Comparative Analysis of Bioinspired and Domain Adaptation Approaches for Structural Health Monitoring Under Varying Environmental Conditions Structural Health Monitoring (SHM) is of great importance across various engineering fields, offering crucial insights into the integrity and performance of structures. With the advent of advanced sensor technologies and data analytics, there has been a shift towards data-driven methods, such as machine learning and pattern recognition. However, the training and test data should not include variations in operational and environmental conditions (EOCs), which present a complex challenge in identifying structural damage. Indeed, the EOCs influence the dynamic properties and extracted features, and their effects can be similar to the damage-induced variations, limiting their detection.