## 1 Introduction
In the Canadian railway network, [PERSON] et al. compared a dataset of track geometric failures which were related to track roughness and stiffness. This comparison, besides confirming the relationship between track stiffness and track failures, led to providing allowable values of roughness and stiffness, along with a risk diagram for the maintenance and repair of the railway track [26]. [PERSON] presented a novel method aimed at extracting new insights from collected data, sourced from geometric quality, dynamic stiffness, and ground-penetrating radar [27; 28]. [PERSON] et al. evaluated data collected by a track recording car from 1999 to 2016, focusing on investigating the geometric deterioration of the track within wavelengths of 1 to 25 m. They examined the correlation between changes in track stiffness and geometric irregularities to understand their impact on track maintenance and repair [29; 30]. [PERSON] et al. investigated the role of track vertical stiffness variations on the long-term track deterioration behavior through a series of computational experiments, leading to the presentation of a process to generate vertical stiffness representative of real-world track conditions [31]. Over the past few decades, numerous researchers have directed their attention towards condition monitoring and track maintenance [32; 33; 34; 35]. One of the appropriative approaches that can be employed to achieve this aim is data mining, which is used in railway engineering in various subjects, as mentioned by [PERSON] et al. [36], [PERSON] and [PERSON] [37], [PERSON] et al. [38], [PERSON] et al. [39], and [PERSON] et al. [40]. In the data mining approach, machine learning (ML) techniques are generally used to find data correlation. Several ML techniques (both supervised and unsupervised methods) are utilized to survey the maintenance of railway tracks. [PERSON] et al. tried to improve the efficiency of maintenance activities, especially railway component defectsand track geometry, utilizing deep reinforcement learning integrated with digital twin techniques [41]. [PERSON] et al. used random forest (RF) and recurrent neural network (RNN) techniques to predict railway track maintenance work and found that the accuracy of the RNN and RF were about 80% and 77%, respectively [42]. In another study, [PERSON] et al. used an unsupervised machine learning algorithm (K-means clustering) and a supervised learning method (support vector machine) to manage the railway earthwork and track circuit maintenance [43]. [PERSON] et al. used Pearson correlation analysis to predict the track gauge and realized that the prediction accuracy of the support vector machine (SVM) is higher in curved sections, while the artificial neural network (ANN) has better accuracy in straight sections [44]. [PERSON] et al. predicted the track geometry degradation by the ANN and SVR techniques [45]. [PERSON] et al. employed several unsupervised algorithms and supervised ML techniques (including cluster analysis, multiple linear regression, decision tree regression, random forest regression, and support vector regression) to analyze the influence of geocell installation on track geometry quality [46]. In order to predict the railway track geometry parameters, [PERSON] et al. developed deep ML models using 3D recurrent neural network-based techniques [47]. [PERSON] et al. studied the potential of two unsupervised ML techniques (including autoencoder and K-means clustering) to localize track defects based on geometry measurements [48]. [PERSON] et al. studied the use of data mining techniques in surveying railway track geometry parameters and track stiffness [49]. However, to the best of the authors' knowledge, the literature pertaining to the relationship between track stiffness and geometrical parameters has remained notably scarce. Therefore, there is a pressing need for further research and studies to formulate a coherent and systematic approach towards clarifying the connection between track stiffness and geometrical parameters.