The development of machine learning algorithms for Structural Health Monitoring (SHM) is rapidly advancing. However, their application for real-world structures finds a high number of complications. One is the need for comprehensive data for training the proper algorithms. Thus, Population-Based Health Monitoring (PBSHM) overcomes these challenges by sharing information between different structures. In this framework, it is necessary to understand to what extent knowledge can be shared, especially for heterogeneous datasets. Therefore, this study implements a simple domain adaptation technique based on Statistical Alignment (SA) on a population of heterogeneous shear structures to investigate how the performance changes due to the variations within the population.