## 1 Introduction  
Moisture damage in asphalt mixtures is the degradation of the mixture's mechanical properties due to the existence of water in the pavement, either as a liquid or vapor, which eventually leads to an adhesive failure between the bitumen and mineral aggregates and a cohesive failure within the bitumen that coats the aggregates [1, 2]. Moisture damage is a critical phenomenon that alleviates the stiffness and load capacity of flexible pavements [3] and causes a costly failure of the pavement structure [4]. It also leads to the formation of various pavement distresses, such as stripping, raveling, shelling, and hydraulic scourp [1], and increases the severity of existing rutting, cracking, and potholes [5]. Moreover, stripping is the most common distress induced by moisture damage, where a physical separation (adhesive failure) can result at the aggregate-bitumen interface due to the higher surface energy of water compared to the asphalt cement [2, 6, 7, 8]. Many laboratory testing methods were used to evaluate and measure the moisture susceptibility of asphalt mixtures. Some of the tests include immersed wheel-tracking configurations such as the Hamburg wheel-tracking test and the Asphalt Pavement Analyzer device [9, 10], while others evaluate moisture damage by calculating the ratio of strength or stiffness of water-conditioned compacted Hot-Mix Asphalt (HMA) samples to that of unconditioned samples [4]. The latter includes several testing protocols such as the immersion-compression test, the resilient modulus test, the double-punch method, the tensile strength ratio (TSR) or modified Lottman (AASHTO T 283), the Retained StabilityIndex (RSI), and others [11; 12; 13; 14]. Even though these testing procedures help compare the moisture susceptibility of different mixtures, they do not provide information about the proper characteristics of asphalt mixes. This refers to the fact that they do not consider the fundamental measurements of material properties that influence the mechanisms of moisture-induced damage in asphalt mixes [8]. Also, adopting a single test from among these methods as an ideal procedure for characterizing the moisture sensitivity performance of mixes is impractical due to the wide variety of conditions, factors, and material properties that impact the moisture susceptibility of asphalt pavement [9; 10; 11; 15]. Machine learning (ML)-based methods, which are subfields of artificial intelligence (AI) techniques, have been widely implemented in various engineering disciplines due to their powerful ability and high accuracy in analyzing and developing predictive models [16; 17; 18; 19]. Artificial Neural Networks (ANNs) and Gene Expression Programming (GEP) are typical examples of methods that are successfully utilized for predicting the complex characteristics of asphalt mixtures [16; 20; 21]. Many research works have predicted the moisture sensitivity of asphalt mixes using the TSR test [22; 23; 24]. A recent study introduced by [PERSON] and [PERSON] [25] utilized the ANN technique to solely study the influence of volumetric and aggregate properties of asphalt mixtures on their moisture resistance based on the Retained Stability Index (RSI). Although their study thoroughly presented the relationships between various mixtures' properties and RSI values, it included some little-to-none influential variables on moisture-induced damage and direct mathematical equations for predicting the RSI values of asphalt mixtures. The RSI test is a straightforward testing method that is still valid and is used by pavement agencies to evaluate the moisture sensitivity of asphalt mixes. This study aims to develop an accurate model that provides a practical and direct mathematical equation for estimating the RSI value of asphalt mixes using a hybrid machine learning approach, including the ANN and GEP techniques.