## 1 Introduction
Assessing the degree of shrinkage and expansion in expansive soils is a complex problem, as influencing factors behave with characteristics of fuzziness and uncertainty that are difficult to interpret using traditional methods. Consequently, it is necessary to develop suitable methods for analysing the classification of swell and shrinkage in expansive soils. To address this challenge, we propose a novel approach that uses machine learning to predict the optimal dry unit weight and applied stress required to mitigate swelling in expansive soils. To assess the degree of risk associated with expansive soils, it is important to identify the main cause of hazards, which is volumetric changes [24]. Therefore, an effective solution must be developed to reduce these changes and limit the associated risks. This can be achieved by following these steps [24]:  
1. Collecting information on the damage that has occurred in the study area, the risk reduction methods employed, and their effectiveness. 2. Conducting a comprehensive analysis of the soil and its properties (in situ and laboratory testing) by a geotechnical engineering expert. 3. Classifying the degree of severity of the expansive soil based on the findings from steps 1 and 2. 4. Proposing an appropriate risk mitigation strategy. In the present study, the primary focus is on steps 3 and 4, which are considered crucial as they involve the selection of an appropriate method to mitigate the risks associated with expansive soils. The first two steps (i.e., collection of information related to damages and a thorough study of soil properties) are also essential, particularly step 2, which requires the expertise of a geotechnical specialist. Therefore, it is imperative to choose an experienced specialist with in-depth knowledge of expansive soils. The classification process in this study relies on predicting the optimal dry unit weight, a value that corresponds to achieving the minimal swell amplitude. Subsequent to this predictive determination, a comparative assessment in relation to the site dry unit weight is executed. In cases where the predicted dry unit weight manifests as inferior to the site's empirically observed dry unit weight, the classification of the expansive soil is assigned a precarious characterization. In contrast, when the predicted value of the dry unit weight exceeds the site's dry unit weight, the expansive soil is classified as stable, implying the absence of swelling-induced structural damage. It is worth noting that the magnitude of perceived risk is determined by the quantitative difference between the predicted and actual dry unit weight. It is important to note, however, that the precise quantification of this risk gradient is not the major focus of the current investigation. Based on the literature, various methods have been used to address the issues related to expansive soils, such as controlling the level of compactness, chemical improvement using lime or cement, and pre-moistening of expansive soil. However, these methods are often expensive and time-consuming to implement, thus emphasizing the need for an alternative approach. Henceforth, this study aims to propose a method that would effectively address the issue of expansive soil by reducing its volumetric changes and limiting its risks. Specifically, the focus of this paper is on step 4, which involves recommending a suitable approach to mitigate the identified risks. It is worth noting that step 2 plays a critical role in this process, and hence, a geotechnical specialist with extensive experience in expansive soils must be selected for this task. This study proposes a stress-based method that leverages the characteristics of the expansive soil. The proposed approach is primarily based on the machine learning model, which is developed through experimental work, as will be discussed later. The steps are outlined as follows:  
* Place a high permeability soil layer beneath the entire building with sufficient thickness to ensure uniform moisture conditions under the structure [25].