Abstract
Fatigue and rutting are two primary failure mechanisms in asphalt pavements. The evaluations of fatigue and rutting performances are significantly uncertain due to large uncertainties involved with the traffic and pavement life parameters. Therefore, deterministically it is inadequate to predict when an in-service pavement would fail. Thus, the deterministic failure time which is known as design life (yr) of pavement becomes random in nature. Reliability analysis of such time (t) dependent random variable is the survival analysis of the structure. This paper presents the survival analysis of fatigue and rutting failures in asphalt pavement structures. It is observed that the survival of pavements with time can be obtained using the bathtub concept that contains a constant failure rate period and an increasing failure rate period. The survival function (S(t)), probability density function (pdf), and probability distribution function (PDF) of failure time parameter are derived using bathtub analysis. It is seen that the distribution of failure time follows three parametric Weibull distributions. This paper also works out to find the most reliable life (YrR) of pavement sections corresponding to any reliability level of survivability.
Highlights
Fatigue and rutting are considered as primary modes of failure in asphalt pavements
This paper presents the survival analysis of asphalt pavements for fatigue and rutting failures
It may be concluded that the survival of pavement structures can well be represented by the three parametric (k, λ, and γ) Weibull distributions. k, λ, and γ parameters are derived through simulation
Summary
Fatigue and rutting are considered as primary modes of failure in asphalt pavements. In mechanistic-empirical (M-E) design of pavements, a design solution is obtained so that the estimated pavement life (fatigue and rutting lives) is not less than the total predicted traffic repetitions during its design period. Both pavement life (N) and traffic (T) parameters show significant uncertainty due to large variabilities associated with their input parameters. That is how the time that it fails becomes a random variable which deals with mortality or failure of the system with time. The randomness of the failure time (yr) shall follow certain probability distribution
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