Health Monitoring and Evaluation of Long-Span Bridges Based on Sensing and Data Analysis: A Survey

In this article, an approach for using structural health monitoring coupled extreme stress data in dynamic extreme stress prediction of steel bridges is presented, where the coupled extreme stress data means the extreme stress data with dynamicity, randomness, and trend. Firstly, the modeling processes about dynamic coupled linear models (DCLM) are provided based on a supposed coupled time series; furthermore, the dynamic probabilistic recursion processes about DCLM are given with Bayes method; secondly, the monitoring dynamic coupled extreme stress data is taken as a time series, historical monitoring coupled extreme stress data-based DCLM and the corresponding Bayesian probabilistic recursion processes are given for predicting bridge extreme stresses; furthermore, the monitoring mechanism is provided for monitoring the prediction precision of DCLM; finally, the monitoring coupled extreme stress data of a steel bridge is used to illustrate the proposed approach which can provide the foundations for bridge reliability prediction and assessment.

show abstract

“…(3) With Bayes method [33,34], (8) and (9), while y t,J � y t is obtained, the posterior PDF about the state at time t can be computed with…”

Section: Bayesian Probabilistic Recursion Of Dlmmentioning

confidence: 99%

“…So far, the researches on SHM have generally experienced two stages. e first stage, falling in the mature stage, is to install an array of sensors for monitoring, collection, and recovery of SHM data during the long-term service periods [4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Incorporation of Structural Health Monitoring Coupled Data in Dynamic Extreme Stress Prediction of Steel Bridges Using Dynamic Coupled Linear Models

Fan

Shang

Yang

et al. 2020

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…As mentioned before, the main goal of a SHM system is to the serviceability, safety, and sustainability of long-span arch bridges. To achieve the goal, a long-term SHM system should include at least five integrated sub-systems [ 30 , 53 ], as shown in Figure 1 .…”

Section: Deployment Of Shm Systems For Long-span Arch Bridgesmentioning

confidence: 99%

Deployment of a Smart Structural Health Monitoring System for Long-Span Arch Bridges: A Review and a Case Study

Chen

Zhou

Wang

et al. 2017

Sensors

View full text Add to dashboard Cite

Structural health monitoring (SHM) technology for surveillance and evaluation of existing and newly built long-span bridges has been widely developed, and the significance of the technique has been recognized by many administrative authorities. The paper reviews the recent progress of the SHM technology that has been applied to long-span bridges. The deployment of a SHM system is introduced. Subsequently, the data analysis and condition assessment including techniques on modal identification, methods on signal processing, and damage identification were reviewed and summarized. A case study about a SHM system of a long-span arch bridge (the Jiubao bridge in China) was systematically incorporated in each part to advance our understanding of deployment and investigation of a SHM system for long-span arch bridges. The applications of SHM systems of long-span arch bridge were also introduced. From the illustrations, the challenges and future trends for development a SHM system were concluded.

show abstract

“…As a paramount monitoring index that reflects the overall stiffness of the structure, deformation is the macro response of the bridge micro complex mechanical mechanism, which contains the internal mechanical evolution information of the structure and affect the safety of bridge structure significantly. Thus, predicting the structural deformation is of great scientific significance and engineering application value in order to give full play to the early warning benefit of the health monitoring system, as well as the structural safety of the bridges [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model

Xin

Zhou

Yang

et al. 2018

Sensors

Self Cite

View full text Add to dashboard Cite

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology.

show abstract

Health Monitoring and Evaluation of Long-Span Bridges Based on Sensing and Data Analysis: A Survey

Cited by 33 publications

References 20 publications

Incorporation of Structural Health Monitoring Coupled Data in Dynamic Extreme Stress Prediction of Steel Bridges Using Dynamic Coupled Linear Models

Incorporation of Structural Health Monitoring Coupled Data in Dynamic Extreme Stress Prediction of Steel Bridges Using Dynamic Coupled Linear Models

Deployment of a Smart Structural Health Monitoring System for Long-Span Arch Bridges: A Review and a Case Study

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model

Contact Info

Product

Resources

About