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.

Filtering of Irrelevant Clashes Detected by BIM Software Using a Hybrid Method of Rule-Based Reasoning and Supervised Machine Learning

“…Machine learning algorithms use computational methods to predict results directly from historical data without relying on predetermined rules or equations on domain knowledge. Besides, the algorithms adaptively improve their performance as the number of training cases increases [13,15]. Despite its ease of identifying trends and patterns without human intervention, researchers often argued that machine learning requires a sufficiently large training dataset that allows a more complex model in order to obtain favorable results [7,15].…”

“…The other is the use of machine learning methods that train classifiers of machine learning through historical data to filter out irrelevant clashes [6]. However, researchers using machine learning on complex problems usually observe that a favorable classification performance often requires a larger training dataset that allows a more complex model with more features [13]. Nevertheless, identifying and labeling a large number of clashes requires tremendous and expensive manpower; therefore, the prediction accuracy of machine learning is often insufficient before a sufficiently large number of cases are collected [7].…”

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“…Moreover, the algorithms adaptively improve their performance because the number of training cases increases. 19,20 Despite the ease with which trends and patterns can be detected without human intervention, researchers often argue that machine learning requires a sufficiently large training dataset for a more complex model to produce favorable results. 1,11 Hu and Castro-Lacouture 12 verified the possibility of using machine learning to identify relevant and irrelevant clashes using historical data.…”

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