Accelerated Mix Testing and Statistical Analysis

2.4.1. Overview

Researchers had named the accelerated tests as the elephant tests, which also include killer tests, design limit tests, design margin tests, design qualification tests, torture tests, and shake and bake (Nelson 1990). If the product survives one of these tests, the responsible

engineers have more faith in it. Otherwise, the engineers will redesign or improve the quality to overcome the cause of failure. In this type of test, the specimen may be subjected to a single, severe level of a stress (temperature). It may be subjected to a number of stresses – either simultaneously or sequentially. A good elephant means one that produces the same failures and in the same proportions that will occur in service. Elephant tests provide only qualitative information on whether a product is good or bad (Nelson 1990).

Overstress testing consists of running a product at higher than normal levels of some accelerating stress(es) to shorten product life or to degrade the product faster. Typical

accelerating stresses on asphalt mixtures can be temperature, mechanical loads, or traffic loads. Accelerated degradation testing involves overstress testing. Instead of life, product performance is observed as it degrades over time. A model for performance degradation is fitted to such performance data and used to extrapolate performance and time of failure. Thus failure and life can be predicted before any specimens fails (Nelson 1990).

Accelerated degradation is concerned with models and data analyses for degradation of the product over time at overstress and design conditions. Performance degradation data can be analyzed before reaching failure criteria. It accelerates the test by extrapolating performance degradation to estimation time to reach failure criteria. Performance degradation can yield better insight into the degradation process and how to improve it (Nelson 1990). Some of the

assumptions of degradation models are as follows: - Degradation is not reversible.

2.4.2 Weibull Distribution

Weibull distribution is a very flexible model for survival analysis. As mentioned earlier, the survival function for Weibull distribution is given by Sx(x) = exp (- λ xα_{). The hazard rate is} expressed as hx _{(x) = λαx}α-1_{. When the log transform of time is taken, the univariate survival} function for Y = ln X can be expressed as in Equation (2.1).

Sy (y) = exp(-λeαy_{) (2.1)}

If we redefine the parameters as λ = exp (-μ/σ) and σ = 1/α, then Y follows the form of a log linear model as in Equation (2.2).

Y = ln X = μ + σW (2.2)

where W is the extreme value distribution with probability density function as given in Equation (2.3),

fw (w) = exp (w-ew_{); (2.3)}

and survival function as given in Equation (2.4),

Sw (w) = exp (-ew_{). (2.4)}

2.4.3 Survival Analysis

Survival analysis generally refers to statistical methods for analyzing survival or time-to- event data. The data can be generated from diverse fields such as medicine, biology, public health, epidemiology, engineering, economics, and demography (Klein and Moeschberger 2003). The analysis involves data which get truncated. For example, let X be the time until some

specified event. This event may be death, development of some disease, equipment breakdown, conception, cessation of smoking, etc. X is a non-negative random variable. Four functions are used to characterize the distribution of X, namely the survival function, which is the probability of survival beyond time x; the hazard rate (function), which is the chance an individual of age x experiences the event in the next instant; the probability density (or probability mass) function, which is the unconditional probability of the event occurring at time x; and the mean residual probability life at time x, which is the mean time to the event of interest, given that the event has not occurred at x. If any of these parameters is known, then the other three can be uniquely

2.4.3.1 Survival Function

The survival function is defined as S (x) = Pr (X>x). When X is a continuous random variable, the survival function is the complement of the cumulative distribution function, that is,

S (x) = 1 - F(x), where F(x) = Pr (X≤ x). The survival function is the integral of the probability

density function, f(x), that is, the survival function for the Weibull distribution is S(x) = exp (- λ xα_{), λ >0, α > 0. Figure 2.9 shows survival curves with a common median of 6.93, but for various} α and λ values. These functions are monotone, non-increasing with values equal to one at zero, and zero as the time approaches infinity.

2.4.3.2. Right-Censored Data

There are two types of right censoring: Type I and Type II. Type I censoring is where the event is observed only if it occurs prior to some pre-specified time. For example, a typical animal study or clinical trial starts with a fixed number of animals or patients to which a treatment (or treatments) is applied (Klein and Moeschberger 2003). Because of time or cost considerations, the investigator will terminate the study or report the results before all subjects realize their events. The second type of right censoring is Type II in which the study continues until the failure of the first r individuals out of n, where r is some predetermined integer. The HWTD test data can be considered as Type I censored since the test is terminated when 20,000 passes or a rut depth of 20 mm (0.8 in.) is reached.

2.4.4 Cubic Model

In an attempt to better capture the curvature of the degradation curves of log (rut) versus log (loadings), in an HWTD test, third-degree polynomials to the degradation paths, called the cubic model, can be fitted.

The following form of equation denotes the cubic model, where Y denotes the log- transformed, rut-depth values and L represents the log-transformed number of load repetitions as shown in Equation (2.5):

Y = β0 + β1 L + β2 L2 _{+ β3 L}3 _{+ e}

ij (2.5)

Parameters β0, β1, β2, and β3 are coefficients for the intercept, linear, quadratic, and cubic terms, respectively, and eij is the error term. This cubic model was considered in this study because of its superior residual behavior. Model choice and comparison can be done using the Akaike information criterion (AIC) and the Bayesian (Schwarz’) information criterion (BIC). Both criteria utilize the log likelihood of the data, yet punish for the number of parameters in accordance with the parsimony principle. Smaller values of these criteria indicate better models.

CHAPTER 3 - OBJECTIVE 1: DEVELOPMENT OF CRITERIA