Kernel-specific Gaussian process for predicting pipe wall thickness maps

Data organised in 2.5D such as elevation and thickness maps has been extensively studied in the fields of robotics and geostatistics. These maps are typically a probabilistic 2D grid that stores an estimated value (height or thickness) for each cell. Modelling the spatial dependencies and making inference on new grid locations is a common task that has been addressed using Gaussian random fields. However, inference faraway from the training areas results quite uncertain, therefore not informative enough for some applications. The objective of this research is to model the status of a pipeline based on limited and sparse local assessments, predicting the likely condition on pipes that have not been inspected. A customised kernel for Gaussian Processes (GP) is proposed to capture the spatial correlation of the pipe wall thickness data. An estimate of the likely condition of non-inspected pipes is achieved by concretising GP to a multivariate Gaussian distribution and generating realisations from the distribution. The performance of this approach is evaluated on various thickness maps from the same pipeline, where data have been obtained by measuring the actual remaining wall thickness. The output of this work aims to serve as the input of a structural analysis for failure risk estimation.