Pavement Management System for Low-volume Paved Roads in Wyoming
Pavement Management System for Low-volume Paved Roads in Wyoming

Author: Marwan Hafez

Publisher:

Published: 2015

Total Pages: 188

ISBN-13: 9781339441450

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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.

Comprehensively Optimized Pavement Management System for Low-volume Paved Roads
Comprehensively Optimized Pavement Management System for Low-volume Paved Roads

Author: Marwan Hafez

Publisher:

Published: 2019

Total Pages: 421

ISBN-13: 9781085622899

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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.

Best Practices to Support and Improve Pavement Management Systems for Low-volume Paved Roads
Best Practices to Support and Improve Pavement Management Systems for Low-volume Paved Roads

Author: Marwan Hafez

Publisher:

Published: 2018

Total Pages: 192

ISBN-13:

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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.

Servicability Prediction Models and Roughness Estimation Through Smartphones for County Paved Roads in Wyoming
Servicability Prediction Models and Roughness Estimation Through Smartphones for County Paved Roads in Wyoming

Author: Waleed Aleadelate

Publisher:

Published: 2016

Total Pages: 122

ISBN-13: 9781369182064

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In Wyoming, most county paved roads were built decades ago without following minimum design standards. However, the recent increase in industrial/mineral activities in the state requires developing a Pavement Management System (PMS) for local paved roads. Thus, The Wyoming Technology Transfer Center/Local Technical Assistant Program (WYT2/LTAP) is currently in the process of developing a PMS for county roads. The PMS which is being developed uses the present serviceability index (PSI) as a pavement performance parameter. The primary steps in the development process show that there are two major issues related to the development of county roads PMS; the none availability of suitable PSI prediction models for county roads and the high costs related to the pavement condition data collection process. This study comprises of two parts. The first part of this study deals with the development of exclusive county roads PSI models. The developed PSI models for county roads are based on: International Roughness Index (IRI), Pavement Condition Index (PCI), and rut depth for flexible pavements only. Ten panelists from Wyoming rated 30 pavement sections that were randomly selected at different distresses’ levels using two vehicles (SUV and Sedan). Regarding the rating process, the F-test results for equal variances indicated that the seating position, age, and gender were not significant to the rating process. However, the vehicle’s type was significant. One model (Sedan model) was proposed to be used in the county roads PMS. The newly proposed model explains 80 percent of the variations in the PSI values of county roads (Adjusted R2 = 0.80). In addition, the new model seems to provide more realistic representation of the county roads conditions. In the second part of this study, modern smartphones are proposed as a cost effective solution to minimize the costs of collecting pavement condition data. Modern smartphones are equipped with many useful sensors such as gyroscopes, magnetometers, GPS receivers and 3D accelerometers. Smartphones’ 3D accelerometer was used for collecting a vehicle’s vertical acceleration data. Through the use of various signal processing and pattern recognition techniques such as cross correlations, Welch periodograms, and variance analyses, the measured signals (time series acceleration data) were identified and correlated with the actual IRI values. It was found that the variance among the vertical acceleration measurements was the key feature for classifying the measured signals. A validation analysis was also conducted to measure the reliability of this methodology. The initial validation results suggested that, using the aforementioned methodology, the smartphones used could predict with high certainty the actual IRI values. In addition, the difference between the predicted and the actual IRI values was not statistically significant. The smartphones data were collected over 20 roadway segments. The selected segments have various lengths and geometric features reflecting the actual roadway segments under any PMS.

Calibration of the Mechanistic-empirical Pavement Design Guide for Local Paved Roads in Wyoming
Calibration of the Mechanistic-empirical Pavement Design Guide for Local Paved Roads in Wyoming

Author: Taylor J. Kasperick

Publisher:

Published: 2013

Total Pages: 190

ISBN-13: 9781303160103

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The Mechanistic-Empirical Pavement Design Guide (MEPDG) was released in 2004 under NCHRP Project 1-37A. Since that time, considerable efforts to calibrate the program and its performance prediction models for local conditions have taken place in multiple states attempting to implement the program. Currently, Wyoming DOT is in the process of implementing the DARWIN-ME (the MEPDG in its most current form) on the interstate and the state highway systems. In order to compliment that effort, this study attempted to develop a set of calibration coefficients and localized traffic inputs that can be used on local paved roads. Wyoming is an energy rich state and has seen an increase in the amount of heavy truck traffic that its roadways encounter, thus requiring locally calibrated inputs for the DARWIN-ME. Predicted distresses using the DARWIN-ME were largely different from measured distresses on local paved roadways included in this study, particularly IRI, rutting, alligator cracking, transverse cracking, and longitudinal cracking. These distresses were measured on the local paved roads using Pathway Services Inc. and the surface imaging that it provided. Inputs for trial runs using the DARWIN-ME were determined through work with local county road maintenance superintendents, WYDOT, and previous research regarding climatic data in Wyoming. Localized traffic inputs were developed using Weigh-In Motion (WIM) data collected on non-interstate roadways across Wyoming. Once a significant error and bias were found between predicted and measured distresses, calibration coefficients for IRI, alligator cracking, rutting, and longitudinal cracking were altered to reduce bias and sum of squared errors. The final calibration coefficients settled on in this study reduced the sum of squared errors and bias significantly. A sensitivity analysis was also performed during this study to determine the effect of layer thicknesses on the prediction capabilities of the DARWIN-ME. The process followed in this study can be utilized by other local governments around the country to help them implement the DARWIN-ME.

Comprehensive Evaluation of Pavement Maintenance Activities Applied to Colorado Low-volume Paved Roads
Comprehensive Evaluation of Pavement Maintenance Activities Applied to Colorado Low-volume Paved Roads

Author: Marwan Hafez

Publisher:

Published: 2018

Total Pages: 157

ISBN-13:

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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.

Wyoming Low-volume Roads Traffic Volume Estimation
Wyoming Low-volume Roads Traffic Volume Estimation

Author: University of Wyoming. Department of Civil and Architectural Engineering

Publisher:

Published: 2015

Total Pages: 227

ISBN-13:

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Low-volume roads are excluded from regular traffic counts except on a need to know basis. But needs for traffic volume data on low-volume roads in road infrastructure management, safety, and air quality analysis have necessitated regular traffic volume estimates. This study developed traffic volume estimation models for low-volume roads in Wyoming. A review of existing estimation models was carried out. Two main model types were identified - regression models and Travel Demand Models (TDMs). The study developed the two model types and recommended the best model for implementation. Two regression models were developed, a linear and a logistic regression model. Each of the regression models was developed using data from 13 randomly selected counties and nine counties were used in model validation. The linear regression model had an R2 of 64 percent and was verified to be a good predictor of traffic volumes across Wyoming. The logistic regression model validation indicated a prediction accuracy ranging from 78 to 89 percent. The TDM was developed using standard factors and trip rates in the NCHRP Report 365. The TDM was implemented for four south eastern counties in Wyoming. The model was then validated and calibrated by comparing actual traffic volumes to those generated by the model. The calibrated model had a Percentage Root Mean Square Error and an R 2 values of 50 and 74 percent respectively. The report compared the three models with respect to cost-effectiveness, ease of use, and accuracy and recommended the TDM for implementation. The regression models were recommended for applications requiring quick traffic volume estimations and for which lower levels of accuracy are acceptable.