## 1 Introduction
Machine learning predictive analytics for structural concrete engineering purposes is a broad subject, but we can divide scientific trends into two main groups that focus on material properties of concrete and structural health monitoring, respectively. The first research trend mentioned by us concerns the prediction of concrete compressive strength based on a machine learning analysis of concrete mix recipes and strength tests. This trend was started in 1998 by [PERSON] et al. [14]. They tries to predict the compressive strength of high-performance concrete by using linear regression and an ANN. In their research, they collected a large amount of data for analysis, but did not take into account the specificity of concrete maturation. In their database, they had concrete test samples that had not yet reached their full compressive strength, which in our opinion, may have led to corrupted results. [PERSON], [15] presented a method in which he improved the accuracy of prediction by introducing the weighting of input neurons and using a parameter condensation technique for the selection of the input neurons number. His neural network had a modular structure built from five networks. The author claimed that the methods he used allowed for optimizing ANN performance well. [PERSON] et al. [16] approached the topic differently and proposed a neural expert system. They used this method to predict the compressive strength of high-performance concrete. The system they proposed used a multilayered neural network architecture, which was trained with generalized backpropagation for interval training patterns. The authors argued that the solution allowed for learning from example inferences. However, their method allowed for the learning of irrelevant input variables that could distort output. Another element that in our opinion is debatable is the fact that the input variables had different metrics. Apart from the composition of the concrete mix, ANNs analyze variables such as curing time. We think that the input parameters adopted for the analysis could lead to the misrepresentation of concrete compressive strength results. A similar topic is described in the work of [PERSON] et al. [17], who focused entirely on the practical application of neural expert systems. An application of DNN architecture to predict the compressive strength of concrete was considered by [PERSON] et al. [18]. They studied a dataset of recycled concrete recipes and used a network with five input variables and Softmax regression to find the best prediction model. Referring to the analysis itself, the authors used so-called deep features. This means that the DNN was trained not directly on the data but on the input variable ratios. A similar approach was presented by [PERSON] and [PERSON] [19], where in their analysis, they used feature scaling. [PERSON] et al. [18] claimed that the use of their method allowed for the achievement of a higher generalization ability and higher efficiency and precision in comparison to the ANN. However, they used convolution networks that were computationally expensive, which is why their database was small, with 74 records. Such a small dataset did not allow their methods to be used on a larger scale and may have resulted in underfitting, which means that the network did not fit the data correctly to such an extent that it could limit the efficiency. On the contrary, [PERSON] et al. [20] compared the ANN and DNN approaches, presenting a similar degree of precision. The second trend is focused on structural health monitoring, which consists of constant observation of the structure to ensure its safe exploitation and reliability over time. Such a monitoring system concerns especially public facilities and elements of critical infrastructure. One of the monitoring techniques that acquires data for machine learning analysis is a dynamic response measurement. It involves monitoring continuous intervals of time damage-sensitive features, and it is mainly used in dams, long-span bridges, or tall buildings.