It is beneficial to know the soil type on the go during excavation to ensure optimal Earth pressure balance tunnel boring machine (EPBM) operation. This encouraged many researchers to perform data-driven analyses to develop prediction models. These models utilize the complex mapping hidden in the operational data collected by TBM sensors that relate them to the excavated ground condition. However, why data-driven prediction models such as machine learning (ML) can capture the ground condition using the operational parameters is a question that is often unanswered due to their highly complex or black-box nature. On the other hand, more quality ground-truth data is often needed along the tunnel alignment to ensure optimal training for the supervised ML algorithms. In such cases, a semi-supervised algorithm can provide a better generalization of the relationship between operational parameters and excavated tunneling ground. To this end, this research attempted to investigate the behavior of the operational parameters while they are encountering different ground conditions based on the predictions made by a successfully developed semi-supervised machine learning model to identify soil type during EPBM operation.