GAN-based anomaly detection: A review
X Xia, X Pan, N Li, X He, L Ma, X Zhang, N Ding - Neurocomputing, 2022 - Elsevier
Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …
State-of-the-art review on advancements of data mining in structural health monitoring
To date, data mining (DM) techniques, ie artificial intelligence, machine learning, and
statistical methods have been utilized in a remarkable number of structural health monitoring …
statistical methods have been utilized in a remarkable number of structural health monitoring …
Mutual information based anomaly detection of monitoring data with attention mechanism and residual learning
Due to the damage of sensors or transmission equipment, abnormal monitoring data
inevitably exists in the measured raw data, and it significantly impacts the condition …
inevitably exists in the measured raw data, and it significantly impacts the condition …
[HTML][HTML] Deep learning in automated ultrasonic NDE–developments, axioms and opportunities
The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator
manually interpreting data with the support of rudimentary automation tools. Recently, many …
manually interpreting data with the support of rudimentary automation tools. Recently, many …
How generative adversarial networks promote the development of intelligent transportation systems: A survey
In current years, the improvement of deep learning has brought about tremendous changes:
As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) …
As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) …
Unsupervised learning-based damage assessment of full-scale civil structures under long-term and short-term monitoring
MH Daneshvar, H Sarmadi - Engineering Structures, 2022 - Elsevier
Abstract Machine learning has become an influential and useful tool for many civil
engineering applications, particularly structural health monitoring (SHM). For this reason …
engineering applications, particularly structural health monitoring (SHM). For this reason …
Artificial intelligence in structural health management of existing bridges
VM Di Mucci, A Cardellicchio, S Ruggieri… - Automation in …, 2024 - Elsevier
The paper presents a systematic review about the use of artificial intelligence (AI) in the field
of structural health management of existing bridges. Using the PRISMA protocol, 81 journal …
of structural health management of existing bridges. Using the PRISMA protocol, 81 journal …
Review of artificial intelligence-based bridge damage detection
Bridges are often located in harsh environments and are thus extremely susceptible to
damage. If the initial damage is not detected in time, it can develop further causing safety …
damage. If the initial damage is not detected in time, it can develop further causing safety …
Unsupervised learning methods for data-driven vibration-based structural health monitoring: a review
Structural damage detection using unsupervised learning methods has been a trending
topic in the structural health monitoring (SHM) research community during the past decades …
topic in the structural health monitoring (SHM) research community during the past decades …
Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems
A challenging problem in risk and reliability analysis of Complex Engineering Systems
(CES) is performing and updating risk and reliability assessments on the whole system with …
(CES) is performing and updating risk and reliability assessments on the whole system with …