Transfer learning framework for post-seismic anomaly detection in historic masonry structures under varying environmental conditions
Year: 2026
Type: article
Abstract: Historical masonry structures present unique challenges for structural health monitoring due to their brittle behaviour, material heterogeneity, and extreme sensitivity to environmental variations, which can often mask earthquake-induced damage. This study proposes an unsupervised deep transfer learning framework based on a Feature-wise Linear Modulation (FiLM)-conditioned convolutional autoencoder designed for post-seismic diagnostics. The methodology utilises dominant singular values extracted via singular value decomposition as compact spectral features, which are explicitly coupled with temperature channels within a multivariate latent space. To address data scarcity, the framework transfers learned “healthy manifolds” from a data-rich source domain (the Consoli Palace) to a data-scarce target domain (the San Pietro Bell Tower). A grid-search optimisation strategy is employed to identify critical transferable layers, while a multi-objective training pipeline autotunes distinct loss terms, ensuring balanced learning across diverse operational states. Validation results from the 2016 Central Italy seismic sequence demonstrate that the transfer-learning model significantly outperforms a scratch-trained baseline, achieving higher balanced accuracy. The framework effectively distinguishes cumulative seismic damage from seasonal thermal noise, providing a robust and scalable tool for the rapid safety evaluation of architectural heritage.
Source: Computers & Structures
Institution: University of Perugia
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,200
OpenAlex ID: W7165891062