Data fusion approaches for structural health monitoring and system identification: Past, present, and future
RT Wu, MR Jahanshahi - Structural Health Monitoring, 2020 - journals.sagepub.com
During the past decades, significant efforts have been dedicated to develop reliable
methods in structural health monitoring. The health assessment for the target structure of …
methods in structural health monitoring. The health assessment for the target structure of …
Imaging and machine learning techniques for diagnosis of Alzheimer's disease
Alzheimer's disease (AD) is a common health problem in elderly people. There has been
considerable research toward the diagnosis and early detection of this disease in the past …
considerable research toward the diagnosis and early detection of this disease in the past …
Physics-informed multi-LSTM networks for metamodeling of nonlinear structures
This paper introduces an innovative physics-informed deep learning framework for
metamodeling of nonlinear structural systems with scarce data. The basic concept is to …
metamodeling of nonlinear structural systems with scarce data. The basic concept is to …
Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling
Accurate prediction of building's response subjected to earthquakes makes possible to
evaluate building performance. To this end, we leverage the recent advances in deep …
evaluate building performance. To this end, we leverage the recent advances in deep …
Deep long short-term memory networks for nonlinear structural seismic response prediction
This paper presents a comprehensive study on developing advanced deep learning
approaches for nonlinear structural response modeling and prediction. Two schemes of the …
approaches for nonlinear structural response modeling and prediction. Two schemes of the …
A new neural dynamic classification algorithm
The keys for the development of an effective classification algorithm are: 1) discovering
feature spaces with large margins between clusters and close proximity of the classmates …
feature spaces with large margins between clusters and close proximity of the classmates …
Structural damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating
Structural health monitoring methods are broadly classified into two categories: data-driven
methods via statistical pattern recognition and physics-based methods through finite …
methods via statistical pattern recognition and physics-based methods through finite …
On the value of monitoring information for the structural integrity and risk management
S Thöns - Computer‐Aided Civil and Infrastructure Engineering, 2018 - Wiley Online Library
This article introduces an approach and framework for the quantification of the value of
structural health monitoring (SHM) in the context of the structural risk and integrity …
structural health monitoring (SHM) in the context of the structural risk and integrity …
Bayesian‐optimized unsupervised learning approach for structural damage detection
KA Eltouny, X Liang - Computer‐Aided Civil and Infrastructure …, 2021 - Wiley Online Library
Structural health monitoring (SHM) is developing rapidly to fulfill the world's need for resilient
and sustainable communities. Due to the current advancements in machine learning and …
and sustainable communities. Due to the current advancements in machine learning and …
A texture‐based video processing methodology using Bayesian data fusion for autonomous crack detection on metallic surfaces
Regular inspection of the components of nuclear power plants is important to improve their
resilience. However, current inspection practices are time consuming, tedious, and …
resilience. However, current inspection practices are time consuming, tedious, and …