Mapping top-two-floor corner coordinates to building strains in deep latent space

Abstract

Structural health monitoring is vital for ensuring building safety but faces numerous challenges, such as sensor durability, maintenance costs, and data loss. To address these issues, this study introduces a deep learning framework designed to obviate the need for ongoing maintenance of strain gauges. The framework aims to map the top-two-floor corner coordinates of a building to its strains in deep latent space. The framework comprises four steps: grouping coordinates, standardizing coordinates, generating a coordinate map, and learning the deep latent space. Here, learning the deep latent space refers to compressing high-dimensional input space features into low-dimensional deep latent space features to address the mapping problem while reducing overfitting and improving the explainability of a deep neural network for practical engineering applications. Once the deep latent space is learned, building strains can be predicted by observing only the top part of the building, eliminating the need for continuous maintenance of strain gauges in structural health monitoring. The proposed framework is evaluated on a four-story scaled building and demonstrates exceptional mapping performance in the learned deep latent space.