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States' Departments of Transportation (DOT) are trying to utilize the best practices of managing low-volume roads (LVRs) due to limited resources and declined transportation funding. Diverse maintenance practices and fluctuating budget allocations are noticed on LVRs which significantly impact the overall pavement performance. In this study, the optimal scheduling of maintenance strategies and effectiveness of different maintenance policies are investigated.
State Departments of Transportation (DOTs) are trying to utilize the best practices of managing low-volume roads (LVRs) due to limited resources and declined transportation funding. Diverse maintenance practices and fluctuating budget allocations are noticed on LVRs which significantly impact the overall pavement performance. In this study, the optimal scheduling of maintenance strategies and effectiveness of treatment options are investigated. Pavement maintenance decision making is supported by three approaches: subjective judgment of pavement engineers; historical data on past practice (i.e., historical pavement performance plots); and optimization-based procedures. The three approaches are integrated using a pavement management data of Colorado LVRs to provide guidelines and recommendations for Colorado DOT (CDOT) and other transportation agencies. The accumulated field experience of Colorado DOT’s pavement engineers is highlighted through a regional survey of practice. In addition, the effectiveness of low-cost treatments on the individual pavement distresses is evaluated using historical values of pavement condition indices. It was concluded that some surface treatments and recycling techniques are effective long-term treatments for fatigue, longitudinal, and transverse cracking. However, the effectiveness of these treatments depends mainly on the initial condition index. Then, an optimization analysis is conducted using genetic algorithms to provide cost-effective capital improvement plans statewide and for deteriorated LVRs with marginal pavement conditions. The large-scale optimization analysis is limited on LVRs for statewide maintenance planning. In this study, the developed optimization models have the ability to maximize the overall pavement condition of LVRs network considering an annual budget constraint. They can also minimize the maintenance costs to achieve desired performance targets by the end of the analysis period. It was concluded that most CDOT engineering regions do not have sufficient maintenance budgets to sustain the network-level pavement condition of LVRs. The results from optimization analysis provide more realistic solutions to define the budget needs on LVRs. Moreover, an effective decision-making process is achieved for each Colorado DOT’s engineering region using a machine-learning approach. Multiple treatment alternatives are proposed using artificial neural networks with pattern recognition algorithms. It was found that these approaches provide beneficial guidelines for managing LVRs in Colorado and nationwide. As a result of this study, transportation agencies can determine future budget needs, funding allocations, and treatment policies in order to demonstrate the best possible use of pavement management resources on LVRs.
The Colorado Department of Transportation (CDOT) has been trying to identify the most effective methods for managing low-volume roads (LVRs). These roads are facing multiple challenges including: reductions in maintenance budgets, impact of industrial activities, and potentially not receiving the most cost effective treatments. Considerable savings can be secured by implementing an effective and informed management system for all LVRs engineering issues, including: planning, design, and maintenance.
The Colorado Department of Transportation (CDOT) has been trying to identify the most effective methods for managing low-volume roads (LVRs). These roads are facing multiple challenges including: reductions in maintenance budgets, impact of industrial activities, and potentially not receiving the most cost effective treatments. Considerable savings can be secured by implementing an effective and informed management system for all LVRs engineering issues, including: planning, design, and maintenance.
In 2014, Wyoming Technology Transfer Center/Local Technical Assistance Program (WYT2/LTAP) initiated a pavement management system (PMS) program for county roads in the State of Wyoming. Building a PMS for county roads provides assistant and defensible tools for legislatures to allocate appropriate funds to maintain county roads. In Wyoming, there are total 2,444 miles of county paved roads managed and maintained under the supervision of local governments and municipalities. More than 50% of county paved roads have an average daily traffic (ADT) less than 400 vehicles per day. These roads are considered as low-volume roads. There is no legal requirement to implement a typical pavement management system on county and local roads. However, the funding constraints for maintaining county roads increase the importance of implementing a pavement management system on the lower systems. The most important issue in managing county paved roads as low-volume roads is to define practices and polices applied within the available resources. This study investigates appropriate tools to better manage low-volume paved roads. The tools provide effective guidelines and statistical techniques to reduce the costs of collecting pavement condition data. Online surveys were disseminated for all experts and pavement managers who are involved in preserving low-volume paved roads in Colorado and nationwide. This study developed four surveys. The summaries of only two surveys were included in this thesis since the two other surveys are in progress. A feedback from TRB standing committee members and specialist engineers in Colorado Department of Transportation (CDOT) was analyzed. The most appropriate practices and recommended tools were developed for managing low-volume paved roads using effective strategies. These strategies help local governments in Wyoming manage their county paved roads in a cost-effective manners. The automated techniques used to collect pavement condition data are relatively expensive for local agencies. In addition, there are questions about the needs to collect pavement condition data annually since county roads have relatively low traffic volumes. In order to optimize the cost of data collection, this study evaluates the possibility of reducing the amount of pavement condition data collected in each survey. Reducing the amount of collected data provides missing values. This study applies multiple imputation analyses as an assistant tool to predict the uncollected data at the network level. Another objective of this study is to determine the most cost-effective pavement condition data collection frequencies. The study uses a historical PMS data of the State secondary highways in Wyoming as a case study. A comparison between different frequencies was developed. It was concluded that uncollected pavement condition indices can be predicted using initial/historical values. The imputation models, developed in this study, provided a good estimation of the uncollected pavement condition indices. Therefore, pavement condition data of low-volume paved roads is not recommended to be collected for the whole network annually. Instead, a less expensive sequence can be adopted where the data which is not collected can be predicted using multiple imputation models developed in this study.
This book provides an overview of asphalt pavement maintenance, highlighting the key asphalt pavement maintenance technologies in China. It analyzes the trend toward preventive maintenance technologies and proposes technical guidelines and implementation rules for preventive maintenance. As such it is a valuable reference resource for technicians in related industries, both in China and abroad, as well as professionals involved in road infrastructure maintenance projects in countries participating in the Belt and Road Initiative.
This study evaluated 6 specific preventive treatments. Four treatments are for asphalt concrete surfaced (flexible) pavements: chip seals; crack sealing; slurry seals; and thin overlays. Two treatments are for portland cement concrete surfaced pavements: joint and crack sealing; and undersealing. Performance of the pavement sections with the treatments was compared with the performance of a similar pavement section without the treatment. Performance is measured in terms of pavement distress, roughness profile, surface friction, and structural capacity. All of the flexible pavement test sections and most of the rigid pavement test sections have been constructed. Performance data are being collected. This report discusses the experimental design, project selection, construction, data collection, analysis, and future activities of the pavement maintenance effectiveness project.
This book aimed to develop methods and tools for supporting maintenance management system for transportation. This is done by using Multicriteria Decision Making Process techniques. Also analytic hierarchy process (AHP) were applied to evaluate the techniques that are used for maintaining the road pavements. Software named AHPM (Analytic Hierarchy Process Model) was developed using MATLAB for flexible pavement. The first step in the AHP procedure is to decompose the decision problem into a hierarchy that consists of the most important elements of the decision problem. In developing a hierarchy identified the objective, factors and alternatives. The hierarchy model of a decision problem is the objective of the decision at the top level and then descends downwards lower level of decision factors until the level of attributes is reached. Each level is linked to the next higher level.