## 1 Introduction  
Composite materials are very widely employed in the construction industry, due to their efficient structural performance (high strength and ductility) and reasonable cost [1]. In this context, concrete-filled steel square hollow section (CFSS) members utilize the advantages of both steel and concrete. They comprise a steel hollow section of square shape, filled with plain concrete. On the one hand, they may be used as columns and beam-columns in high-rise buildings [2; 3]. On the other hand, CFSS members could serve as beams in low-rise infrastructures (i.e., industrial buildings) [4]. Indeed, CFSS members have become increasingly popular in structural systems, due to their favorable structural performance characteristics, including high strength and ductility, and easy beam-to-column connection manufacturing process [5; 6; 7; 8; 9; 10]. In terms of research and development, extensive investigations have been carried out on the complex problem of concrete-filled steel tubular members over the last fifty years. One of the first studies on the compressive behavior of CFSS was done in the 1970s by [PERSON] et al. [11], showing that the load-bearing capacity (LBC) of CFSS members depended heavily on various parameters: the width-to-thickness ratio of the steel tube, strength of the concrete core, etc. In the past few years, with the increase in high-rise construction, the study of CFSS members under compression has received growing amounts of attention from both engineers and scholars, including [PERSON] et al. [12], [PERSON] et al. [13], [PERSON] et al. [14], [PERSON] et al. [15], [PERSON] et al. [16], [PERSON] and [PERSON] [8] and [PERSON] et al. [17]. These works revealed that CFSS members exhibited good strength, strong ductility and beneficial construction characteristics. Although there are usually members under eccentric loading in buildings (i.e., corner and side columns), there is a scant body of work examining the compressive behavior of eccentrically loaded CFSS columns [18; 19]. Therefore, it is absolutely crucial to study this type of composite structures under eccentric loading, especially in predicting their LBC [20; 21]. In recent decades, Machine Learning (ML) approaches have been extensively used for predicting the behavior of structural members [22; 23; 24]. Such models exhibit significant advantages cannot be found in traditional techniques (i.e., linear regression, regularization, response surface methodology and multiple regression [25; 26; 27]). The main reason for such superiority is that ML techniques do not require assumptions or predefined constraints about the form of the model (for instance between dependent and independent variables) [25; 26; 27; 28]. Nonetheless, several investigations have been proposed to relate physical laws and ML techniques. For instance, [PERSON] et al. [29] discussed physics-constrained deep learning in a high-dimensional surrogate model. [PERSON] et al. [30] proposed label-free supervision for an Artificial Neural Network (ANN) model, under constraints derived from prior known laws of physics. [PERSON] et al. [31] trained an ANN to approximate the solution by minimizing the violation of the governing Partial Differential Equations in complex geometries. Despite the lack of physical constraints in ML models (could be considered as their major limitation), they are more and more used as a surrogate model in various problems, especially for capturing and tracking nonlinear behavior between inputs and outputs [32; 33; 34]. Many researchers have studied the enhanced capability of the ANN and compared it with conventional models for various structural engineering applications, including [PERSON] et al. [22], [PERSON] et al. [24] and [PERSON] et al. [33]. The results of these studies showed that ML approaches outperformed the conventional models. The most common ML techniques used in civil engineering problems include Support-Vector Machine [35], Regression Tree [36], Random forest [37] and especially ANN [22; 24; 34; 38; 39; 40].