On the Use of the Generalized Lambda Distribution and Parametric Bootstrap Method in the Prediction of Maximum Pit Depths: Comparison With Experimental Pipeline Data

The approach proposed by Najjar and coworkers for the prediction of maximum pit depth is applied and validated through direct comparison with real pipeline steel pitting corrosion data. This methodology combines the Generalized Lambda Distribution (GLD) and the Bootstrap Method (BM) in order to estimate both the maximum pit depth and confidence intervals associated with the estimation. Samples are drawn from real-life pitting corrosion data and the GLD is used to obtain modeled pit depth distributions emulating the experimental ones. In order to estimate the maximum pit depth over an N-times larger area, simulated distributions, N-times larger than the experimental ones, are generated 10⁴ times. The deepest pit depth is extracted from each simulated bootstrap sample to obtain a dataset of 10⁴ extreme pit-depth values. An estimate of the maximum pit depth for the N-times larger surface can be obtained from this dataset by calculating the average of the 10⁴ extreme values. The uncertainty in the estimation is derived from the 95% confidence interval of the bootstrap estimate. In this report, the results of the application of the GLD-BM framework are compared with extreme pit depth values observed in real pitting corrosion data. The agreement between the estimated and actual maximum pit depths points to the applicability of the GLD-BM as an alternative in estimating the maximum pit depth when only a small number of samples are available. The main advantage of the combined methodology over the Gumbel method is its great simplicity, since fast and reliable estimations can be made with at least only two experimental samples.