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 …
Structural dynamic response reconstruction using self-attention enhanced generative adversarial networks
In structural health monitoring (SHM) of civil engineering structures, loss of measured
structural responses inevitably occurs in practice, especially when structures encounter …
structural responses inevitably occurs in practice, especially when structures encounter …
Model-assisted compressed sensing for vibration-based structural health monitoring
The main challenge in the implementation of long-lasting vibration monitoring systems is to
tackle the constantly evolving complexity of modern “mesoscale” structures. Thus, the design …
tackle the constantly evolving complexity of modern “mesoscale” structures. Thus, the design …
Missing data imputation framework for bridge structural health monitoring based on slim generative adversarial networks
S Gao, W Zhao, C Wan, H Jiang, Y Ding, S Xue - Measurement, 2022 - Elsevier
In structural health monitoring (SHM) systems, sensors are important to collect structural
responses to assess the load-resistant capacity and health status of structures. However …
responses to assess the load-resistant capacity and health status of structures. However …
Model-free data reconstruction of structural response and excitation via sequential broad learning
In this study, a novel sequential broad learning (SBL) approach is proposed to reconstruct
the missing signal of damaged sensors in structural health monitoring (SHM) sensory …
the missing signal of damaged sensors in structural health monitoring (SHM) sensory …
A comprehensive review on compressive sensing
C Shaik, R RajaA, SS Kalapala… - … on Applied Artificial …, 2022 - ieeexplore.ieee.org
Sparse sampling, also known as compressed sampling or compressed sensing (CS), is a
new signal processing technique that samples the signal with considerably fewer samples …
new signal processing technique that samples the signal with considerably fewer samples …
Deep learning-based recovery method for missing structural temperature data using LSTM network
Benefiting from the massive monitoring data collected by the Structural health monitoring
(SHM) system, scholars can grasp the complex environmental effects and structural state …
(SHM) system, scholars can grasp the complex environmental effects and structural state …
Exploiting the inter-correlation of structural vibration signals for data loss recovery: A distributed compressive sensing based approach
Compressive Sensing (CS) is a novel signal sampling technique that can enhance the
resiliency of the data transmission process in Structural Health Monitoring (SHM) systems by …
resiliency of the data transmission process in Structural Health Monitoring (SHM) systems by …
[PDF][PDF] Structural health monitoring response reconstruction based on UAGAN under structural condition variations with few-shot learning [J]
Inevitable response loss under complex operational conditions significantly affects the
integrity and quality of measured data, leading the structural health monitoring (SHM) …
integrity and quality of measured data, leading the structural health monitoring (SHM) …
Performance evaluation of compressive sensing based lost data recovery using OMP for damage index estimation in ultrasonic SHM
Compressive sensing (CS) has been widely explored for data compression and signal
recovery in presence of lossy transmission in structural health monitoring (SHM) …
recovery in presence of lossy transmission in structural health monitoring (SHM) …