## 1 Introduction  
Data-driven methodologies are attracting great interest in all engineering fields for modeling natural phenomena and predicting physical quantities. A growing number of researchers have recently advanced the use of computational methods with the aim of managing and processing large amounts of data [1]. Recent studies have proved that data-driven methods can be particularly beneficial in the development of capacity equations, allowing us to preserve the physical meaning of the resisting mechanism and to extract useful information about the examined phenomenon at the same time. Over the past few decades, artificial intelligence (AI) methods have been utilized to extract models from observed datasets, particularly in areas such as real-time analysis, large-scale data interpretation, and optimization tasks. Among the various numerical approaches, Artificial Neural Networks (ANNs) and Evolutionary Algorithms (EAs) stand out as particularly effective for data analytics applications. ANNs work similarly to the human brain's neural network and are formed by hidden units interconnected by weights that can be updated according to quality parameters, which evaluate the proximity between the critical response and the one obtained [2]. The networks are organized in layers; the first layer takes an input, the last layer produces outputs while the middle layers have no connection with the external world and for this reason are called hidden layers. Within this structure, ANNs consist of a backpropagation feed-forward neural network. Although ANNs represent accurate predictors, the technique suffers from several drawbacks, including its black box nature, that is its incapability of returning explicit formulae or equations. On the contrary, in order to find the best mathematical solution within a certain search space, EAs use an analogy with [PERSON]'s theory, according to which the driving force behind natural evolution is the capability of a population of individuals to reproduce and deliver new populations of individuals, which are better fit to their environment [3]. One of the important features of evolutionary computation is that a large population of solutions is considered at once and then only the better solutions are allowed to \"have children\", while the worse ones are quickly eliminated. In this way the search is performed by evolving solutions, based on recombination and/or mutation and characterized by higher quality [4; 5]. After a number of generations, the best selected models are substantially better than their initial long-dead ancestors. Among the various AI methodologies, this paper explores the capabilities of the Evolutionary Polynomial Regression (EPR) modeling strategy. EPR is a data-driven hybrid approach that merges the advantages of Genetic Algorithms (GAs) with traditional numerical regression techniques [6], aiming at simple and interpretable mathematical expressions. In this method, GAs are employed to identify the structure and exponents of symbolic formulas, while Ordinary Least Squares (OLSs) is used to estimate the model parameters. The EPR approach generates a set of symbolic expressions that best fit the training data and remain accessible and understandable to practitioners [7]. Each resulting model can be further validated by assessing its consistency with physical mechanics, thus generalizing its practical application beyond the original dataset. The EPR technique has been successfully applied in the field of civil engineering [7; 8; 9; 10; 11; 12; 13]. Current EPR techniques include both conventional and robust regression approaches to estimate the model parameters. The first approach involves minimizing the Root-Mean-Square Error (RMSE) between observed and predicted data by adopting a linear function, or alternatively, maximizing the likelihood function [14]. However, the OLS method yields consistent results only when certain assumptions are met--specifically, when the regressors are exogenous and the errors are normally distributed and homoscedastic.