Probabilistic Aging Pipe Strength Estimation Using Multimodality Information Fusion

Accurate pipe material strength estimation is critical for the integrity and risk assessment of aging pipeline infrastructure systems. To predict the strength without interrupting the serviceability of the pipeline, inference methods are used through the relationship between the bulk yield tensile strength and surface material properties from nondestructive testing, such as chemical composition, microstructure images, and hardness testing. In order to make the best of information provided by multimodality surface measurements, Bayesian model averaging (BMA) method is used in this paper to integrate the information from various types of surface measurements for a more accurate bulk strength estimation. The models being considered are constructed by randomly combining the multimodality surface measurements and each case of linear combinations is included. The models considered are assessed by assigning different weights based on the posterior model probability. Markov Chain Monte Carlo sampling provides an effective way for numerically computing the marginal likelihoods, which are essential for obtaining the posterior model probabilities. To avoid the risk of overfitting, BMA is implemented to account for model uncertainty. The predictive performance of single model and BMA are compared by logarithmic scoring rule. The data collected from industry are used for demonstration and model predictive performance assessment. It is shown that the Bayesian model averaging approach can provide more reliable results in predicting the strength of the aging pipelines.

[1]  S. Sorooshian,et al.  Multi-model ensemble hydrologic prediction using Bayesian model averaging , 2007 .

[2]  Bruce L. Bramfitt,et al.  Structure/Property Relationships in Irons and Steels , 1998 .

[3]  S. Ravi.  Bayesian Logical Data Analysis for the Physical Sciences: a Comparative Approach with Mathematica® Support , 2007 .

[4]  Adrian E. Raftery,et al.  Bayesian Model Averaging: A Tutorial , 2016 .

[5]  M. Newton.  Approximate Bayesian-inference With the Weighted Likelihood Bootstrap , 1994 .

[6]  Rutger van Haasteren,et al.  Marginal Likelihood Calculation with MCMC Methods , 2014 .

[7]  Tiago M. Fragoso,et al.  Bayesian Model Averaging: A Systematic Review and Conceptual Classification , 2015, 1509.08864.

[8]  J. Kuha.  AIC and BIC , 2004 .

[9]  Siegfried Schmauder,et al.  A simple mechanical model to predict fracture and yield strengths of particulate two-phase materials , 1999 .

[10]  Gong Li,et al.  Application of Bayesian model averaging in modeling long-term wind speed distributions , 2010 .

[11]  Yongming Liu,et al.  Bayesian Network inference for probabilistic strength estimation of aging pipeline systems , 2018 .

[12]  Xuefei Guan,et al.  Model selection, updating, and averaging for probabilistic fatigue damage prognosis , 2011 .

[13]  W. Zucchini,et al.  A Mixture-Based Bayesian Model Averaging Method , 2016 .

[14]  Nils Lid Hjort,et al.  Model Selection and Model Averaging , 2001 .

[15]  S. Chib,et al.  Understanding the Metropolis-Hastings Algorithm , 1995 .

[16]  D. Madigan,et al.  Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window , 1994 .

[17]  A. Raftery,et al.  Using Bayesian Model Averaging to Calibrate Forecast Ensembles , 2005 .

[18]  S. Hashemi.  Strength–hardness statistical correlation in API X65 steel , 2011 .

[19]  Peter D. Hoff,et al.  A First Course in Bayesian Statistical Methods , 2009 .

[20]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[21]  Aki Vehtari,et al.  Comparison of Bayesian predictive methods for model selection , 2015, Stat. Comput..

[22]  Adrian E. Raftery,et al.  Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance , 1995 .

[23]  David R. Anderson,et al.  Multimodel Inference , 2004 .