A Combined Model-Order Reduction and Deep Learning Approach for Structural Health Monitoring under Varying Operational and Environmental Conditions

Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate

“…Considering data-driven algorithms, damage localization is often addressed by exploiting a DL feature extractor followed by a classification or a regression module, e.g., as done in [9,10,13]. However, due to the need of training in a simulated environment, the risk of losing generalization capabilities on real monitoring data is high.…”

“…reduced-order model in Equation ( 2), i.e., the LF model used to construct D LF , has been built performing a POD upon 40,000 snapshots in time, collected while exploring the parametric input space x LF . 14 POD-bases are selected and stored in matrix W, in place of the original 4659 dofs, after having fixed a suitable tolerance on the energy norm of the reconstruction error (tol POD = 10 −3 ); for further details see, e.g., [9,13].…”

“…Because handling finite element (FE) simulations within an MCMC analysis is computationally impractical, a FE model capable of simulating the effect of damage on the structural response is adopted only to build labelled datasets of vibration recordings for known damage positions, see for instance [13]. A data-driven surrogate model is adopted instead to map operational and damage parameters to the associated vibration signals in place of the FE model.…”

A Parsimonious Yet Robust Regression Model for Predicting Limited Structural Responses of Remote Sensing

“…Structural health monitoring (SHM) of bridge structures [1][2][3][4] must fully account for various environmental actions such as ambient temperature, wind, moisture, and possible chemical attacks [5]. In most cases, bridges, especially long-span ones, are slender and are therefore susceptible to vibrations [6][7][8].…”

A Comparative Study on Structural Displacement Prediction by Kernelized Regressors under Limited Training Data

“…This methodology is based on sensor deployment over the structure to be monitored, data acquisition, modeling, feature extraction, and feature analysis [6][7][8]. The modeling stage can be either physics-or data-based [9][10][11][12]. Sensors are obviously important to any SHM process because the acquired data from the structures provide information on their behavior and current state.…”