This report presents information on the characterization of cementitiously stabilized layers and the properties that influence pavement performance. It also contains recommended performance-related procedures for characterizing these layers and performance-prediction models for incorporation into the mechanistic–empirical pavement analysis methods. Individual chapters highlight pavement distresses of hot-mix asphalt pavements and concrete pavements, laboratory tests and model development, and model calibration. The material contained in the report will be of immediate interest to state materials, pavement, and construction engineers and others involved in the different aspects of pavement design and construction.

Characterization of Cementitiously Stabilized Layers for Use in Pavement Design and Analysis

Particle shape analysis of coarse aggregate using digital image processing

Digital image-based numerical modeling method for prediction of inhomogeneous rock failure

Evaluating the condition of transportation infrastructure is an expensive, labor intensive, and time consuming process. Many traditional road evaluation methods utilize measurements taken in situ along with visual examinations and interpretations. The measurement of damage and deterioration is often qualitative and limited to point observations. Remote sensing techniques offer nondestructive methods for road condition assessment with large spatial coverage. These tools provide an opportunity for frequent, comprehensive, and quantitative surveys of transportation infrastructure. The goal of this paper is to provide a bridge between traditional procedures for road evaluation and remote sensing methodologies by creating a comprehensive reference for geotechnical engineers and remote sensing experts alike. A comprehensive literature review and survey of current techniques and research methods is provided to facilitate this bridge. A special emphasis is given to the challenges associated with transportation assessment in the aftermath of major disasters. The use of remote sensing techniques offers new potential for pavement managers to assess large areas, often in little time. Although remote sensing techniques can never entirely replace traditional geotechnical methods, they do provide an opportunity to reduce the number or size of areas requiring site visits or manual methods.

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Review of remote sensing methodologies for pavement management and assessment

Potholes can generate damage such as flat tire and wheel damage, impact and damage of lower vehicle, vehicle collision, and major accidents. Thus, accurately and quickly detecting potholes is one of the important tasks for determining proper strategies in ITS (Intelligent Transportation System) service and road management system. Several efforts have been made for developing a technology which can automatically detect and recognize potholes. In this study, a pothole detection method based on two-dimensional (2D) images is proposed for improving the existing method and designing a pothole detection system to be applied to ITS service and road management system. For experiments, 2D road images that were collected by a survey vehicle in Korea were used and the performance of the proposed method was compared with that of the existing method for several conditions such as road, recording, and brightness. The results are promising, and the information extracted using the proposed method can be used, not only in determining the preliminary maintenance for a road management system and in taking immediate action for their repair and maintenance, but also in providing alert information of potholes to drivers as one of ITS services.

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Image-Based Pothole Detection System for ITS Service and Road Management System

Predicting future conditions of pavement plays an important role in pavement management. Prediction for a specific pavement is usually based on the deterioration trend of a group of pavements with similar characteristics, i.e., the same pavement family. This study proposes using the linear mixed effects model (LMEM) to predict future conditions of a specific pavement section by a weighted combination of the average deterioration trend of the family and the past conditions of the specific pavement. The relative weights are determined by the number of past condition measurements available and the degree of variations of the measured past conditions for the specific pavement. The results of the LMEM show significantly higher accuracy in predicting specific pavement conditions compared with two existing adjustment methods that use the last available condition measurement of the specific pavement to adjust the family trend prediction. The finding of this study shows that the LMEM can be used for project level ...

Development of Linear Mixed Effects Models for Predicting Individual Pavement Conditions

The International Roughness Index (IRI) has been used as a road roughness index on highways for nearly two decades. Research shows that subjective ratings such as present serviceability rating or mean panel rating could be reasonably predicted from IRI. Acceptable IRI values for highways have been developed and published by FHWA. Such IRI standards, however, are not applicable to local streets. Because vehicle speeds are lower on local streets, a comfortable ride can still be achieved at higher IRI values. There is a need to determine the acceptable IRI threshold values for local streets with various speed limits. Such IRI threshold values allow local pavement management officials to compare objectively the ride quality of streets with different speed limits. It was found that human ratings depend linearly on the logarithm of the rate of change of the vertical acceleration, namely, the jolt or jerk experienced by the raters. By analyzing the IRI records in the Long-Term Pavement Performance database and the corresponding calculated jolts, the authors found that jolt is linearly proportional to IRI at a given speed and approximately linearly proportional to the travel speed for a given IRI. By assuming further that the same jolt will lead to the same ride quality and that Interstate highways are operated at 120 km/h (75 mph), the authors use the jolt corresponding to the IRI thresholds set by FHWA for highways to develop speed-related ride quality thresholds at different travel speeds. The variation of jolt for roads with the same IRI value is also discussed.

Development of Speed-Related Ride Quality Thresholds Using International Roughness Index

Development of a genetic algorithm (GA)-based optimization tool for determining the optimal multiyear pavement repair schedule is described. The problem of finding the best multiyear work plan can be modeled as a combinatorial optimization problem, the objective of which is to achieve the highest possible average network condition for a given budget and operating constraints. Two GAs and a dynamic programming (DP) approach were implemented to determine multiyear repair schedules. The DP method resulted in optimal solutions, but it suffers from rigidity and inability to handle large-scale problems. The GA techniques obtained solutions that were near optimal and maintained flexibility and scalability. The GAs implemented were of two types: simple and preconstrained. The simple GA uses constraints when searching for solutions. The preconstrained GA uses constraints that limit which repair can be selected before searching for solutions to attain better efficiency.

Multiyear pavement repair scheduling optimization by preconstrained genetic algorithm

In this paper, the clusterwise regression model, which fits data to more than one curve, was introduced to model the deterioration of pavement condition. To make the model solvable in practice, a modification was made to the model by the membership of a pavement to each cluster being estimated with the fuzzy sets concept with the corresponding errors. The number of unknowns in the modified model is reduced significantly. The model was then extended to be applicable to cases in which more than two clusters or nonlinear equations were used. Based on the result of the modified clusterwise regression model, a procedure was proposed to predict the pavement condition rating (PCR) for any individual pavement, given that the pavement condition rating at the present age was known. In the example, the ordinary least squares regression method was first employed to determine the PCR prediction curve for a pavement group. The PCRs of the individual pavements were predicted by an adjusted prediction curve based on the prediction curve of the group. The proposed prediction procedure was then applied to the same data set to make the predictions. The results showed that the proposed procedures using the modified clusterwise regression method could result in a smaller prediction error and thus produce a more accurate prediction than one produced with the adjusted curve. Therefore, to improve the accuracy of the pavement condition predictions, the modified clusterwise regression model is recommended in cases in which the pavement families are not well defined.

Pavement Condition Prediction Using Clusterwise Regression

The objectives of this study were to: (1) determine the cost effectiveness of thin hot mix asphalt (HMA) overlays as a maintenance technique; (2) determine under what conditions a thin overlay would be suitable; (3) determine the timing of constructing a thin overlay to maximize its benefits; and (4) develop a prototype aggregate source information system to correlate aggregate source quality to pavement performance Performance data for thin overlays constructed by the Ohio Department of Transportation (ODOT) since 1990 were collected to study the cost-effectiveness of thin overlay

Eddie Yein Juin Chou

Papers

Development of Linear Mixed Effects Models for Predicting Individual Pavement Conditions

Development of Speed-Related Ride Quality Thresholds Using International Roughness Index

Multiyear pavement repair scheduling optimization by preconstrained genetic algorithm

Pavement Condition Prediction Using Clusterwise Regression

Effectiveness of Thin Hot Mix Asphalt Overlay on Pavement Ride and Condition Performance