Nasir G. Gharaibeh

Assessing the Agreement among Pavement Condition Indexes

Pavement condition indexes are numerical indicators of the structural and material integrity of a pavement. Because these indexes appear to be similar (essentially a 0–100 scale, with 100 indicating ideal condition), it can be tempting to use different indexes for comparing the performance of pavement networks in different states or jurisdictions within a state. To ascertain the level of agreement among these condition indexes, six pavement condition indexes from five DOTs in the United States are discussed and compared using distress and ride quality data obtained from the Pavement Management Information System of the Texas Department of Transportation. The computed scores were compared visually (using scatter plots) and statistically (using paired t -test). The results provide empirical evidence that there are significant differences among seemingly similar pavement condition indexes.

A Spatial-Bayesian Technique for Imputing Pavement Network Repair Data

This article describes how pavement construction and repair history is necessary for several pavement management functions such as developing pavement condition prediction models and developing maintenance and rehabilitation (M&R) trigger values that are based on past repair frequencies. The article describes how it is often difficult to integrate M&R data with condition data since these data are often stored in disparate heterogeneous databases. The article provides a computational technique for estimating construction and M&R history of a pavement network from the spatiotemporal patterns of its condition data. The technique is founded on Bayesian and spatial statistics and searches pavement condition data in groups of adjacent pavement sections for evidence of repair. The technique developed in this article was applied to a pavement network in Texas and has been found to have a 74% precision and a 95% accuracy in estimating repair history data.

Prioritizing Infrastructure Maintenance and Rehabilitation Activities Under Various Budgetary Scenarios: Evaluation of Worst-First and Benefit–Cost Analysis Approaches

Infrastructure maintenance and rehabilitation (M&R) projects are commonly prioritized by using the worst-first (W-F) and benefit–cost analysis (BCA) approaches. While many acknowledge the inherent disadvantages of the W-F approach relative to that of the BCA, many transportation and public works agencies still use the W-F approach. W-F and BCA approaches were compared in regard to their impact on network condition (specifically, lane miles in good condition and backlog) under various budgetary scenarios. These comparisons were motivated by the premise that under certain budget allocation and availability scenarios, the shortcomings of the W-F approach might be abated. The analysis presented used highway pavement network data from the Bryan District of the Texas Department of Transportation. The Bryan District is located in east central Texas (wet-warm climate and generally poor subgrade). In 2011, this network consisted of approximately 3,178 roadbed centerline miles. Results suggest that when M&R share a single combined budget, the W-F approach is dramatically less effective than the BCA approach in improving the network condition and reducing backlog. However, when the M&R budget is divided into two separate budgets (one for maintenance and one for rehabilitation), the disadvantages of the W-F approach diminish.

Determining Optimum Sample Size for Percent-Within-Limits Specifications

Highway construction and materials acceptance plans use a sample size that is often established on the basis of practical considerations such as personnel and time constraints. Commonly used sample sizes range between three and seven units. While a sample size within this range may be practical, it may not be economically optimal. If this sample size is too small, the probability of making erroneous acceptance or pay adjustment decisions (and thus the expected cost consequences of these decisions) would be too high for state departments of transportation (DOTs). If this sample size is too large, the cost of sampling and testing would be unnecessarily high, especially where destructive testing is used. A computational model for determining the optimum sample size was developed and is presented in this paper. This model is intended to help highway agencies determine how much to sample to minimize their total acceptance cost (cost of sampling and testing plus the cost of erroneously accepting poor-quality materials and construction). Inputs to this model can be obtained from an agencys specifications book, historical data on quality, prevalent unit bid prices, and prevalent sampling and testing prices. The developed model was applied to determine the optimum sample size for the AASHTO acceptance plan for binder content and density of hot-mix asphalt concrete pavements. The model shows that, when historical quality levels are satisfactory, the state DOT may consider reducing sample size as much as practically possible (in most cases, a sample size of three per lot for each acceptance quality characteristic is optimal). Only in the case of large lot size, combined with historically extremely poor quality and high unit bid price, was a larger sample size found to be optimal (n = 7 to 8).

Process to Estimate Permit Costs for Movement of Heavy Trucks on Flexible Pavements

A process based on a mechanistic–empirical (ME) analysis was developed to estimate permit fees on the basis of truck-axle loading and configuration as well as the predicted pavement deterioration that they cause. The process was implemented in a software package, Integrated Pavement Damage Analyzer (IntPave). IntPave is a finite element–based program that calculates pavement responses, uses ME distress models to predict performance under any type of traffic load, is capable of comparing the level of distress caused by a heavy truck relative to a standard truck, and accordingly provides a permit fee. On the basis of a parametric study, it was found that, aside from the truck gross vehicle weight and axle configuration, pavement structure and the damage threshold to rehabilitation also heavily affect the permit fee.

Bayesian Model for Predicting the Performance of Pavements Treated with Thin Hot-Mix Asphalt Overlays

Recent studies suggest that thin hot-mix asphalt (HMA) overlay is one of the most frequently used preservation techniques for HMA pavements. This preservation treatment is often applied to address functional problems, such as roughness, raveling and weathering, and friction loss. A novel probabilistic model is provided for predicting the international roughness index (IRI) values of asphalt pavements treated with thin HMA overlay. The model consists of two tightly coupled components. The first component is a set of artificial neural networks responsible for predicting the IRI value of the existing pavement if no treatment is applied, and the second one is a set of Bayesian regression models responsible for predicting the reduction in IRI value owing to applying the thin HMA overlay. The model considers key design and material characteristics of both the existing pavement and the thin overlay, and site factors. The developed Bayesian models have less than 5% outliers (i.e., data points falling within either 2.5% tail area of model predictions), indicating high goodness of fit for these models. It is hoped that this IRI prediction model will enable pavement engineers to estimate the performance, service life, and life-cycle costs of thin HMA overlays.

Impact of Error in Pavement Condition Data on the Output of Network-Level Pavement Management Systems

The quality of pavement condition data is important not only in assessing the current condition of the network but also in predicting its future condition and planning future maintenance and rehabilitation (M&R) activities. This paper provides a quantitative assessment of the impact of error magnitude and type (systematic and random) in pavement condition data on the accuracy of pavement management system (PMS) outputs (i.e., forecasted needed budget and M&R activities in a multiyear planning period). The process developed to simulate the propagation of pavement condition errors to PMS output consisted of five components: condition data generation, error perturbation, condition prediction, M&R prioritization, and output generation. This process was applied to the 2011 pavement condition data set of the Bryan District of the Texas Department of Transportation. In 2011, the roadway network of the Bryan District consisted of approximately 3,200 roadbed centerline miles. The study results show that both systematic and random errors can highly distort some PMS output parameters, even in error ranges that may be considered acceptable in practice. These effects tend to persist throughout the planning period. The findings of this study can help highway agencies optimize processes for collecting pavement condition data by focusing on error levels and types that cause the greatest impact on PMS output.

Project Selection and Prioritization of Pavement Preservation: Competitive Approach

Several methods help agencies select and prioritize pavement preservation projects. Often these methods are built within an agencys pavement management system. Unfortunately, these decision support tools often produce recommendations that do not match actual decisions, particularly for project selection of pavement management. Ad hoc selection procedures for preservation projects may be effective for many highway agencies. Fiscal constraints and pressure from administrators and legislators, however, have forced agencies to justify their use of funds. This paper offers a new method for the selection and prioritization of pavement projects, with the use of the analytic hierarchy process as its multicriteria decision-making platform. The new method uses several parameters and input from decision makers to create a prioritized preservation project list. The method was applied in a case study in Texas; projects suggested by the method matched actual decisions 75% of the time. The ability to capture multiple parameters and determine weights for each parameter on the basis of decision-maker input, along with the high level of agreement between the method and actual decisions, indicated that the method could be a viable decision support tool.

Use of Micro Unmanned Aerial Vehicles in Roadside Condition Surveys

Micro unmanned aerial vehicles (MUAVs) that are equipped with digital imaging systems and Global Positioning System (GPS) provide a potential opportunity for improving the effectiveness and safety of roadside condition and inventory surveys. This paper provides an assessment of the effectiveness of MUAVs as a tool for collecting condition and inventory data for roadside infrastructure assets using a field experiment. The field experiment entails performing a level of service (LOS) condition assessment on 10 roadway sample units on IH-20 in Tyler, Texas. The condition of these sample units was assessed twice: onsite (i.e., ground truth) and by observing digital images (still and video) collected via a MUAV. The results of these surveys are analyzed to determine if there are statistically significant differences in the standard deviation and mean values of the condition ratings. Additionally, the operational performance of the MUAV was observed in various weather and field conditions. The results of this study will help transportation agencies to decide if MUAV technology can be adopted for inventory and condition surveys of roadside assets and maintenance activities.

The development of a participatory assessment technique for infrastructure: Neighborhood-level monitoring towards sustainable infrastructure systems

Climate change and increasing natural disasters coupled with years of deferred maintenance have added pressure to infrastructure in urban areas. Thus, monitoring for failure of these systems is crucial to prevent future impacts to life and property. Participatory assessment technique for infrastructure provides a community-based approach to assess the capacity and physical condition of infrastructure. Furthermore, a participatory assessment technique for infrastructure can encourage grassroots activism that engages residents, researchers, and planners in the identification of sustainable development concerns and solutions. As climate change impacts disproportionately affect historically disenfranchised communities, assessment data can further inform planning, aiming to balance the distribution of public resources towards sustainability and justice. This paper explains the development of the participatory assessment technique for infrastructure that can provide empirical data about the condition of infrastructure at the neighborhood-level, using stormwater systems in a vulnerable neighborhood in Houston, Texas as a case study. This paper argues for the opportunity of participatory methods to address needs in infrastructure assessment and describes the ongoing project testing the best use of these methods.