Imen Zaabar

Estimating the Effects of Pavement Condition on Vehicle Operating Costs

This report presents models for estimating the effects of pavement condition on vehicle operating costs. These models address fuel consumption, tire wear, and repair and maintenance costs and are presented as computational software on the accompanying CD-ROM, CRP-CD-111, to facilitate use. The material contained in the report should be of immediate interest to state pavement, construction, and maintenance engineers; vehicle fleet managers; and those involved in pavement investment decision processes and financial aspects of highway transportation.

Calibration of HDM-4 Models for Estimating the Effect of Pavement Roughness on Fuel Consumption for U. S. Conditions

Fuel consumption costs are an essential part of life-cycle cost analysis. These costs are influenced by vehicle technology, pavement condition, roadway geometrics, environment, speed, and other factors. Many models for the effects of pavement condition on fuel consumption were developed on the basis of data generated years ago in other countries for vehicles that vary substantially from those used currently in the United States. Therefore, new information is needed to help in refining and developing models that would better apply to U.S. conditions. The mechanistic model developed as part of the Highway Development and Management software (HDM-4) is recommended after calibration for predicting fuel consumption. The results of the calibration exercise for U.S. conditions, with field data collected as part of the NCHRP Project 1-45, are presented. The calibrated HDM-4 fuel consumption model was able to predict very adequately the fuel consumption of five different vehicle classes under different operating, weather, and pavement conditions. The better accuracy achieved after calibration has improved the prediction of the effect of roughness on fuel consumption. The comparison of sensitivity analyses before and after calibration has shown that the effect of roughness on fuel consumption increased by 1.75 for the van, 1.70 for the articulated truck, 1.60 for the medium car, 1.35 for the sport utility vehicle, and 1.15 for the light truck.

A Field Investigation of the Effect of Pavement Type on Fuel Consumption

Fuel consumption costs are influenced by vehicle technology, pavement condition, roadway geometrics, environment, speed, and other factors. The goal of this paper is to investigate the effect of pavement type on fuel consumption. The study has entailed the use of five instrumented vehicles to make fuel consumption measurements over different concrete (PCC) and asphalt (AC) pavement sections. The sections selected have similar characteristics (grade, roughness and texture) and differ only in the type of pavement. The data was collected at three different speeds. The results showed that the difference in fuel consumption between asphalt and concrete pavements is statistically significant at 95 percent confidence level for (loaded) light and heavy trucks at low speed (56 km/h) and summer conditions. Under these conditions, trucks driven over AC pavements will consume about 4% more than if they were driven over PCC pavements. Fuel consumption data for heavy truck in winter was not available. The analysis also showed that the mean differences of fuel consumption between asphalt and concrete pavements for passenger car, van and SUV are statistically not significant for both winter and summer conditions.

Model Development, Field Section Characterization, and Model Comparison for Excess Vehicle Fuel Use Attributed to Pavement Structural Response

In this study, consumption of energy attributed to pavement structural response through viscoelastic deformation of asphalt pavement materials under vehicle loading was predicted for 17 field sections in California by using three different models. Calculated dissipated energy values were converted to excess fuel consumption (EFC) to facilitate comparisons under different traffic loads (car, SUV, and truck) and speeds and different temperature conditions. The goal of the study was to compare the different modeling approaches and provide first-level estimates of EFC in preparation for simulations of annual EFC for different traffic and climate scenarios, as well as different types of pavement structures on the California state highway network. Comparison of the predicted EFC for test sections showed that all three models produced different results, which can be attributed to the differences in the three modeling approaches. However, predictions from the three models were generally of the same order of magnitude or an order of magnitude different, indicating that overall these models can be calibrated with data from field measurements, which is the next step in the research program.

Estimating Vehicle Operating Costs Caused by Pavement Surface Conditions

This paper presents a summary of findings on the effect of pavement roughness [international roughness index (IRI)] and texture [mean profile depth (MPD)] on vehicle operating costs. The most important cost affected by roughness was fuel consumption, followed by repair and maintenance, then tire wear. An increase in IRI of 1 m/km (63.4 in./mi) increased fuel consumption of passenger cars by 2% to 3%, regardless of speed. For heavy trucks, this increase was 1% to 2% at 70 mph and 2% to 3% at 35 mph. Surface texture and pavement type had no effect on fuel consumption for vehicle classes except heavy trucks. An increase in MPD of 1 mm (0.039 in.) increased fuel consumption by 1.5% at 55 mph and 2% at 35 mph. The effect of pavement type on fuel consumption was statistically not significant for all light vehicles and was statistically significant for heavy trucks only at 35 mph in summer conditions (308C). No data were available for heavy trucks in winter. For repair and maintenance, there was no effect of roughness up to an IRI of 3 m/km (190 in./mi). Beyond this range, an increase in IRI up to 4 m/km (254 in./mi) increased repair and maintenance costs by 10% for passenger cars and heavy trucks. At an IRI of 5 m/km (317 in./mi), the increase was up to 40% for passenger cars and 50% for heavy trucks. An increase in IRI of 1 m/km (63.4 in./mi) increased tire wear of passenger cars and heavy trucks by 1% at 55 mph.

New Mechanistic-Empirical Approach for Estimating the Effect of Roughness on Vehicle Durability

Vehicle manufacturers place a major focus on improving the design of vehicle components to respond better to changes in road surface profiles. Nevertheless, changes in the surface profile still directly affect user costs including repair and maintenance (R&M) costs and damage to goods. For example, AASHTO reported that poor road conditions added an estimated

Backcalculation of asphalt concrete modulus master curve from field-measured falling weight deflectometer data: Using a new time domain viscoelastic dynamic solution and genetic algorithm

76.8 billion to transport costs annually. The objective here is to estimate the effect of roughness, as expressed by the international roughness index, on vehicle durability and R&M costs. First, the R&M costs of Zaniewski et al. (the latest comprehensive research conducted in the United States) are updated by multiplying their reported costs by the inflation rate of R&M costs between 1982 and 2007. Then a mechanistic–empirical (M-E) methodology is proposed to estimate the effect of roughness on R&M costs. The proposed approach is based on fatigue damage analysis by using numerical modeling of the vehicle response. Finally, the results from the M-E approach are compared with the empirical results (updated Zaniewski tables) and found to be very close up to 5 m/km. The standard error is about 2%. Also, a case study of the I-69 section near Lansing, Michigan, is presented. This detailed analysis is useful for identifying those segments of the road within a project that cause higher operating costs to the traveling vehicles. These localized rough sections could then be subject to maintenance activities in order to remedy the problem.

Impact of Diamond Grinding on Rigid Pavement Performance

A new backcalculation program, DYNABACK-VE, was used to backcalculate pavement layer properties with field data. DYNABACK-VE used a time domain viscoelastic dynamic solution (ViscoWave-II) as a forward routine and a genetic algorithm for backcalculation analysis. The genetic algorithm search method was selected because it had a high potential for converging efficiently to a global solution. The forward solution used continuous integral transforms (Laplace and Hankel) that were more appropriate for transient, nonperiodic signals in the time domain. The algorithm was implemented in C++ and coded for parallel processing with multithreading for achieving better computational efficiency. Field falling weight deflectometer load and defection sensor time histories from three sites (Waverly Road near Lansing, Michigan, and two long-term pavement performance sections) were used for validation. The backcalculated asphalt concrete modulus master curves were compared with those obtained from laboratory testing. Very good agreement was obtained. The new algorithm was capable of backcalculating reliably the master curve of the asphalt concrete layer (four sigmoidal coefficients and two time-temperature shift factors), the elastic moduli for the unbound base or subbase and subgrade materials, and the modulus of stiff layer and the depth to stiff layer, if present. The advantage of the new solution is that it can analyze the response of pavement systems in the time domain and can therefore accommodate time-dependent layer properties and incorporate wave propagation. Also, because the backcalculation is performed in the time domain, the algorithm is not sensitive to truncation in the deflection time histories. This is a significant improvement to the state of the art, since truncation of deflection time histories has prevented frequency domain backcalculation solutions from being successful when measured field data are used.

Effect of Pavement Surface Conditions on Sustainable Transport

The surface roughness in terms of International Roughness Index (IRI) captures variation in vertical surface elevations along the pavement length. Diamond grinding is a rigid pavement preservation treatment which is typically utilized to increase pavement smoothness by eliminating the surface undulations. The focus of this study is to evaluate the impact of diamond grinding on rigid pavement performance by using the mechanistic-empirical procedure. The effectiveness of a diamond grinding was assessed by evaluating the IRI before and after the treatment. Generally, smoother pavements will have lower IRI, which will result in less vehicle body and axle bounce. Consequently, the pavement will be subjected to reduced dynamic loads which may cause less pavement damage or vice versa. The longitudinal profile and axle load data from the Long-Term Pavement Performance (LTPP) in the SPS-6 experiment were utilized to evaluate the impact of diamond grinding on pavement performance. These experiments were designed to evaluate the effectiveness of rigid pavement rehabilitation treatments. The data for pavement sections from two climatic zones were evaluated before and after diamond grinding. The axle distributions for each pavement section were utilized to generate truck gross-vehicle weight (GVW) distributions. Subsequently, the truck GVW distributions were used to generate truck response on different pavement surface profiles by using TruckSim software. The impact of surface roughness on dynamic loads was evaluated by comparing axle load spectra before and after diamond grinding. The variations in the axle load spectra are used to explain the differences in the predicted pavement performance in terms of cracking, faulting and IRI.

Reasoning about Cryptographic Protocols in Observational Theories

This chapter deals with the effect of pavement surface conditions on transport costs, including Vehicle Operating Costs (VOC) and damage to transported goods. The chapter starts with a brief introduction on the need for economic analysis of pavement projects in the context of sustainable pavement management strategies. Then, various user costs are presented, focusing on those cost components that are specifically affected by pavement surface conditions. These include fuel consumption, repair and maintenance, and tire wear (vehicle operating costs), and damage to transported goods/packaging costs (non-vehicle operating costs). The discussion differentiates between empirical and mechanistic models putting a vision for future mechanistic-based models. Finally, a section on trends in emerging vehicle and tire technology and how they affect future costs is presented. The discussion does not include details on the effect of pavement conditions on changes in travel time, safety-related or other implications of pavement conditions.