Modelling of structures is an important tool for decision-making regarding infrastructure. In the absence of sufficient knowledge of the physical phenomena that affect the structure, one can follow a data-driven approach to model its behaviour, relying exclusively on data acquired from it. However, a common problem of this approach is the scarcity of data or biased data. To deal with these two problems, approaches have been considered to transfer knowledge via machine learning models from one domain to another. The current work considers the case of population-based structural health monitoring (PBSHM) of structures. Such an approach is motivated by the common physics that dictates the behaviour of similar structures, which could offer an opportunity to exploit information from a population to create more robust and trustworthy models of data-poor structures of the same population. More specifically, the approach followed here is that of symbolic regression and the transfer is attempted between an extensively monitored structure and a data-poor structure for a regression application. The methodology is applied in a prognosis problem of crack growth in metal plates, and the results reveal the potential of symbolic regression to perform knowledge transfer.