Finja Rentzsch holm

Reducing Experimental Data Requirements in CNN-based damage detection through Transfer Learning

Finja Rentzsch holm, Tobias Schalm, Jorge Luis Jiménez Aparicio, K. Schröder

TL;DR: This study demonstrates that transfer learning enables efficient adaptation to real-world conditions, offering a cost-effective and scalable solution for data-driven SHM.

While neural networks represent a promising approach for evaluating sensor data to assess damage presence, location and severity, large amounts of data are required for training. However, the generation of experimental data is both labor-intensive and costly. Transfer learning is…Read more