A systematic review of advanced sensor technologies for non-destructive testing and structural health monitoring

S Hassani, U Dackermann - Sensors, 2023 - mdpi.com
This paper reviews recent advances in sensor technologies for non-destructive testing
(NDT) and structural health monitoring (SHM) of civil structures. The article is motivated by …

The state of the art of artificial intelligence approaches and new technologies in structural health monitoring of bridges

R Zinno, SS Haghshenas, G Guido, K Rashvand… - Applied Sciences, 2022 - mdpi.com
The challenges of urban administration are growing, as the population, automobiles, and
cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A …

[HTML][HTML] Data anomaly detection for structural health monitoring by multi-view representation based on local binary patterns

Y Zhang, Z Tang, R Yang - Measurement, 2022 - Elsevier
Structural health monitoring (SHM) systems provide opportunities to understand the
structural behaviors remotely in real-time. However, anomalous measurement data are …

[HTML][HTML] Abnormal data detection for structural health monitoring: State-of-the-art review

Y Deng, Y Zhao, H Ju, TH Yi, A Li - Developments in the Built Environment, 2024 - Elsevier
Structural health monitoring (SHM) is widely used to monitor and assess the condition and
performance of engineering structures such as, buildings, bridges, dams, and tunnels …

Data Anomaly Detection for Structural Health Monitoring Based on a Convolutional Neural Network

SY Kim, M Mukhiddinov - Sensors, 2023 - mdpi.com
Structural health monitoring (SHM) has been extensively utilized in civil infrastructures for
several decades. The status of civil constructions is monitored in real time using a wide …

Multiclass anomaly detection of bridge monitoring data with data migration between different bridges for balancing data

C Qu, H Zhang, R Zhang, S Zou, L Huang, H Li - Applied Sciences, 2023 - mdpi.com
Structural health inspection systems are widely used to manage and maintain infrastructure
that involves massive sensor devices. However, these sensors receive the natural …

Development of data anomaly classification for structural health monitoring based on iterative trimmed loss minimization and human-in-the-loop learning

SK Huang, TX Lin - Structural Health Monitoring, 2025 - journals.sagepub.com
Huge amounts of data can be generated during long-term monitoring performed by
structural health monitoring (SHM) and structural integrity management applications …

[PDF][PDF] A hybrid deep neural network compression approach enabling edge intelligence for data anomaly detection in smart structural health monitoring systems

TG Mondal, JY Chou, Y Fu, J Mao - Smart Struct Syst, 2023 - researchgate.net
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) …

Deep learning–based data anomaly detection for highway slope structural health monitoring: A comparative study

S Dong, Z Long, S Zhang, J Wang, C Zuo… - Transportation …, 2025 - Elsevier
Highway slope instability has a significant influence on traffic safety. However, there are
many anomalies in slope SHM data, which is critical to timely warnings and safety …

Anomaly detection of massive bridge monitoring data through multiple transfer learning with adaptively setting hyperparameters

CX Qu, HM Zhang, TH Yi, ZY Pang, HN Li - Engineering Structures, 2024 - Elsevier
Civil infrastructure relies heavily on structural health monitoring systems. However, these
systems often encounter challenges due to sensor failures and environmental damage …