## 1 Introduction  
Refineries are oil and gas processing plants that produce fuels, oils, lubricants, asphalt, and other crude oil-based raw materials. Refineries often occupy large areas of land where there are hundreds of machine installations that are located on concrete foundations. Failures of concrete foundations for industrial machines are highly problematic and expensive to repair. Repairing a damaged foundation may require even a temporary shutdown of the machinery, which may result in direct and indirect losses that are challenging to estimate. Machine foundations differ significantly from the foundations of buildings and engineering structures. These foundations are specially designed for transferring static and dynamic loads from machines in operation to the ground and are mounted separately from the existing structure [1]. Separating these foundations from the structure of the building in which they are located is of vital importance and is performed primarily to protect the construction of the building against the effects of dynamic loads. These foundations are most often made of reinforced concrete. Machine foundations are designed for the specific machine that is mounted on it, which makes each foundation unique. The problem of failures that occur in concrete machine foundations is quite challenging to explore and find one universal answer. First and foremost, because the machines mounted on the foundations have diverse functions, they support various stages of technological processes and perform and operate in different ways [2]. In our paper, we would like to present applied predictive analytics in civil engineering based on machine learning. In the study, we try to answer the question of what affects the failure occurrence rate of concrete machine foundations for one of the refineries with which we cooperate. In addition, we would like to show a solution that we adopted to solve a classification problem, which can be used in further studies on this subject. The topic of predictive analytics in civil engineering is present in scientific discourse. However, analysis regarding the failure occurrence rate of concrete foundations for machines in the refining industry has not been addressed in the literature. Therefore the subject is new and pioneering. The analysis we prepared involved the use of machine learning techniques. First, we gathered a comprehensive database of different parameters, from the geometry of the foundations to the operational parameters of the machines. We used this vast-ranging database, looking for answers about whether some of these parameters influence the failure occurrence rate of concrete machine foundations and if so to what extent. As we mentioned, to solve the considered problem, we decided to use machine learning techniques, because in our opinion, they cope better with multilevel dependencies between many parameters than well-established statistical methods do. We want to point out that the adopted approach is experimental and that the conclusions drawn are not final and might be revised in further studies. The subject of concrete foundations for industrial machines is a separate field within concrete structures that requires careful consideration. The design practice of machine foundations requires design assumptions, which consist of type, rotation speed, power, and weight [3]. However, the essential matter to consider is the type of machine that we plan to mount on the foundation. Machines are characterized by different work parameters that can influence the supporting structure in various ways. According to European norm PN-EN 60034-1:2009--\"Rotating Electrical Machines--Part 1: Nominal data and parameters\" [4], machine operation can be characterized as 1 of 10 modes. The most popular material from which the foundations for machines are made is concrete. To secure the foundation and improve its reliability parameters, active and passive vibration isolations are used.