Health monitoring data analysis and comparison of prototype and laboratorial model for long-span cable-stayed bridge

2009 ◽  
Author(s):  
Ou Yang ◽  
Jinping Ou
Keyword(s):  
Data Analysis ◽  
Health Monitoring ◽  
Monitoring Data ◽  
Cable Stayed Bridge ◽  
Long Span

scholarly journals A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge

2018 ◽  
Vol 18 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Nguyen Lu Dang Khoa ◽  
Yang Wang ◽  
Bijan Samali ◽  
Xinqun Zhu
Keyword(s):  
Data Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Scale ◽  
Damage Identification ◽  
Tensor Analysis ◽  
Support Vector ◽  
Damage Scenarios ◽  
Structural Health ◽  
Cable Stayed Bridge

A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.

Monitored structural behavior of a long span cable-stayed bridge under environmental effects

2018 ◽  
Vol 4 (4) ◽  
pp. 137 ◽  
Author(s):  
Alemdar Bayraktar ◽  
Ashraf Ashour ◽  
Halil Karadeniz ◽  
Altok Kurşun ◽  
Arif Erdiş
Keyword(s):  
Air Temperature ◽  
Environmental Effects ◽  
Monitoring Data ◽  
Structural Behavior ◽  
Steel Cable ◽  
Daily Monitoring ◽  
Cable Stayed Bridge ◽  
Long Span ◽  
Cable Forces ◽  
Cable Stayed Bridges

An accurate numerical analysis of the behavior of long-span cable-stayed bridges under environmental effects is a challenge because of complex, uncertain and varying environmental meteorology. This study aims to investigate in-situ experimental structural behavior of long-span steel cable-stayed bridges under environmental effects such as air temperature and wind using the monitoring data. Nissibi cable-stayed bridge with total length of 610m constructed in the city of Adıyaman, Turkey, in 2015 is chosen for this purpose. Structural behaviors of the main structural elements including deck, towers (pylons) and cables of the selected long span cable-stayed bridge under environmental effects such as air temperature and wind are investigated by using daily monitoring data. The daily variations of cable forces, cable accelerations, pylon accelerations and deck accelerations with air temperature and wind speed are compared using the hottest summer (July 31, 2015) and the coldest winter (January 1, 2016) days data.

Intelligent Health Monitoring System for a Railway Cable-Stayed Bridge

2010 ◽  
Vol 148-149 ◽  
pp. 1390-1393
Author(s):  
De Shan Shan ◽  
Peng Yan ◽  
Zhen Hua Wang
Keyword(s):  
Information Processing ◽  
Monitoring System ◽  
Health Monitoring ◽  
Signal Transmission ◽  
Structural Safety ◽  
Signal Acquisition ◽  
Processing Technologies ◽  
Health Monitoring System ◽  
Cable Stayed Bridge ◽  
Long Span

Intelligent health monitoring system of the long-span railway cable-stayed bridge requires the comprehensive knowledge of instrumentation, analytical and information processing technologies with the knowledge and experiences in design, construction, operation and maintenance of railway cable-stayed bridge for long-term monitoring the performance throughout its lifecycle. It is necessary to perform sensor-based structural monitoring for identifying the bridge conditions in order to assure the structural safety and to evaluate the operational performance. The considerations for deploying a proper monitoring system are appropriate sensor instrumentation, robust signal acquisition, reliable signal processing, and intelligent signal and information processing. The experience on developing an autonomous monitoring system in the one certain railway cable-stayed bridge newly constructed is introduced in this paper. Sensor and hardware instrumentation, signal transmission, signal acquisition and analysis are schematically described mainly. Experience through this work will be worthwhile lessons for other similar efforts.

scholarly journals Overview of integrated health monitoring system installed on cable-stayed bridge and preliminary analysis of results

2021 ◽  
Vol 73 (06) ◽  
pp. 591-604
Keyword(s):  
Monitoring System ◽  
Data Storage ◽  
Health Monitoring ◽  
Yangtze River ◽  
Structural Damage ◽  
Monitoring Data ◽  
Extreme Condition ◽  
Health Monitoring System ◽  
Cable Stayed Bridge ◽  
Integrated Health

The Junshan Yangtze River Bridge, built in Wuhan, China in 2001, is a steel box girder cable-stayed bridge with a main span of 460m. Due to decades of service, the bridge suffered gradual degradation and some damage. Structural health monitoring has attracted worldwide attention due to its capacity of monitoring structural damage, assisting maintenance and management, and ensuring safe operation of bridges. To monitor the performance status of bridges and generate a timely safety alarm, an integrated health monitoring system has been designed and implemented on bridges. This paper provides a thorough account of the monitoring system used at the Junshan Yangtze River Bridge. It mainly focuses on the selection of monitoring variables and arrangement of sensor points, the data collection and transmission system, the data storage and management strategy, and the user interface system. All kinds of monitoring data collected under daily operation, such as vehicle load, deflection, displacement, and cable tension, are analysed. Monitoring data for an extreme condition, which involves two heavy trucks of 178 tons moving across the bridge, are also analysed. The results indicate that the monitoring system works well and that some local welded joints may experience fatigue damage.

Structural Health Monitoring Data Analysis for Ageing Fixed Offshore Wind Turbine Structures

2021 ◽  
Author(s):  
Baran Yeter ◽  
Yordan Garbatov ◽  
Carlos Guedes Soares
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Offshore Wind ◽  
Monitoring Data ◽  
Offshore Wind Turbine ◽  
Support Structures ◽  
Structural Health

Abstract The objective of the present study is to perform a systematic data analysis of structural health monitoring data for ageing fixed offshore wind turbine support structures. The life-cycle extension of the first offshore wind farms is under serious consideration since the support structures are still in a condition to be used further. Big data analytics and machine learning techniques can aid to extract useful information from the monitoring data collected during the service life and build models for future predictions of an optimal life-extension. To this end, it is aimed to analyse the big data provided by embedded control systems and non-destructive inspections of ageing offshore wind turbine support structures using pre-processing techniques, including denoising, detrending, and filtering to remove the noise of different nature and seasonality as well as to detect the signal-specific contents affecting the structural integrity in the time and frequency domain. The effectiveness of the Welch method is investigated in terms of dealing with noisy signals in the frequency domain. Besides, the principal component analysis is carried out to reduce the dimensionality of the data and to select the most significant features that are responsible for most of the variance in the structural health monitoring data. Moreover, nonparametric statistical methods are used to test whether the data before noise being added and the data after cleansing the added noise came from the population with the same distribution. Further, permutation (randomisation) testing is performed to predicate that the results of the nonparametric test are statistically significant. The outcome of this study provides refined evidence that enables to feed the condition monitoring data into the training of the deep neural network to be able to discriminate different structural conditions.

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.

scholarly journals Evaluation of the Wind-Resistant Performance of Long-Span Cable-Stayed Bridge Using the Monitoring Correlation between the Static Cross Wind and Its Displacement Response

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Gaoxin Wang ◽  
Youliang Ding ◽  
Dongming Feng ◽  
Jihong Ye
Keyword(s):  
Health Monitoring ◽  
Wavelet Packet ◽  
Identification Accuracy ◽  
Good Service ◽  
Cable Stayed Bridge ◽  
Long Span ◽  
Cross Correlation Analysis ◽  
Bridge Structures ◽  
Service State ◽  
Static Cross

The wind-resistant performance of existing long-span cable-stayed bridge structures will inevitably deteriorate due to concrete carbonation, reinforcement corrosion, accumulated fatigue damage, etc., which can threaten the entire bridge safety. However, few studies currently focus on assessing the wind-resistant performance of existing long-span cable-stayed bridge structures, and at present, no studies have revealed how to identify the deterioration of the wind-resistant performance using a bridge health-monitoring technique. Therefore, based on the health-monitoring system installed on the Sutong Cable-stayed Bridge, the monitoring wind field and GPS displacement are captured to analyze the wind-resistant performance. First, the correlation between static cross wind and transversal displacement is analyzed, which is nearly linear but also contains discrete points caused by environmental noise. Second, considering that the discrete points can decrease the identification accuracy, one new method called the cross-correlation analysis of wavelet packet coefficients is put forward to effectively remove the discrete points. Third, considering that the traditional function cannot match the monitoring correlation very well, some new fitting functions are thoroughly studied to determine the best function for fitting the monitoring correlation. Fourth, the abnormal variation in the monitoring correlation caused by a deterioration in the wind-resistant performance is studied, and the root-mean-square (RMS) variable, which represents the difference between a good service state and a deteriorated service state, is used as a detection indicator to identify the deterioration of wind-resistant performance. Finally, the monitoring data from ten months are selected to evaluate the wind-resistant performance of the Sutong Bridge, and the result shows that its wind-resistant performance was still in a good service state during these ten months.

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