## 4 Discussion  
Vibration comfort, related to the personal sensation of comfort or discomfort felt by people subjected to vibrations in a building, is essential in influencing the well-being, focus, and overall health of residents. This concern is becoming more important due to the continuous urbanization trend that results in the expansion of roads and the increase in vehicles in city environments. Consequently, there is an urgent requirement to alleviate the negative impacts of vibrations caused by traffic on the structural safety of buildings and the residents living inside them. Recent studies indicate that the selection of slab materials significantly affects the comfort of people inside a building. For example, it has been noted that a wooden slab usually exhibits greater susceptibility to transmitting vibrations than other options, such as the Klein slab design [10]. This highlights the significance of investing in insulating materials for slabs to ensure optimal comfort and a decrease in the transfer of vibrations throughout the building environment. The effect of vibrations on human occupants is a complex and crucial matter that requires additional thorough investigation and analysis. Through the thoughtful choice and use of suitable insulating materials, one can considerably reduce the transmission of vibrations in structures, thus improving the overall comfort and health of people living in buildings exposed to vibrations. Fundamentally, the effort to tackle vibration comfort issues requires a comprehensive strategy that includes both structural adjustments and a greater awareness of how environmental problems like vibrations from traffic can affect human health and overall quality of life. By actively tackling these challenges with efficient insulation methods and ongoing research, safer and more comfortable living spaces can be ensured. The outlined model (artificial neural network model) is a crucial tool employed in the verification process to ensure that the methodology effectively captures the impact of forced or communication vibrations and meets the necessary acceptance criterion. It is imperative to note that the reliability and accuracy of the model are directly proportional to the volume of measurement data used [27]. The larger the dataset, the higher the chances of the model's accuracy and reliability. Figure 10: Graph showing the accuracy of learning and validation samples in network no. 2, depending on the number of epochs with one hidden layer. Prior to starting the construction of a neural network, it is crucial to perform initial steps to establish strong groundwork for effective analysis. To begin with, the dataset needs careful organization, which includes cleaning and converting the database into an appropriate format for subsequent processing. Subsequently, the data should be partitioned into distinct learning, validating, and testing sets. This separation is paramount as it helps to prevent overfitting issues and ensures the development of a robust model that can effectively be generalized to new data [28; 29; 30]. When exploring different methodologies for model development, a decision tree emerges as a promising approach. Its practicality and user-friendly nature make it a valuable tool for predictive analytics, especially in scenarios requiring knowledge extraction from tabular data [28]. Decision trees exhibit high precision even with smaller datasets, enabling swift learning times and efficient model development. Given these advantages, the decision tree strategy will be employed in predicting traffic patterns, initially offering satisfactory results that can be further optimized as needed [29]. Looking into the specifics of constructing the decision tree, the input variables will be categorized into nodes representing characteristic attributes--for instance, different slab types made from materials like timber, steel, or concrete.