Assessment of Failure Occurrence Rate for Concrete Machine Foundations Used in Gas and Oil Industry by Machine Learning

Abstract

Concrete machine foundations are structures that transfer loads from machines in operation to the ground. The design of such foundations requires a careful analysis of the static and dynamic effects caused by machine exploitation. There are also other substantial differences between ordinary concrete foundations and machine foundations, of which the main one is that machine foundations are separated from the building structure. Appropriate quality and the preservation of operational parameters of machine foundations are essential, especially in the gas and oil industry, where every disruption in the technological process is costly. First and foremost, there are direct repair costs from damage to foundations, but there are also indirect costs associated with blockages of the production process. Foundation repairs can temporarily shut down a given part of the refining process from operation. Thanks to cooperation from our partner, we obtained data from 510 concrete machine foundations from a refinery. Our database included many parameters, such as concrete cover thickness, machine gravity center distortion, the angular frequency of vertical self-excited vibrations, the angular frequency of horizontal self-excited vibrations, amplitudes of oscillation, foundation area, foundation volume, and information on occurring failures. Concrete machine foundation failure is not yet fully understood. In our study, we assessed what affects the failure occurrence rate of concrete machine foundations and to what extent. We wanted to find out whether there are correlations between the foundation failure occurrence rate and the mentioned parameters. To achieve this goal, we utilized state-of-the-art machine learning techniques.