Fatigue failure is regarded as one of the most common failures in the road pavement and necessitates spending huge cost annually to maintain the road. Asphalt binder modification and asphalt mixture reinforcement are among the commonly used methods to increase the pavement resistance to a failure caused by fatigue. By proposing a modified-reinforced composite hot mix asphalt (MRC-HMA), the present study aimed to examine the fatigue life of this mixture with one of the most traditional methods (i.e., four-point bending beam fatigue test) and compare it at constant strain conditions and the strain levels of 500, 700, and 900
μ
ε
and a temperature of
20
±
0.8
°
C
to that of the other three specimens, including control specimens, geogrid-reinforced (GR-HMA) specimens, and nanosilica-modified (NSM-HMA) specimens with 5% nanosilica. In all experiments, the condition to reach the failure stage was assumed equivalent to a 50% reduction in the stiffness coefficient in each load repetition, and the load was applied semisinusoidal at a frequency of 10 Hz without rest. The results showed that the MRC-HMA mixture improved the fatigue life at the strain level of 500
μ
s
by about 701, 172.5, and 156.4% compared to the control, NSM-HMA, and GR-HMA specimens, respectively. Based on the results, the use of GR-HMA specimens has almost the same results as NSM-HMA ones, but the use of the MRC-HMA mixture can significantly increase the fatigue life of MRC-HMA in all three levels of strain compared to all specimens studied in the present study. Thus, the introduced mixture can be a proper choice for pavements with heavy or light (with a large amount) traffic loads, which usually have a vast adverse effect on the fatigue behaviour of asphalt mixtures.
This study investigates the influence of segmentation type on the goodness-of-fit and the significance of crash prediction accuracy of non-linear crash prediction models. The models are developed regarding runoff road accidents, pavement distresses, and roughness. The nonlinear negative binomial regression models are employed to establish a relationship between runoff road accident frequency and other independent variables. Results show that homogeneous segmentation have fewer errors and is preferred against fixed-length segmentation, whereas the model development of fixed-length segments defines enhanced goodness-of-fit values. The comparison of the suggested nonlinear models confirm that it is better to execute the segmentation process for studying the crash prediction models of some specific explanatory variables, like pavement characteristics variables.
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