Pavement management systems and performance prediction modeling tools are essential for maintaining an efficient and cost effective roadway network. One indicator of pavement performance is the International Roughness Index (IRI), which is a measure of ride quality and also impacts road safety. Many transportation agencies use IRI to allocate annual maintenance and rehabilitation strategies to their road network. The objective of the work in this study was to develop a methodology to evaluate and predict pavement roughness over the pavement service life. Unlike previous studies, a unique aspect of this work was the use of non-linear mathematical function, sigmoidal growth function, to model the IRI data and provide agencies with the information needed for decision making in asset management and funding allocation. The analysis included data from two major databases (case studies): Long Term Pavement Performance (LTPP) and the Minnesota Department of Transportation MnROAD research program. Each case study analyzed periodic IRI measurements, which were used to develop the sigmoidal models.The analysis aimed to demonstrate several concepts; that the LTPP and MnROAD roughness data could be represented using the sigmoidal growth function, that periodic IRI measurements collected for road sections with similar characteristics could be processed to develop an IRI curve representing the pavement deterioration for this group, and that pavement deterioration using historical IRI data can provide insight on traffic loading, material, and climate effects. The results of the two case studies concluded that in general, pavement sections without drainage systems, narrower lanes, higher traffic, or measured in the outermost lane were observed to have more rapid deterioration trends than their counterparts. Overall, this study demonstrated that the sigmoidal growth function is a viable option for roughness deterioration modeling. This research not only to demonstrated how historical roughness can be modeled, but also how the same framework could be applied to other measures of pavement performance which deteriorate in a similar manner, including distress severity, present serviceability rating, and friction loss. These sigmoidal models are regarded to provide better understanding of particular pavement network deterioration, which in turn can provide value in asset management and resource allocation planning.
Deterioration models are employed to forecast pavement performance and to support decisions of funds allocation for maintenance and rehabilitation. However, they traditionally lack a measure of reliability. This book uses multilevel Bayesian regression modeling for mixing prior knowledge with experimental observations in order to develop deterioration modeling with the ability to quantify uncertainty. It explicitly considers materials properties, structural capacity (or strength), external loading and environmental exposure by adapting classical mechanistic models. Two network level case studies illustrate the applicability of the method and deal with some of the practical limitations: (1) a novel method develops performance modeling from two time series data, using the concept of apparent age, (2) another model uses pavement roughness and strength to address practical limitations such as missing data, incorporating expert criteria and handling predictors from different data structures. The methods presented can help local, regional or national authorities to develop initial, practical or more advanced models for pavement deterioration, capable of capturing uncertainty.
The significant contribution of the research presented in this report lies in the fact that it utilizes the most comprehensive database of pavement conditions that is readily available and promises to provide the sought data in future years. The first part of this report reviews the existing literature covering related topics including pavement roughness, the LLTP background, artificial neural networks, regression analysis and the existing pavement deterioration models. The second part discusses the work done in data analysis and data manipulation in addition to the development of the training of the neural network model. The third part deals with various aspects of the model development using neural networks and regression analysis. The next part concludes the research with summarizing the results of model development. The models developed in this research are then compared to some existing models by applying the models to similar data sets and performing statistical analysis of the results.
Accurate prediction of pavement deterioration is vital for an efficient and cost-effective allocation of available budgets for keeping an agency's road networks operating at a desirable level. Currently, most cities in the Dallas-Fort Worth Metroplex area are using the software PAVERTM and the associated performance models to predict future conditions as they do not have available reliable prediction models. However, the problem with this type of modeling is that the models are not calibrated to local conditions.The Pavement Deterioration Prediction models that have been developed in this research will help any pavement management agencies within DFW Metroplex area to identify and predict the future pavement performance for any planning period. The models were developed based on the available data collected by the city's pavement management department for the DFW Metroplex area. In this research, a family modeling approach has been used as this method reduces the number of independent variables in performance modeling to a single variable (age in this research) by enabling the development of models in each pavement family. Separate models are also developed for areas with expansive and non-expansive subgrade soil. A total of eleven models are developed for the areas non-expansive subgrade soil area and nine models for the areas with expansive subgrade soil. Deterministic models that are developed are applicable to cities with available historical data on PCI or IRI. The developed probabilistic models are applicable to cities with a current pavement condition data, but no less than the last two consecutive years.
Performance prediction models are a vital component in pavement management systems (PMS). Along with decision trees, prediction models are used to set priorities for maintenance and rehabilitation planning, and ultimately for budget allocations at the network level. Reliable and accurate prediction of pavement deterioration over time helps transportation agencies accurately predict future spending and save significant amounts of money. Within a PMS, raw performance data is often converted into aggregated performance indices, such as the Riding Comfort Index (RCI), to quantify the road's roughness, or the Distress Surface Index (SDI), to quantify accumulated pavement distress. Technology has evolved rapidly in the last two decades, making data collection for pavement conditions (i.e. roughness and distress data) more feasible for transportation agencies. However, transportation agencies, especially at the municipal level, only maintain condition data to evaluate the present pavement status. Only limited attempts have so far been made to develop or enhance existing deterioration models in pavement management systems, using periodically collected condition data over time. A well-maintained historical database of pavement condition measurements and performance indices can be a useful source for the development of performance prediction models. In some cases, however, the database may contain incomplete data and insufficient information to develop reliable performance models. In addition to inconsistency in the historical performance data, the age of the pavement or the date of the last maintenance/ rehabilitation treatment may not be available to develop the pavement performance over time. The goal of this research is to develop enhanced empirical performance models capable of capturing the unpredictable and indeterminate nature of pavement deterioration behavior. This research provides a methodology to develop empirical models in the absence of the construction and/or rehabilitation dates. The models developed in this research use limited available historical data, and examine different parameters, such as pavement thickness, traffic pattern, and subgrade condition. Parameters such as the date of pavement construction and the age of the pavement are also incorporated into the proposed models, and are constrained by local experience and engineering judgment. A linear programming optimization technique is employed to develop the empirical models presented in this research. The approach demonstrated in this research can also be expanded to account for additional parameters, and can easily be adapted to match the needs of different agencies based on their local experience. In addition, the current research develops a second set of deterioration models based on mechanistic-empirical principles. Models incorporated into the mechanistic-empirical design guide are locally calibrated. A genetic algorithm optimization technique is employed to guide the calibration process, in order to determine the coefficients that best represent pavement performance over time. The two sets of performance models developed in this research are compared at both the project and network level of analysis. A decision-making framework is implemented to incorporate the two sets of models, and a comprehensive life cycle cost analysis is carried out to compare design alternatives in the project level analysis. The two model sets are also evaluated at the network level analysis using a municipal pavement management system. Two budget scenarios are executed, based on the developed performance models, and a comparison between network performance and budget spending is presented. Finally, a summary and current research contribution to the pavement industry will be presented, along with recommendations for future research.
Pavement and Asset Management contains contributions from the World Conference on Pavement and Asset Management (WCPAM 2017, Baveno, Italy, 12-16 June 2017). For the first time, the European Pavement and Asset Management Conference (EPAM) and the International Conference on Managing Pavement Assets (ICMPA) were joining forces for a global event that aimed not only at academics and researchers, but also at practitioners, engineers and technicians dealing with everyday tasks and responsibilities related to transport infrastructures pavement and asset management. Pavement and Asset Management covers a wide range of topics, from emerging research to engineering practice, and is grouped under the following themes: - Data quality and monitoring - Economics, political and environmental management, strategies - Deterioration models - Key performance indicators - PMS-case studies - Design and materials - M&R treatments - LCA & LCCA - Risk and safety - Bridge and tunnel management - Smart infrastructure and IT Pavement and Asset Management will be valuable to academics and professionals interested and/or involved in issues related to transport infrastructures pavement and asset management.
The growing interest in pavement management systems (PMSs), both in South Mrica and internationally, has been in response to a shift in importance from the construction of new roads to the maintenance of the existing paved network coupled with increasingly restrictive road funding. In order to develop a balanced expenditure programme for the national roads of South Africa there is a need to predict the rate of deterioration of a pavement and the nature of the changes in its condition so that the timing, type and cost of maintenance needs could be estimated. Internationally these expected changes in pavement condition are predicted by pavement deterioration models, which normally are algorithms developed mathematically or from a study of pavement deterioration. Since no usable pavement deterioration models existed locally, it was necessary to evaluate overseas literature on pavement deterioration prediction models with the aim of identifying models possibly applicable to the national roads of South Africa. Only deterioration models developed from the deterioration results of inservice pavements under a normal traffic spectrum were evaluated. Models developed from accelerated testing were avoided since these models virtually eliminated long?term effects (these are primarily environmental but also include effects of the rest periods between loads), and that the unrepresentative traffic loading regimes can distort the behaviour of the pavement materials, which is often stress dependent. Models developed from the following studies were evaluated: AASHO Road Test The Kenya study Brazil-UNDP study (HDM-ill models) Texas study Of all the above models studied that were developed from major studies it was concluded that the incremental models developed during the Brazil study, were the most appropriate for further evaluation under South African conditions. A sensitivity analysis was conducted on the HDM-III models to evaluate their sensitivity to changes in the different parameters comprising each model. The results obtained from the sensitivity analysis indicate that the incremental roughness prediction model incorporated into the HDM-III model tends to be insensitive to changes in most parameters. Accuracy ranges for input data were, however, also identified for parameters which indicated an increase in sensitivity in certain ranges. The local applicability of the HDM-III deterioration models were finally evaluated by comparing HDM-III model predictions with the actually observed deterioration values of a selected number of national road pavement sections. To enable the above comparison, a validation procedure had to be developed according to which the format of existing data could be transformed to that required by the HDM-ill model, as well as additional information be calculated. From the comparison it was concluded that the HDM-III models are capable of accurately predicting the observed deterioration on South African national roads, but that for most models calibration is needed for local conditions. Guidelines regarding recommended calibration factor ranges for the different HDM-ill models are given. Finally it is recommended that the HDM-III models should be considered for incorporation into a balanced expenditure programme for the national roads of South Africa.
Emphasizing sound, cost-effective management rather than emergency repairs, this comprehensive volume offers practical guidelines on evaluating and managing pavements for airports, roads, and parking lots. The author focuses on the implementation and maintenance of successful management strategies for both network and project levels, with repair techniques also described . Detailed chapters: 1) outline step-by-step procedures for project and network level pavement management 2) illustrate effective cost analysis and budget planning for pavement maintenance 3) guide the reader in the selection and use of non-destructive deflection, roughness measurement, and friction measurement equipment 4) present state-of-the-art pavement rehabilitation and condition prediction techniques 5) demonstrates the Pavement Condition Index (PCI) procedure for airfields and surfaced and unsurfaced roads. Extensive appendices serve as a field manual for identifying all types of pavement distress and their causes, and hundred of photographs facilitate accurate pavement evaluation. Civil and pavement engineers will find complete information on pavement inspection, evaluation, and management in this indispensable reference.
Pavement Maintenance and Rehabilitation (M&R) are the most critical and expensive components of infrastructure asset management. Increasing traffic load, climate change and resource limitations for road maintenance accelerate pavement deterioration and eventually increase the need for future maintenance treatments. Consequently, pavement management programs are increasingly complex. The complexities are attributed to the precise assessment process of the overall pavement condition, realistic distress prediction and identification of cost-effective M&R schedules. Cost-effective road M&R practices are only possible when the evaluation of pavement condition is precise, pavement deterioration models are accurate, and resources must also be available at the right time. In a Pavement Management System (PMS), feasible M&R treatments are identified at the end of each branch of the decision trees. The decision trees are based on empirical relationships of the pavement performance index. Moreover, the predicted improvements in pavement performance for any treatment are set based on engineering experiences. Furthermore, the remaining service life of the pavement is estimated from the predicted deterioration of the overall condition. The future deterioration of the overall condition is estimated based on the initial condition and by considering only the effect of age notwithstanding the effect of traffic or materials. In assessing the overall condition of the pavement, this research overcomes the limitations of engineering judgment by incorporating a Mechanistic-Empirical (M-E) approach and estimating the improvement in performance for specific treatment types. It also considers the effect of traffic and materials on pavement performance to precisely predict its future deterioration and subsequent remaining service life. The objective of this research is to develop cost-effective pavement M&R schedules by incorporating (a) the M-E approach into the overall condition index and (b) the estimate of performance indices by considering the factors affecting pavement performance. The research objective will be accomplished by (i) incorporating variability analysis of existing performance evaluation practices and maintenance decisions of pavement, (ii) investigating estimates of existing performance indices, (iii) incorporating the M-E approach: sensitivity analysis, prediction, comparison and verification, (iv) estimating the deterioration model based on traffic characteristics and material types, and (v) identifying cost-effective M&R treatment options through Life Cycle Cost Analysis (LCCA). This study uses the pavement performance data of Ontario highways recorded in the Ministry of Transportation (MTO) pavement database. Precise assessment of pavement condition is a significant part in achieving the research goal. In a PMS, an accurate location reference system is necessary for managing pavement evaluations and maintenance. The length of the pavement section selected for evaluation may have a significant impact on the assessment irrespective of the type of performance indices. In Ontario, the highway section lengths range from 50m to 50,000m. For this reason, a variability in performance evaluation is investigated due to changes in section length. This study considers rut depth, Pavement Condition Index (PCI), and International Roughness Index (IRI) as performance indices. The distributions of these indices are compared by the following groupings of section lengths: 50m, 500m, 1,000m and 10,000m. The variations of performance assessments due to changing section lengths are investigated based on their impact on maintenance decisions. A Monte Carlo simulation is carried out by varying section lengths to estimate probabilities of maintenance work requirements. Results of such empirical investigations reveal that most of the longer sections are evaluated with low rut depth and the shorter sections are evaluated with high rut depth. This Monte Carlo simulation also reveals that 50m sections have a higher probability of maintenance requirements than 500m sections. The method of estimating performance indices is also investigated to identify the requirement of improvement in estimation of the prediction models. Generally, in a PMS, the prediction models of Key Performance Indicators (KPIs) are estimated by using the Ordinary Least Square (OLS) approach. However, the OLS approach can be inefficient if unobserved factors influencing individual KPIs are correlated with each other. For this reason, regression models for KPI predictions are estimated by using an approach called the 'Seemingly Unrelated Regression (SUR)' method. The M-E approach is used in this study to predict the future distresses by employing mechanistic-empirical models to analyze the impact of traffic, climate, materials and pavement structure. The Mechanistic-Empirical Pavement Design Guide (MEPDG) software uses a three-level hierarchical input to predict performance in terms of IRI, permanent deformation (rut depth), total cracking (reflective and alligator), asphalt concrete (AC) thermal fracture, AC bottom-up fatigue cracking and AC top-down fatigue cracking. However, these inputs have different levels of accuracy, which may have a significant impact on performance prediction. It would be ineffective to put effort for obtaining accuracy at Level 1 for all inputs. For this reason, a sensitivity analysis is carried out based on an experimental design to identify the effect of the accuracy level of inputs on the distresses. Following this, a local sensitivity analysis is carried out to identify the main effect of input variables. Interaction effects are also analyzed based on a random combination of the inputs. Since the deterioration of pavement is affected by site-specific traffic, local climate and properties of materials, these variables are carefully considered during the development of the pavement deterioration model to assess overall pavement conditions. The prediction model is developed by using a regression approach considering distresses of the M-E approach. In this study, the deterioration model is estimated for three groups of Annual Average Daily Traffic (AADT) to recognize their individual impact along with properties of materials. The time required for maintenance is also estimated for these categories. The investigations reveal that the expected time to maintenance for overlay with Dense Friction Course (DFC) and Superpave mixes is higher than other Hot Laid (HL) asphalt layers. This will help pavement designers and managers to make informed decisions. The probability of failure is also investigated by a probabilistic approach. With the increasing trend towards M&R of existing pavements, it is essential to make cost-effective use of the M&R budget. As such, identification of associated cost-effective M&R treatments is not always simple in most PMS. For this reason, a LCCA is carried out for alternate pavement treatments using the deterioration model based on traffic levels and material types. Comparing the Net Present Worth (NPW) value of alternative treatment options reveals that the overlay of pavement with DFC is the most cost-effective choice in the case of higher AADT. On the other hand, overlay with Hot Laid-1 (HL-1) is a cost-effective treatment option for highway sections with lower AADT. Although the results are related to the Ontario highway system, this can also be applied elsewhere with similar conditions. The outcome of the empirical investigations will result in the adoption of efficient road M&R programs for highways based on realistic performance predictions, which have significant impact on infrastructure asset management.
