus extending the life of the concrete pavement.

Punchout evaluations and classification of continuously reinforced concrete pavement to improve pavement design

In continuously reinforced concrete pavement (CRCP) research, it has been assumed that transverse crack spacing plays an important role in determining CRCP behaviour and performance. For the longitudinal reinforcement design, the 1993 American Association of State Highway and Transportation Officials (AASHTO) Design Guide recommends a minimum crack spacing of 3.5 ft. The minimum spacing of 3.5 ft is recommended to minimise the occurrence of punchouts. This concept of narrow crack spacing being more prone to punchout has been accepted. Hence, all the available CRCP design algorithms are based on this concept. However, since treated base/non-erodible base is now provided in CRCP, this theory has some discrepancies with the real CRCP performance especially in Texas. In this study, an extensive field evaluation was conducted for the number of punchouts recorded in TxDOT pavement management information system (PMIS). Out of 1472 punchouts, 232 punchouts that developed in major metropolitan Districts in TxDOT were surveyed and evaluated to determine whether punchouts have a relationship with closely spaced transverse cracks and the resulting load transfer efficiency (LTE). Field survey and testing results show that the majority of distresses in CRCP have been caused by quality issues of either materials or construction in the vicinity of transverse construction joint and repair joint.

Impacts of Seasonal and Annual Weather Variations on Network-Level Pavement Performance

This study was aimed to determine the extent of association between network-level pavement condition and seasonal and annual weather variations. Pavement condition data recorded in the Texas Department of Transportation’s Pavement Management Information System (PMIS) database between 2000 and 2008 was highlighted. Meteorological data collected at different districts across the whole state were used in the explanatory variables. Dynamic panel data analysis was used in the deterioration models to quantify the effects of temperature and precipitation variations on pavement conditions. Based on the statistical implication from the model estimation results, significant correlations were identified between pavement conditions, the average monthly rainfall, and the average monthly temperature recorded 1 to 23 months prior to pavement condition inspection.

Rebuilding the Distress Thresholds for Pavement Warranty Program in Mississippi

Pavement warranty is an innovative contracting procedure increasingly adopted by state transportation agencies to protect investment in pavement construction and maintenance. In Mississippi, the pavement warranty program was initiated in 2000 and the pavement distress thresholds were set based on the deduct points calculated by conversion equations from distress measurements of unacceptable pavement conditions. The conversion of distress measurements into deduct points using empirically regressed conversion equations has been questioned, however, on the grounds that it actually reduces the accuracy of the objective distress measurements by adding subjective and random errors. Moreover, the validity and applicability of continual use of these conversion equations has become problematic with time, because the equations were empirically developed in the 1990s, reflecting the data, experience, and technologies at that time. This research aims to develop analytically new distress thresholds based on direct measurements of pavement distresses for the pavement warranty program in Mississippi. The bootstrapping method was employed to process the skewed historical distress measurement data into normal distributions. The upper bound of confidence interval or standard deviations of the normalized distress measurement data was determined as an alternative method to rebuild the threshold for each distress type. The confusion matrix was employed to consider a smooth transition of updating the thresholds from the current deduct point based system to a new measurement based system. This study provides a practical procedure for developing a set of measurement based thresholds to renew the pavement warranty program using the available pavement management system data.

Development of representative section selection method for large-scale concrete pavement remodeling project based on trial and error correction approach

In this study, a method to identify the representative section (RS) which can represent the pavement conditions of entire aged concrete expressway pavement (CEP) sections for the large-scale remodeling project, was developed based on pavement management system (PMS) data and an optimized approach. To fulfill this objective, various PMS data sets on the target section (TS) of concrete expressway pavement that exceeded 20 years of design life were collected with 100m intervals. Then, the first version of the optimized 100 RSs, which was a reasonable amount of data for analysis of aged CEP condition, was selected based on the different severity levels of surface damage and international roughness index (IRI) data. Secondly, another crucial PMS factor: the alkali-silica reaction (ASR) severity level, was considered and substituted partially in the previously selected 100 RSs to provide additional and reasonable representative characteristics. Finally, the characteristics of the RS were evaluated and verified by considering whether the various distress size rankings of the RS were uniformly distributed within the entire distress size ranking of the total jointed concrete pavement (JCP) and continuously reinforced concrete pavement (CRCP) TS. It was found that the RS selection approach and corresponding criteria proposed in this study, based on the optimization approach, could be successfully used to determine the future large-scale remodeling priority of the old concrete expressway pavements.

Selecting Network-Level Project Sections for Sustainable Pavement Management in Texas

In recent years, the increasing gap between available funding and preservation needs has influenced district pavement engineers to select and prioritize projects to effectively use funding. However, currently, projects are often selected after an informal assessment, based on local conditions and local district engineers’ experience, in the absence of a statewide systematic process. The primary objective of this study is to determine network-level project sections for effective sustainable pavement management using logistic regression analysis. A large volume of inventory data, documented using pavement-management information systems (PMIS), was used to develop the logistic regression (LR) model for selecting candidate sections. The LR model was subsequently validated using a single 50/50 split sample method. The findings of this study will assist the Austin, Texas, USA district to select and evaluate candidate projects. Furthermore, the study will eventually contribute to improved efficiency in project selection and prioritization by reducing not only the amount of time necessary to review the district PMIS data to identify project candidates, but also the potential for human error.

Developing a methodology to evaluate the effectiveness of pavement treatments applied to low-volume paved roads

ABSTRACTA significant number of agencies allocate low funding levels for their low-volume roads (LVRs) as part of their pavement management system (PMS) funding strategies. Although agencies have been applying a number of low-cost treatments, the effectiveness of the applied treatments towards improving pavement performance is not well understood or documented. The application of low-cost treatments on roads has different levels of impact on the pavement performance depending on the initial pavement condition. This study introduces a methodology of evaluating in-practice surface treatments on LVRs. The methodology can be implemented by different transportation agencies using historical data of pavement condition and treatment records. A case study of managing LVRs in Colorado was investigated using the historical PMS data of Colorado Department of Transportation (CDOT). The performance of pavement in the context of fatigue, longitudinal, and transverse cracking was analysed and the results show different trends of common treatment options applied to LVRs. It was concluded that some surface maintenance strategies are effective long-term treatments for fatigue, longitudinal, and transverse cracking. However, the effectiveness of these treatments depends mainly on the initial condition index. The findings of this study permits increased understanding of selected maintenance treatments. An appropriate maintenance strategy was developed including all effective treatments. The comprehensive long-term analysis of road maintenance and costs shows that the proposed maintenance strategy is more cost-effective compared to the current policy followed by CDOT.

Preparation of Joint Faulting Data for the Local Calibration of AASHTO Pavement Mechanistic–Empirical Design in Louisiana

Louisiana utilized performance data from the pavement management system (PMS) to evaluate and calibrate the AASHTO Pavement Mechanistic–Empirical (ME) Design. Analysis of the PMS faulting data revealed that there were no records between 0 and 0.2 in. (5 mm); others over 0.2 in. (5 mm) appeared to be much greater than would be expected based on engineering experience. Therefore, several tasks were completed to validate the PMS faulting data and prepare them for local calibration. This paper presents details of the problem, approach, results, and lessons learned. First, faulting data from the PMS and Long-Term Pavement Performance database were analyzed to have an overview of the common range of joint faulting. To validate the PMS faulting data, 43 representative projects across Louisiana were selected for further analysis. Longitudinal profiles were collected with high-speed profilers and analyzed with the AASHTO R36 automated faulting measurement (AFM) algorithms. Manual measurements were also conducted during site visits. The comparison of faulting from different methods showed that the PMS data extremely overestimated faulting compared with the AFM estimation or the manual measurement. Results from the AFM algorithm were much closer (in the same magnitude) to the manual measurements. Therefore, faulting data from the AFM algorithm were used, and the faulting model was successfully calibrated. It is recommended to evaluate PMS faulting data carefully before applying them to calibrate the AASHTO Pavement ME Design software. Automated faulting measurement based on high-speed profiles is a feasible approach.

Hybrid Modeling Approach for Quantifying Maintenance and Rehabilitation Treatment Effectiveness of Asphalt Pavements

Analysis tools in pavement management systems are critical to assist transportation agencies in developing the most adequate maintenance and rehabilitation program. A key aspect in the treatment selection process is to evaluate the effectiveness of maintenance and rehabilitation alternative treatments. Treatment effectiveness is related to the time of the application and should be measured in the short term and long term. Short-term treatment effectiveness measures include condition improvement and reduction of the deterioration rate, while long-term treatment effectiveness is related to its ability to extend the pavement remaining life. In this paper, a hybrid modeling approach is presented to integrate expert knowledge with pavement performance data for quantifying short-term and long-term treatment effectiveness. Optimal times to apply asphalt treatments were obtained as a result of the hybrid modeling approach. The Texas Department of Transportation (DOT) has successfully incorporated these results to enhance its pavement management information system, which is used as a tool to assist Texas DOT decision makers to formulate maintenance and rehabilitation programs.

Use of Pavement Management System Data to Enhance Pavement Performance Specifications in Canada

Most agency materials and construction specifications for pavement rehabilitation and construction activities provide little or no linkage between quality assurance methods and the in-service performance of the treatment. The Ministry of Transportation of Ontario (MTO), Canada, is developing and implementing performance specifications to bridge this gap. MTO defines performance specifications as specifications that describe how the finished product should perform over time. This paper describes how MTO used historical pavement management system data to assess performance sensitivity and to create historical performance distributions and how the agency used these distributions to set acceptance criteria, including payment adjustment for performance.

Determining Future Maintenance Costs of Low-Volume Highway Rehabilitation Projects for Incorporation into Life-Cycle Cost Analysis

AbstractLife-cycle cost analysis (LCCA) has grown in importance, yet it is also regarded as a daunting task, because of the lack of reliable analytical models specifically aimed at quantifying future maintenance costs (FMC). The key objectives of this study are to identify the most critical factors that affect pavement performance, create a FMC predictive model that accounts for such factors, and validate the model’s robustness in quantifying a reliable FMC for low-volume highway rehabilitation projects. This study combines regression analysis techniques with a cluster analysis by using a large quantity of real-world data obtained from the pavement management information system. The clustering analysis revealed that traffic loading would be the most crucial affecting factor for pavement performance. A series of sensitivity analyses were performed to investigate the effect of individual critical performance-driven factors on FMC. A prediction error analysis validated the model’s robustness, proving that re...