Research and application of real time evaluation method of shield method tunnel structural health based on automated monitoring data analysis

2021 ◽  
Author(s):  
Chuangfeng Duan
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Evaluation Method ◽  
Monitoring Data ◽  
Automated Monitoring ◽  
Structural Health ◽  
Time Evaluation

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>

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.

scholarly journals Real-Time Evaluation Method of Vehicle Conflict Risk on Urban Expressway Based on Smartphone GPS Data

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Le-Ning Wang
Keyword(s):  
Real Time ◽  
Traffic Safety ◽  
Evaluation System ◽  
Evaluation Method ◽  
Angular Acceleration ◽  
Abnormal Behavior ◽  
Gps Data ◽  
Urban Expressway ◽  
Conflict Risk ◽  
Time Evaluation

To effectively evaluate the traffic safety risk of urban expressways in real time and ensure their traffic safety and smoothness, a real-time evaluation method of vehicle conflict risk of an urban expressway based on smartphone GPS data was proposed. We screened and processed smartphone GPS data to obtain vehicle behavior data, including acceleration and angular acceleration, and road state data, including average vehicle speed. Urban expressways were divided into four categories, closed straight section, closed curve section, vehicle entry section, and vehicle exit section; the evaluation indexes of abnormal vehicle behavior were established. Based on the improved entropy weight method, the vehicle conflict risk entropy was established to distribute the weight of different types of abnormal behaviors of vehicles. The evaluation system of vehicle conflict risk entropy was applied to the vehicle behavior data. Urban Expressways with more abnormal vehicle behavior were obtained to evaluate the risk of vehicle conflict in real time. The results showed that the easily obtained smartphone GPS data may be effectively used to analyze the abnormal behavior of vehicles, identify vehicle conflict risk points hidden in urban expressways in real time to provide effective methods for batch and dynamic real-time evaluations of vehicle conflict risks on urban expressways, and improve the traffic safety service level of urban expressways.

Real-time quantitative evaluation on the cable damage of cable-stayed bridges using the correlation between girder deflection and temperature

2021 ◽  
pp. 147592172110350
Author(s):  
Gaoxin Wang ◽  
Jingshu Shao ◽  
Weizhou Xu ◽  
Zhaoxing Dong ◽  
Bin Chen ◽  
...  
Keyword(s):  
Data Analysis ◽  
Correlation Analysis ◽  
Real Time ◽  
Quantitative Evaluation ◽  
Damage Index ◽  
Monitoring Data ◽  
Damage Evaluation ◽  
Stay Cable ◽  
Cable Damage ◽  
Cable Stayed Bridges

Stayed cable is an important prestress-bearing component in cable-stayed bridges, and the cable damage will seriously threaten bridge safety. In this research, the method of real-time quantitative evaluation on cable damage is proposed through monitoring data analysis, correlation analysis, damage evaluation analysis, and validation analysis. Monitoring data analysis shows that temperature has a good linear relationship with girder deflection and cable force. Correlation analysis shows that this relationship is well described by a time-varying multiple linear regression model. In damage evaluation analysis, a new damage index is proposed for real-time quantitative evaluation. Each stay cable has a corresponding damage index, and a large value of damage index indicates a serious damage. The results of experiment and finite element analysis show that the evaluation error of this damage index is very small, which is feasible for real-time quantitative evaluation. This method can provide valuable reference for real-time quantitative evaluation on cable damage of cable-stayed bridges.

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