Due to the growing number of ageing structures and infrastructures that require monitoring to ensure adequate safety, automation in dynamic identification has gained increasing importance. This thesis proposes the development of an Automated Operational Modal Analysis (AOMA) Builder Algorithm designed and tested for civil engineering applications. In this field, determining modal parameters becomes more challenging due to the structures' large size and low frequencies. Moreover, configurable and quantifiable external excitations are often disregarded due to the devices' high cost and weight. Given the unsupervised learning and unmeasured input process, the primary objective is to automate the selection of potential physical modes from the stabilisation diagram obtained using the Covariance-driven Stochastic Subspace Identification (Cov-SSI) method.