Structural health monitoring and reliability estimation: Long span truss bridge application with environmental monitoring data

2008 ◽  
Vol 30 (9) ◽  
pp. 2347-2359 ◽  
Author(s):  
F. Necati Catbas ◽  
Melih Susoy ◽  
Dan M. Frangopol
Keyword(s):  
Structural Health Monitoring ◽  
Environmental Monitoring ◽  
Health Monitoring ◽  
Reliability Estimation ◽  
Monitoring Data ◽  
Structural Health ◽  
Long Span ◽  
Truss Bridge ◽  
Environmental Monitoring Data

Restoration of missing structural health monitoring data using spatiotemporal graph attention networks

2021 ◽  
pp. 147592172110568
Author(s):  
Jin Niu ◽  
Shunlong Li ◽  
Zhonglong Li
Keyword(s):  
Missing Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Low Cost ◽  
Monitoring Data ◽  
Structural Health ◽  
Long Span ◽  
Temporal Correlations ◽  
Spatial Dependencies ◽  
Cable Forces

For structural health monitoring systems with many low-cost sensors, missing data caused by sensor faults, power supply interruptions and data transmission errors are almost inevitable, significantly affecting structural diagnosis and evaluation. Considering the inherent spatial and temporal correlations in the sensor network, this study proposes a spatiotemporal graph attention network for restoration of missing data. The proposed model was stacked with a graph convolutional layer and several spatiotemporal blocks composed of spatial and temporal layers. The monitoring data of normal sensors were first mapped to all sensors through the graph convolutional layer, and attention mechanisms were used in the spatiotemporal blocks to model the spatial dependencies of sensors and the temporal dependencies of time steps, respectively. The extracted spatiotemporal features were assembled through a fully connected layer to reconstruct the missing signals. In this study, both homogeneous and heterogeneous monitoring items were used to calculate the spatial attention coefficients. The data restoration accuracy with and without the multi-source data fusion was discussed. Application on a long-span cable-stayed bridge to restore missing cable forces demonstrates that spatiotemporal attention modelling can achieve satisfactory restoring accuracy without any prior analysis.

Acceleration data quality assessment for bridge structural health monitoring via statistical and deep-learning approach

2021 ◽  
Author(s):  
Huaqiang Zhong ◽  
Limin Sun ◽  
José Turmo ◽  
Ye Xia
Keyword(s):  
Structural Health Monitoring ◽  
Data Quality ◽  
Health Monitoring ◽  
Structural Damage ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Operating Conditions ◽  
Monitoring Data ◽  
Statistical Characteristics ◽  
Structural Health

<p>In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of the main girder and arch rib of a long-span arch bridge as the research object, analyzes and summarizes the statistical characteristics of the data, summarizes 6 abnormal data conditions, and proposes a data quality evaluation method of convolutional neural network. This paper conducts frequency statistics on the acceleration vibration amplitude of the bridge in December 2018 in hours. In order to highlight the end effect of frequency statistics, the whole is amplified and used as network input for training and data quality evaluation. The results are good. It provides another new method for structural monitoring data quality evaluation and abnormal data elimination.</p>

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