Ningyuan Li

Reliability-Based Processing of Markov Chains for Modeling Pavement Network Deterioration

Accurate prediction of pavement deterioration is the most important factor in the determination of pavement repair years and optimization programming of highway network maintenance. The Nonhomogeneous Markov Probabilistic Modeling Program, developed to determine pavement deterioration rates in different stages, is described. In this program the transition probability matrices (TPMs) are considered as a time-related transition process. Each element of the TPMs is determined on the basis of a reliability analysis and a Monte Carlo simulation technique. This avoids the use of the existing conventional methods, which involve taking an average subjective opinion of pavement engineers or observing a large number of multiyear pavement performance data and conducting a number of statistical calculations. As a result a series of TPMs for an individual pavement section for different stages can be determined by running the program. Furthermore, the pavement condition state in terms of a probability vector at each stage (year) is calculated. In applying the models both the predicted actual traffic (in terms of equivalent single axle loads) at each stage and the maximum traffic that the pavement can withstand at each defined pavement condition state interval are considered to be random variables. In addition, the sensitivities of pavement deterioration rates to pavement design parameters, such as traffic growth rate, subgrade strength, and material properties, are studied. Finally, an example of calculating the TPMs for a pavement section located in southeastern Ontario, Canada, is demonstrated. It shows that the sensitivities of the TPMs to traffic growth rate, subgrade deflection, and pavement thickness are significant.

Incorporating Road Safety into Pavement Management

Improving road safety through proper pavement engineering and maintenance should be one of the major objectives of pavement management systems. When pavements are evaluated in terms of safety, a number of factors related to pavement engineering properties are raised, such as pavement geometric design, paving materials and mix design, pavement surface properties, shoulder type, and pavement color and visibility. Each year there are voluminous annual reports on traffic accident statistics and discussions of such road safety issues as road safety modeling and pavement safety measurements and criteria. Although road safety may be considered a separate area, it should be incorporated into pavement management systems. The main pavement engineering relationships associated with road safety are identified, and the various aspects of road safety related to pavement management, such as pavement types, pavement surface macrotexture and microtexture, and pavement safety measurements, criteria, and evaluation methods, are discussed. A systematic approach is proposed for the coordination of pavement maintenance programs with road safety improvement and the incorporation or integration of safety management with pavement and other management systems. Finally, a list of possible remedial measures for road safety improvements associated with pavement maintenance activities is recommended.

Investigation of relationship between deterministic and probabilistic prediction models in Pavement management

A good pavement management system should have the capacity to predict pavement structural and functional deterioration versus age or accumulated traffic loading. Basically, there are two types of performance prediction models in pavement management: deterministic and probabilistic. Although both performance models can be used to predict pavement deterioration, the inherent relationship between the two models has not been explored. An investigation was directed to find the relationship in terms of system conversion. Some of the findings related to system conversion, including the concepts and techniques applied in model conversion, the characteristics of model development, comparisons of prediction results between the two models, sensitivity analysis of the probabilistic models, and sample applications in real situations, are highlighted. The deterministic models that are to be converted to probabilistic models are the flexible pavement deterioration model used in the Ontario Pavement Analysis of Costs system and the flexible pavement design model recommended in the 1993 AASHTO design guide. The converted probabilistic models are time-related (nonhomogeneous) Markov processes, which are represented by a set of yearly transition probability matrices (TPMs). TPMs can be established for any individual pavement section in a road network.

Integer Programming of Maintenance and Rehabilitation Treatments for Pavement Networks

A new approach to multiyear maintenance and rehabilitation (M&R) optimization programming for pavement network management is discussed; the approach can be used to help highway agencies make strategic decisions in choosing the optimal investment for their pavement networks. The M&R treatments are standardized in terms of costs, benefits, and performance impacts on the existing pavements. Each standardized pavement treatment strategy, ranging from minor and routine maintenance to major rehabilitation or reconstruction, is defined by its effect and improvement on the existing pavement serviceability. The optimization model is a cost-effectiveness-based integer M&R programming on a year-by-year basis. The objective of the optimization system is to select the most effective M&R projects for each programming year. The optimization system can also be used to calculate the minimum budget requirements for maintaining a prescribed level of the pavement network performance or serviceability. In such a case, sensitivity analysis can be performed to evaluate the annual budget effect on individual pavement performance. The prediction of individual pavement deterioration is modeled as a time-related (nonhomogeneous) Markov transition process. The investigation described was primarily concerned with integration of the performance prediction model, the standardized M&R treatments, and the network optimization process. The principle and methodology developed can be applied to different levels of pavement network management. Finally, a sample application of the integrated pavement optimization model is demonstrated.

COST-EFFECTIVENESS-BASED PRIORITY PROGRAMMING OF STANDARDIZED PAVEMENT MAINTENANCE

A new optimization model and priority programming for pavement network maintenance and rehabilitation management are described. The optimization formulation is directed to determining the most cost-effective treatment action plans for preserving the pavement network’s serviceability above a specified level. The priority programming is conducted on a year-by-year basis, whereas a comprehensive prediction model for pavement deterioration versus time is considered. It is governed by traffic volume, pavement performance, a set of designed standard treatment alternatives, and budget limitations for network preservation. Each standardized pavement treatment alternative, including minor and major maintenance and rehabilitation, is defined by its effect on or level of improvement of the existing pavement surface quality and the corresponding costs. The effectiveness is calculated as a yearly product of the area under the performance curve and a minimum acceptable pavement condition index level multiplied by pavement length, traffic volume, and service days. The costs for applying any one of the standardized alternative treatments are expressed on a present worth basis. The prediction for each individual pavement deterioration is modeled as a time-related (nonhomogeneous) Markov transition process, in which pavement structural and functional improvements upon application of a treatment action are considered. The focus is on an integrated approach to pavement network preservation programming through cost-effectiveness analysis and comprehensive performance prediction in combination with standardized pavement treatment strategies. A case study application to a regional asphalt pavement network in Ontario, Canada, illustrates the use of the optimization model. The priority programming is practical and flexible with regard to the size of a road network, and the results of the example run are discussed.

Validation and Implementation of Ontario, Canada, Network-Level Distress Guidelines and Condition Rating

The Centre for Pavement and Transportation Technology at the University of Waterloo and the Ministry of Transportation of Ontario (MTO) have been studying for the past 4 years the suitability of applying automated technologies for network-level evaluations in the province. Three projects have been developed for this purpose. The main results of these studies were a better understanding of available digital technologies, evaluation of the performance of semiautomated and automated technologies, development of new guidelines for pavement distress collection at the network level, design of an adjusted distress manifestation index, and recommendations for the use of semiautomated and automated digital technologies at the network level. The objective of this paper is to present the findings of the third and last phase of the project, Validation and Implementation of MTO Network Level Automated–Semiautomated Pavement Distress Guidelines and Condition Rating Methodology. The scope of the study was to validate and implement in the field MTO network-level distress guidelines and a distress manifestation index for network-level evaluations (DMINL), considering the use of automated technologies. A complete statistical analysis of data collected in the field through manual evaluations and semiautomated and automated technologies is presented. The performance of currently available technologies using network-level distress guidelines was assessed. Finally, from the field validation, distress guidelines were adjusted accordingly and DMINL equations were recalibrated.