[HTML][HTML] A comparative study of damage-sensitive features for rapid data-driven seismic structural health monitoring
Rapid post-earthquake damage assessment forms a critical element of resilience, ensuring
a prompt and functional recovery of the built environment. Monitoring-based approaches …
a prompt and functional recovery of the built environment. Monitoring-based approaches …
The value of monitoring a structural health monitoring system
Abstract Structural Health Monitoring (SHM) systems are adopted to acquire timely and
continuous data on the state of civil structures, aerospace vehicles, and industrial machines …
continuous data on the state of civil structures, aerospace vehicles, and industrial machines …
[HTML][HTML] Towards a dynamic earthquake risk framework for Switzerland
Scientists from different disciplines at ETH Zurich are developing a dynamic, harmonised,
and user-centred earthquake risk framework for Switzerland, relying on a continuously …
and user-centred earthquake risk framework for Switzerland, relying on a continuously …
[HTML][HTML] Fusing expert knowledge with monitoring data for condition assessment of railway welds
Monitoring information can facilitate the condition assessment of railway infrastructure, via
delivery of data that is informative on condition. A primary instance of such data is found in …
delivery of data that is informative on condition. A primary instance of such data is found in …
Optimized U-shape convolutional neural network with a novel training strategy for segmentation of concrete cracks
Crack detection is a vital component of structural health monitoring. Several computer vision-
based studies have been proposed to conduct crack detection on concrete surfaces, but …
based studies have been proposed to conduct crack detection on concrete surfaces, but …
[PDF][PDF] A hybrid deep neural network compression approach enabling edge intelligence for data anomaly detection in smart structural health monitoring systems
This study explores an alternative to the existing centralized process for data anomaly
detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) …
detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) …
Abnormal data detection and recovery of sensors network based on spatiotemporal deep learning methodology
This paper proposes a novel deep-learning-enabled framework for abnormal data detection
and recovery considering spatial-temporal correlation among sensors. The wavelet …
and recovery considering spatial-temporal correlation among sensors. The wavelet …
Damage identification of wind turbine blades using the microphone array under different parametric and measuring conditions: A prototype study with laboratory-scale …
Structural health monitoring (SHM) of wind turbine blades is significant to the reliability and
efficiency of wind energy generation, and it is a challenging issue due to the complicated …
efficiency of wind energy generation, and it is a challenging issue due to the complicated …
A self-supervised classification algorithm for sensor fault identification for robust structural health monitoring
AM Oncescu, A Cicirello - European Workshop on Structural Health …, 2022 - Springer
A self-supervised classification algorithm is proposed for detecting and isolating sensor
faults of health monitoring devices. This is achieved by automatically extracting information …
faults of health monitoring devices. This is achieved by automatically extracting information …
Data Anomaly Detection through Semisupervised Learning Aided by Customised Data Augmentation Techniques
Structural health monitoring (SHM) systems may suffer from multiple patterns of data
anomalies. Anomaly detection is an essential preprocessing step prior to the use of …
anomalies. Anomaly detection is an essential preprocessing step prior to the use of …