## 1 Introduction  
During earthquake events, it is common to observe aftershocks following a mainshock. Moderate-to-strong aftershocks may lead to additional structural damage and even the collapse of buildings that sustained damage from the mainshock. Thus, the seismic performance of structural systems subjected to successive ground motions has received increasing attention in recent years. The recent disaster that occurred on March 2021 in the Tyrnavos-Elassona region, Thessally of Greece due to a pair of compatible magnitude (Mw = 6.3, Mw = 6.1) [1] shallow earthquakes with more than 1800 damaged or non-servicable buildings demonstrated the necessity of predicting the damage potential caused by mainshock-aftershock sequences in order to assess the seismic risk. It should be noted that the final, accumulated damage includes the initial damage caused by the major earthquake and the incremental damage caused by the following seismic sequence. The effect of successive seismic events on the structural performance has been thoroughly examined by many researchers [2; 3; 4; 5; 6]. Specifically, [PERSON] et al. [7] studied the influence of repeated shocks on the response of nonlinear single degree of freedom (SDOF) systems using different hysteretic models. [PERSON] and [PERSON] [8] conducted an exhausting parametric study on SDOF systems and proposed an empirical relation to calculate the inelastic displacement ratio under repeated earthquakes. [PERSON] and [PERSON] [9]examined the nonlinear behaviour of reinforced concrete (RC) frames subjected to multiple shocks considering a set of eight frames that varied both at height regularity and dimensioning practice. [PERSON] and [PERSON] [10] studied the accumulation of damage and ductility demands due to seismic sequence on three dimensional RC structures. [PERSON] and [PERSON] [11] studied the impact of different aspects, such as earthquake direction, aftershock polarity and the influence of the vertical component, on the nonlinear response of RC frames under successive earthquakes. Additionally, more recently, [PERSON] et al. [12] and [PERSON] et al. [13] investigated the correlation between aftershock-related intensity measures (IMs) and final structural damage indices. Additionally, multiple researchers [14; 15; 16; 17] have evaluated the fragility of buildings and infrastructures against seismic sequences, in the past. In recent years advanced machine learning algorithms (MLAs), such as artificial neural networks (ANNs), have been successfully applied to many areas of system modelling. Their success is based on the thorough processing of data that captures the behaviour of a system. By detecting patterns in the collected data, valuable information can be extracted and predictions can be made that automate the decision-making process. That fact makes machine learning (ML) an advanced tool in modern engineering modelling. From this point of view, the utilization of MLAs in earthquake engineering has been increasing year by year, examining mainly the capability of such models in predicting seismic structural damage [18; 19; 20]. Among others, [PERSON] and [PERSON] [21] investigated the efficiency of ANNs on the prediction of seismic damage on numerous RC frames, while [PERSON] et al. [22] also examined regular RC structures and, by incorporating fuzzy layers in ANN configuration (architecture). Subsequently, [PERSON] and [PERSON] [23] used feature selection methods in a dataset of 3-dimensional RC buildings to identify the more damage-correlated set of seismic IMs. More recently, the same authors [24] examined the effectiveness of ANNs on the damage prediction of non-regular at-height structures. Applications of recurrent neural networks (RNNs) on earthquake engineering are presented by [PERSON] et al. [25] and [PERSON] and [PERSON] [26]. Furthermore, [PERSON] et al. [27] developed a long-short term memory (LSTM) network to predict structural responses. For the same purpose, convolutional neural networks (CNNs) have been applied by [PERSON] et al. [28] and [PERSON] et al.