TY - GEN
T1 - On the use of the generalized lambda distribution and parametric bootstrap method in the prediction of maximum pit depths
T2 - 2010 8th International Pipeline Conference, IPC2010
AU - Alfonso, L.
AU - Caleyo, F.
AU - Hallen, J. M.
AU - Araujo, J.
PY - 2010
Y1 - 2010
N2 - 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 10 4 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.
AB - 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 10 4 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.
UR - http://www.scopus.com/inward/record.url?scp=80054035973&partnerID=8YFLogxK
U2 - 10.1115/IPC2010-31327
DO - 10.1115/IPC2010-31327
M3 - Contribución a la conferencia
AN - SCOPUS:80054035973
SN - 9780791844236
T3 - Proceedings of the Biennial International Pipeline Conference, IPC
SP - 537
EP - 543
BT - 2010 8th International Pipeline Conference, IPC2010
Y2 - 27 September 2010 through 1 October 2010
ER -