## 1 Introduction
The inherent ability of ML algorithms to handle large volumes of data and their potential in modeling multiparametric cases [50; 51; 52; 53; 54] have led to their engagement as the main modeling tools for the estimation of FUP in infilled RC buildings. This has led to different research efforts over the last several years, a brief review of which is presented below. [PERSON] [55] developed relationships for calculating the fundamental period of masonry infilled buildings using a multiple Linear Regression model. To investigate the structural parameters that most influence the value of the fundamental period, he performed sensitivity analyses using a feed-forward artificial neural network (ANN) with one hidden layer. In this way, he derived models that correlate the FUP with the height, the percentage of RC walls, and the percentage of masonry infills of the buildings. The employed dataset consisted of 189 data points generated using a 3D finite element approach. On the other hand, [PERSON] developed a much more extended dataset [56] consisting of 4026 data points, which was suitable for training ML algorithms to develop simplified equations for estimating the fundamental period of RCB_MI. This dataset has been used by other researchers, and is briefly described in Section 2.1 of the present paper. [PERSON] et al. [57] used the above dataset [56] to train feed-forward ANNs with one or two hidden layers. The developed model offered values for the fundamental period of infilled RC buildings. These values have been compared with the ones derived from the equations suggested by the codes with the comparisons showing that the correlation between the FUP and the structural parameters (e.g., the buildings' height, the number and the length of their openings, and the strength and percentage of masonry infills) leads to more realistic results than correlation of the fundamental period with the height of the building, as proposed by most available codes. In a later study, [PERSON] and [PERSON] [58] used the aforementioned dataset [56] to train feed-forward ANNs which were optimized via an artificial bee colony optimization algorithm. Using this methodology, they obtained high correlation coefficients between the considered structural parameters (building height, number and length of openings, strength and percentage of masonry infills) and the fundamental period. Similar to [PERSON] et al. [56; 58], [PERSON] et al. [59] employed the same dataset [56] to train feed-forward ANNs with a different architecture consisting of two or three hidden layers, which can be considered too complicated for a general model. They extended their work by applying the _M5 Rules_ algorithm, which belongs to the group of decision tree learners. They finally arrived at a set of simplified equations for computing the fundamental period as a function of the number of stories, the stiffness of the infill walls, and the frames' span length. They derived separate equations for different masonry infill ratios (opening ratio from 0% for bare frames to 100% for fully infilled RC frames) and a single equation for all masonry infill rates (opening ratios). Deviating from the work of [PERSON] et al. [56; 58] and [PERSON] et al. [59], in which ANNs were employed, [PERSON] et al. [60] applied a series of ML algorithms (linear regression, ridge regression, \(k\)-nearest neighbors, support vector regression, random forest, decision tree, extreme gradient boosting, and adaptive boosting) using the same training dataset created by [PERSON] [56]. In their study, they reached the main conclusion that the XGBoost algorithm is the most effective algorithm for predicting the value of the FUP. Moreover, they concluded that the number of stories is the most important structural parameter. This conclusion is consistent with the choice of most codes to relate the FUP to the height of RC buildings. Based on the same Asteris dataset [56], [PERSON] and [PERSON] [61] experimented with the use of Fuzzy Logic, which combines rigorous mathematical modeling with the experience of experts.