Daba S Gedafa

Perpetual pavement temperature prediction model

Structural capacities of flexible pavements are determined from surface deflection measurements. These deflections must be corrected to a standard load and/or a reference pavement temperature. A number of models are available to predict pavement temperature, but they may not be applicable to perpetual (thicker) asphalt pavements. Mid-depth pavement temperature was measured in six sessions on four perpetual pavement sections in Kansas. Data from five sessions were used to develop the prediction model based on four independent variables. Data from the last session were used to validate it. Predicted mid-depth pavement temperatures from the new model and three other models were compared with the measured mid-depth pavement temperature. Sensitivity of the model to changes in all independent variables was also investigated. The effect of mid-depth pavement temperature on the centre deflection of the falling weight deflectometer was also studied. The prediction model developed in this study yields mid-depth pavement temperature that is closest to the measured mid-depth temperature. It also results in lowest bias in terms of centre deflection. Predicted mid-depth pavement temperature is most sensitive to the time of day when measurements are made and least sensitive to the layer mid-depth thickness.

Effect of aging on dynamic modulus and fatigue life of superpave mixes

In the Mechanistic–Empirical Pavement Design Guide (MEPDG), prediction of flexible pavement response and performance needs an input of dynamic modulus of hot-mix asphalt (HMA) at all three levels of hierarchical inputs. This study investigated the effect of aging on the dynamic modulus and fatigue life of Superpave mixes. Five newly built Superpave pavements for local calibration of MEPDG by the Kansas Department of Transportation (KDOT) were selected as test sections in this study. Volumetric properties of the mixes have been obtained from the mix design database of KDOT. Asphalt concrete (AC) cores were obtained at 30.5 m interval over a 305 m section from all calibration sites and tested in the laboratory for dynamic Moduli. Dynamic modulus was predicted with the Witczak equation. The results show that there is an increase in predicted modulus with an increase in age and frequency of loading. The difference between predicted modulus initially and after 5 years is greater than the difference between the predicted moduli after 5 and 10 years. This shows that the aging process slows down with time. The rate of increase in predicted modulus over the years is higher at 21 o C than at 35 o C. Increase in dynamic modulus with aging results in decrease in tensile strain, but the rate of increase in the predicted number of load repetitions is greater than the allowable number of load repetitions. As a result, fatigue life decreases with time. Laboratory dynamic modulus was comparable for all US routes at 4°C. The variation increased as the test temperature increased. Witczak equation underestimated the dynamic modulus at low temperature and overestimated at high temperature when compared with the laboratory modulus.

Network-level flexible pavement structural evaluation

The Kansas Department of Transportation has a comprehensive pavement management system known as network optimisation system (NOS). Annual condition surveys are conducted for NOS. Currently, the structural number (SN) of flexible pavements is computed using the American association of state highway and transportation officials equation based on the centre and fifth sensor deflections of a falling weight deflectometer (FWD). However, a rolling wheel deflectometer (RWD) can be used to collect deflection data at the network-level. This study was conducted to see whether the SN of flexible pavements can be obtained from this RWD deflection and NOS condition survey results. In this study, FWD deflection data, collected from 1998 to 2006, were analysed. Multiple regression analysis was done. The results showed that there is a negative relationship between SN and centre deflection. Equations can be used to calculate SN based on FWD (or RWD) centre deflections and network-level condition survey results. The SN is more sensitive to centre deflection than the total pavement thickness.

Effects of Binder and Mix Properties on the Mechanistic Responses of Fatigue Cracking APT Sections

One of the major benefits of Accelerated Pavement Testing (APT) as a research tool is that the performance of pavement materials and structures can be evaluated at a reduced cost and in a short period of time. The Civil Infrastructure Systems Laboratory (CISL) for APT at Kansas State University was established in 1997. Six fatigue pavement sections that had 100 mm thick hot mix asphalt layer were constructed for APT in CISL. The sections had the same base and subgrade materials. The sections were instrumented using strain gauges and pressure cells to measure pavement responses under APT loading. They were loaded with a 100 kN single axle load at 20 °C. The main objective of this paper is to investigate the effects of binder content, binder grade, and mixture nominal maximum aggregate size (NMAS) on the mechanistic responses from the experimental sections. Stress, strains, deflections, and profile data were collected periodically. Normalized falling weight deflectometer (FWD) deflection data were used to back-calculate moduli using the EVERCALC software. Measured pavement temperatures were used to adjust back-calculated moduli to a temperature of 20 °C. The stiffer the binder, the lower the longitudinal strain, permanent deformation, and roughness, but the higher the transverse strain, stress on the top of subgrade in general, and back-calculated moduli. Higher binder content results in a higher longitudinal strain, transverse strain, stress on top of subgrade, permanent deformation, rut depth, and roughness. The larger the NMAS, the higher the transverse strain, back-calculated moduli, permanent deformation, and roughness, but the lower the longitudinal strain, stress on the top of subgrade, and rut depth.

Impacts of Alternative Yield Sign Placement on Pedestrian Safety

Although pedestrian crashes account for only 1% of reported motor vehicle crashes in the United States, pedestrian crashes account for 14% of fatal crashes. Pedestrians are at a higher risk of fatalities than are automobile operators when crashes per traveling distance are considered. Because of the high societal costs of pedestrian crashes, measures designed to reduce their frequency and severity should receive high priority. Additional countermeasures such as crosswalks and signs need to be devised to decrease pedestrian–automobile collisions. The main objective of this study was to investigate the effects of yield signs on yielding for pedestrians and on traffic speed and the implication of these effects for pedestrian safety. The city of Grand Forks, North Dakota, and the University of North Dakota campus were used as test sections. Data were collected on yielding for pedestrians and on traffic speed without and with a yield sign. The two-proportion z-test and an independent t-test were used to investigate the effect of the sign on yielding for pedestrians and on traffic speed, respectively. Placing a yield sign at the crosswalk is the most effective way of increasing yielding for pedestrians. The percentage of drivers not yielding for pedestrians and of pedestrian–driver conflicts are significantly lower in the presence of the sign. The presence of the sign results in lower average traffic speed, which implies that the risk to pedestrians will be lower if there is a crash.

Prediction of Asphalt Pavement Temperature

Structural capacities of flexible pavements are determined from surface deflection measurements. These deflections must be corrected to a standard load and/or a reference pavement temperature. A number of models are available to predict pavement temperature. In this study, measured pavement temperatures were compared with the predicted (calculated) temperatures. The effect of temperature correction on the deflection under the load (center) was also investigated. The results show that there is a significant difference between calculated and measured temperatures at the pavement mid-depth for most models. Only one model yielded a mid-depth pavement temperature close to the measured value. The results also showed that the effect of temperature-correction method at times can be very pronounced. The most accurate model for correcting pavement surface deflections for temperature was identified.