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 …
Missing measurement data recovery methods in structural health monitoring: The state, challenges and case study
In the field of structural health monitoring (SHM), the sensor measurement signals collected
from the structure are the foundation and key of the SHM system. However, the loss of …
from the structure are the foundation and key of the SHM system. However, the loss of …
Automating fake news detection using PPCA and levy flight-based LSTM
In recent years, rumours and fake news are spreading widely and very rapidly all over the
world. Such circumstances lead to the propagation and production of an inaccurate news …
world. Such circumstances lead to the propagation and production of an inaccurate news …
Deep learning neural network model for tunnel ground surface settlement prediction based on sensor data
Monitoring and prediction of ground settlement during tunnel construction are of great
significance to ensure the safe and reliable operation of urban tunnel systems. Data‐driven …
significance to ensure the safe and reliable operation of urban tunnel systems. Data‐driven …
Current and future role of data fusion and machine learning in infrastructure health monitoring
Rapid advances in infrastructure health monitoring and sensing technologies allow the
monitoring of infrastructure assets continuously and in real-time throughout their life span …
monitoring of infrastructure assets continuously and in real-time throughout their life span …
Anomaly detection of high-frequency sensing data in transportation infrastructure monitoring system based on fine-tuned model
Anomaly detection has been widely studied in previous studies during recent decades;
however, there are still some challenges for high-frequency sensing data. The most …
however, there are still some challenges for high-frequency sensing data. The most …
A reconstruction method for missing data in power system measurement based on LSGAN
C Wang, Y Cao, S Zhang, T Ling - Frontiers in Energy Research, 2021 - frontiersin.org
The integrity of data is an essential basis for analyzing power system operating status based
on data. Improper handling of measurement sampling, information transmission, and data …
on data. Improper handling of measurement sampling, information transmission, and data …
A State Monitoring Algorithm for Data Missing Scenarios via Convolutional Neural Network and Random Forest
Y Xu, K Sun, Y Zhang, F Chen, Y He - IEEE Access, 2024 - ieeexplore.ieee.org
In Unmanned Aerial Vehicle (UAV) systems, packet loss during sensor data transmission
causes data missing, which reduces fault features in sensor signals and causes the …
causes data missing, which reduces fault features in sensor signals and causes the …
Heterogeneous structural responses recovery based on multi-modal deep learning
For structural health monitoring, a complete dataset is important for further analysis such as
modal identification and risk early warning. Unfortunately, the missing data normally exist in …
modal identification and risk early warning. Unfortunately, the missing data normally exist in …
Abnormal data recovery of structural health monitoring for ancient city wall using deep learning neural network
Continuous structural health monitoring is of great importance to preventive conservation for
ancient architectural heritages. However, abnormal monitoring data may trigger false …
ancient architectural heritages. However, abnormal monitoring data may trigger false …