[HTML][HTML] A digital twin framework for civil engineering structures
The digital twin concept represents an appealing opportunity to advance condition-based
and predictive maintenance paradigms for civil engineering systems, thus allowing reduced …
and predictive maintenance paradigms for civil engineering systems, thus allowing reduced …
Recent advances in uncertainty quantification in structural response characterization and system identification
Structural dynamics has numerous practical applications, such as structural analysis,
vibration control, energy harvesting, system identification, structural safety assessment, and …
vibration control, energy harvesting, system identification, structural safety assessment, and …
Early damage assessment in large-scale structures by innovative statistical pattern recognition methods based on time series modeling and novelty detection
Time series analysis and novelty detection are effective and promising methods for data-
driven structural health monitoring (SHM) based on the statistical pattern recognition …
driven structural health monitoring (SHM) based on the statistical pattern recognition …
SHM under varying environmental conditions: An approach based on model order reduction and deep learning
Data-driven approaches to structural health monitoring (SHM) have been recently shown to
be a powerful paradigm, helping to lead to an evolution of traditional scheduled-based …
be a powerful paradigm, helping to lead to an evolution of traditional scheduled-based …
Online structural health monitoring by model order reduction and deep learning algorithms
Within a structural health monitoring (SHM) framework, we propose a simulation-based
classification strategy to move towards online damage localization. The procedure combines …
classification strategy to move towards online damage localization. The procedure combines …
A new Kalman filter approach for structural parameter tracking: Application to the monitoring of damaging structures tested on shaking-tables
In this paper, a new data assimilation framework for correcting finite element models from
datasets acquired on-the-fly in low-frequency dynamics is presented. An Unscented Kalman …
datasets acquired on-the-fly in low-frequency dynamics is presented. An Unscented Kalman …
Fast unsupervised learning methods for structural health monitoring with large vibration data from dense sensor networks
A Entezami, H Shariatmadar… - Structural Health …, 2020 - journals.sagepub.com
Data-driven damage localization is an important step of vibration-based structural health
monitoring. Statistical pattern recognition based on the prominent steps of feature extraction …
monitoring. Statistical pattern recognition based on the prominent steps of feature extraction …
Multi-storey shear type buildings under earthquake loading: Adversarial learning-based prediction of the transient dynamics and damage classification
In this paper, the transient dynamic response of shear type multi-storey buildings subjected
to earthquake ground motion is generated via adversarial learning technique under different …
to earthquake ground motion is generated via adversarial learning technique under different …
[HTML][HTML] A multi-fidelity surrogate model for structural health monitoring exploiting model order reduction and artificial neural networks
Stochastic approaches to structural health monitoring (SHM) are often inevitably limited by
computational constraints. For instance, for Markov chain Monte Carlo algorithms relying …
computational constraints. For instance, for Markov chain Monte Carlo algorithms relying …
Fully convolutional networks for structural health monitoring through multivariate time series classification
We propose a novel approach to structural health monitoring (SHM), aiming at the automatic
identification of damage-sensitive features from data acquired through pervasive sensor …
identification of damage-sensitive features from data acquired through pervasive sensor …