Spatial variability of soil and localised underground corrosion

Underground corrosion is a problem that often goes unnoticed, but has a significant economic impact. Corrosion of buried metallic infrastructure such as pipelines is highly variable, and can be influenced by many complex factors. In addition to phenomena such as stray currents and differential aeration that lead to rapid levels of corrosion, we have shown that the spatial variability, or heterogeneity of soil itself can lead to differential conditions that evolve into rapid localised corrosion.  

In a previous blog post I described how to create random field realisations using Python for various soil parameters with given statistical properties. The idea was to export these realisations as point clouds into numerical modelling software to simulate various processes under heterogenous soil conditions. When we simulated corrosion of buried metal in heterogenous soil using this method, we saw that the spatial variations in soil resulted in localised corrosion defects termed "corrosion patches" that evolve with time until failure occurs. In this context, failure is defined as the point when when the corrosion patch depth exceeds the pipe wall thickness. 

The emergence of localised corrosion from numerical modelling of spatially variable soil, similar to actual corrosion patches observed in the field 

However, running such simulations is computationally intensive, especially if multiple realisations over longer pipe lengths are needed to be simulated. To overcome this problem, we used Artificial Neural Networks (ANN). ANNs are a machine learning method that use a system of simulated neurons to identify and infer patterns from data without being given explicit instructions. It is similar to how neurons in our brains work, with a neuron having several inputs and is activated (similar to a neuron firing in the brain) when the weighted sum of the inputs exceed a certain threshold. An organised set of such neurons can "learn" to map input data to outputs by adjusting the weights and biases used for activation.   

We identified two of the most influential soil properties that influence corrosion: degree of saturation and saturated electrical resistivity. We then trained an ANN using input data from random field realisations of these two variables and numerical model outputs of corrosion parameters for the same realisations. Through this method we developed an ANN that was capable of predicting the level of corrosion over time for given values of these input variables. We were now able to use this trained neural network to simulate corrosion over much longer pipe segments for multiple realisations with lower computational effort and time. We outlined this method and simulation results in a recently published paper: 

https://link.springer.com/article/10.1007%2Fs11440-021-01385-5

In this paper we show how the soil degree of saturation and resistivity influence the corrosion patch configurations. We also describe a linear approximation that can be adopted for assessing information along buried pipelines, and note that the workflow and methods used in this work can be adopted together with underground sensing methods for non-destructive pipeline condition assessment. 

Using Artificial Neural Networks together with numerical modelling to predict pipeline corrosion in spatially variable soil