Switching Bayesian dynamic linear model for condition assessment of bridge expansion joints using structural health monitoring data

2021 ◽  
Vol 160 ◽  
pp. 107879
Author(s):  
Yi-Ming Zhang ◽  
Hao Wang ◽  
Yu Bai ◽  
Jian-Xiao Mao ◽  
Xiang-Yu Chang ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Linear Model ◽  
Health Monitoring ◽  
Condition Assessment ◽  
Monitoring Data ◽  
Dynamic Linear Model ◽  
Expansion Joints ◽  
Structural Health

Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model

2020 ◽  
pp. 147592172097702
Author(s):  
Yi-Ming Zhang ◽  
Hao Wang ◽  
Hua-Ping Wan ◽  
Jian-Xiao Mao ◽  
Yi-Chao Xu
Keyword(s):  
Structural Health Monitoring ◽  
Linear Model ◽  
Health Monitoring ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Monitoring Data ◽  
Model Parameters ◽  
Dynamic Linear Model ◽  
Structural Health ◽  
Log Likelihood

Enormous data are continuously collected by the structural health monitoring system of civil infrastructures. The structural health monitoring data inevitably involve anomalies caused by sensors, transmission errors, or abnormal structural behaviors. It is important to identify the anomalies and find their origin (e.g. sensor fault or structural damage) to make correct interventions. Moreover, online anomaly identification of the structural health monitoring data is critical for timely structural condition assessment and decision-making. This study proposes an online approach for detecting anomalies of the structural health monitoring data based on the Bayesian dynamic linear model. In particular, Bayesian dynamic linear model, consisting of various components, is implemented to characterize the feature of real-time measurements. Expectation maximization algorithm and Kalman smoother are combined to estimate the Bayesian dynamic linear model parameters and generate log-likelihood functions. The subspace identification method is introduced to overcome the initialization issue of the expectation maximization algorithm. The log-likelihood difference of consecutive time steps is then used to determine thresholds without introducing extra anomaly detectors. The proposed Bayesian dynamic linear model-based approach is first illustrated by the simulation data and then applied to the structural health monitoring data collected from two long-span bridges. The results indicate that the proposed method exhibits good accuracy and high computational efficiency and also allows for reconstructing the strain measurements to replace anomalies.

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>

Sign in / Sign up

Export Citation Format

Share Document