LSTM-based bi-directional urban safety network using a conditional vector of frequency domain decomposition data.

ABSTRACT: Structural health monitoring (SHM) systems play a critical role in ensuring the safety of buildings during seismic events. However, their effectiveness is limited when sensor data is lost due to malfunction, damage, or communication failure. To address this issue, this study proposes a bi-directional urban safety network that utilizes LSTM models to predict the dynamic structural responses of buildings based on the responses of adjacent structures within the network. The framework incorporates both time-domain displacement data and frequency-domain features, which are transformed into conditional vectors for input into the LSTM model. The proposed method was evaluated using both linear and nonlinear structural systems subjected to seismic loads. Results demonstrate that the use of conditio

SUBMITTER: Lee HY 

PROVIDER: S-EPMC12378354 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature