• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.2
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• pp.145-155
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• 2002

It is essential for health monitoring of a cable-stayed bridge to provide more accurate and enough information from the sensors. In experimental modal testing, the chosen measurement locations and the number of measurements have a major influence on the quality of the results. The choice is often difficult for complex structures like a cable-stayed bridge. It is extremely important a cable-stayed bridge to minimize the number of sensing operations required to monitor the structural system. In order to obtain the desired accuracy for the structural test, several issues must take into consideration. Two important issues are the number and location of response sensors. There are usually several alternative locations where different sensors can be located. On the other hand, the number of sensors might be limited due to economic constraints. Therefore, techniques such as methodologies, algorithms etc., which address the issue of limited instrumentation and its effects on resolution and accuracy in health monitoring systems are paramount to a damage diagnosis approach. This paper discusses an optimum sensor placement criterion suitable to the identification of structural damage for continuous health monitoring. A Kinetic Energy optimization technique and an Effective Independence Method are analyzed and numerical and theoretical issues are addressed for a cable-stayed bridge. Its application to a cable-stayed bridge is discussed to optimize the sensor placement for identification and control purposes.

• Smart Structures and Systems
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• v.13 no.5
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• pp.755-773
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• 2014

The natural frequency of a bridge is an important parameter in many engineering applications such as bridge seismic design and modal-based bridge health monitoring. The natural frequency of a bridge vibrating alone may differ from that vibrating along with a vehicle. Although such vehicle-induced variability in bridge frequency is revealed in several experimental and numerical simulation studies, few attempts have been made on the theoretical descriptions. In this study, both theoretically and experimentally, the variability in the bridge frequency induced by a parked vehicle is verified, and is therefore suggested to be considered in bridge-related engineering, especially for those cases with near vehicle-bridge resonance conditions or with large vehicle-to-bridge mass ratios. Moreover, the variability ranges could be estimated by an analytical formula presented herein.

• Journal of Korean Society of societal Security
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• v.2 no.4
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• pp.35-41
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• 2009

If any bridge has the monitoring system, the bridge manager can check the history of bridge behavior and the progress of the damage more exactly. When the unexpected event (ex: earthquake and flood) happens, the manager can check the safety condition of the bridge and make the pertinent action for bridge management which is reduction of vehicle speed or traffic control through the system. Additionary the manager can make the well-timed repair or reinforcement through the system, so he can save the management cost or the life cycle cost. This study presents the method of setting the criteria by FEM analysis in bridge monitoring system, and the standard progress for setting the criteria about measurement items of monitoring system for the steel box type bridge.

• Smart Structures and Systems
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• v.24 no.6
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

• Smart Structures and Systems
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• v.26 no.5
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• pp.667-680
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• 2020

This study aims at reporting a systematic procedure for evaluating the static and dynamic structural performance of steel bridges based on a short-period structural health monitoring measurement. Sungsu bridge located in Korea is considered as a case study presenting the most recent tests carried out to examine the bridge condition. Short-period measurements of Structural Health Monitoring (SHM) system were used during the bridge testing phase. A novel symmetry index is introduced using statistical analyses of deflection and strain measurements. Frequency Domain Decomposition (FDD) is implemented to the strain measurements to estimate the bridge mode shapes and damping ratios. Furthermore, Markov Chain Monte Carlo (MCMC) is also implemented to examine the reliability of bridge performance while ambient design trucks are in static or moving at different speeds. Strain, displacement and acceleration were measured at selected locations on the bridge. The results show that the symmetry index can be an efficient and useful measure in assessing the steel bridge performance. The results from the used method reveal that the performance of the Sungsu bridge is safe under operational conditions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.3
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• pp.377-383
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• 2010

This study aims to develop the system which is able to monitor long span bridge behavior in real time using GPS. Through measuring displacement of long span bridge by GPS in real time, over all 3D behavior of bridge could be analyzed and managed. Monitoring system of long span bridge which is developed in this study is able to manage in real time the safety of bridge by transmitting horizontal and vertical displacement of bridge, and danger signals to an integrated operations center. Also it is able to monitor the absolute behavior of long span bridge by GPS, and to construct a national bridge safety management networks.

• Earthquakes and Structures
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• v.25 no.1
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• pp.69-78
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• 2023

Life-cycle performance analysis of a reinforced concrete box section bridge was generated. Moreover, Monte Carlo simulation with important sampling (IS) was used to simulate the bridge material and load uncertainties. The bridge deterioration model was generated with the basic probabilistic principles and updated according to the measurement data. A genetic algorithm (GA) with the response surface model (RSM) was used to determine the deterioration rate. The importance of health monitoring systems to sustain the bridge to give services economically and reliably and the advantages of fiber-optic sensors for SHM applications were discussed in detail. This study showed that the most effective loss of strength in reinforced concrete box section bridges is corrosion of the reinforcements. Due to reinforcement corrosion, the use of the bridge, which was examined, could not meet the desired strength performance in 25 years, and the need for reinforcement. In addition, it has been determined that long-term health monitoring systems are an essential approach for bridges to provide safe and economical service. Moreover the use of fiber optic sensors has many advantages because of the ability of the sensors to be resistant to environmental conditions and to make sensitive measurements.

• Smart Structures and Systems
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• v.21 no.5
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.79-92
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• 2018

The evaluation theory of reliability, availability, maintainability and safety (RAMS) as a mature theory of state evaluation in the railway engineering, can be well used to the evaluation, management, and maintenance of complicated structure like the long-span bridge structures on the high-speed railway. Taking a typical steel-truss arch bridge on the Beijing-Shanghai high-speed railway, the Nanjing Dashengguan Yangtze River Bridge, this paper developed a new method of state evaluation for the existing steel-truss arch high-speed railway bridge. The evaluation framework of serving state for the bridge structure is presented based on the RAMS theory. According to the failure-risk, safety/availability, maintenance of bridge members, the state evaluation method of each monitoring item is presented. The weights of the performance items and the monitoring items in all evaluation levels are obtained using the analytic hierarchy process. Finally, the comprehensive serving state of bridge structure is hierarchical evaluated.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.439-459
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• 2010

Structural health monitoring (SHM) of civil infrastructure using wireless smart sensor networks (WSSNs) has received significant public attention in recent years. The benefits of WSSNs are that they are low-cost, easy to install, and provide effective data management via on-board computation. This paper reports on the deployment and evaluation of a state-of-the-art WSSN on the new Jindo Bridge, a cable-stayed bridge in South Korea with a 344-m main span and two 70-m side spans. The central components of the WSSN deployment are the Imote2 smart sensor platforms, a custom-designed multimetric sensor boards, base stations, and software provided by the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. In total, 70 sensor nodes and two base stations have been deployed to monitor the bridge using an autonomous SHM application with excessive wind and vibration triggering the system to initiate monitoring. Additionally, the performance of the system is evaluated in terms of hardware durability, software stability, power consumption and energy harvesting capabilities. The Jindo Bridge SHM system constitutes the largest deployment of wireless smart sensors for civil infrastructure monitoring to date. This deployment demonstrates the strong potential of WSSNs for monitoring of large scale civil infrastructure.