• Smart Structures and Systems
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• v.11 no.4
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• pp.331-348
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• 2013

Effective monitoring, reliable data analysis, and rational data interpretations are challenges for engineers who are specialized in bridge health monitoring. This paper demonstrates how to use the Global Positioning System (GPS) and accelerometer data to accurately extract static and quasi-static displacements of the bridge induced by ambient effects. To eliminate the disadvantages of the two separate units, based on the characteristics of the bias terms derived from the GPS and accelerometer respectively, a wavelet based multi-step filtering method by combining the merits of the continuous wavelet transform (CWT) with the discrete stationary wavelet transform (SWT) is proposed so as to address the GPS deformation monitoring application more efficiently. The field measurements are carried out on an existing suspension bridge under the normal operation without any traffic interference. Experimental results showed that the frequencies and absolute displacements of the bridge can be accurate extracted by the proposed method. The integration of GPS and accelerometer can be used as a reliable tool to characterize the dynamic behavior of large structures such as suspension bridges undergoing environmental loads.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.221-230
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• 2016

As structures' long term performances and users' safety have been highlighted, a new maintenance technique using IT has drawn attention around the globe. Therefore, throughout the paper, by analyzing bridge's static and dynamic data using wireless measuring sensor, a "real time smart bridge monitoring system" has developed. Smart bridge monitoring system is consists of three main parts a sensor that can measure major members' movement, a wireless system that informs the data from the sensor, and the database system that analysis the data. In order to test the performance of the system, five different were placed on the Olympic Bridge, Seoul. The power system of the sensors was replaced by self-sustain solar energy system. In order to validate data from the real time smart bridge monitoring system, the data was collected for a week from both wireless system and the wired system and the two data were compared to see the relevance.

• Structural Monitoring and Maintenance
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• v.1 no.1
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• pp.47-67
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• 2014

This study has been motivated to examine the performance of a wireless sensor system under the typhoons as well as to analyze the effect of the typhoons on the bridge's vibration responses and the variation of cable forces. During the long-term field experiment on a real cable-stayed bridge in years 2011-2012, the bridge had experienced two consecutive typhoons, Bolaven and Tembin, and the wireless sensor system had recorded data of wind speeds and vibration responses from a few survived sensor nodes. In this paper, the wireless structural health monitoring of stay cables under the two consecutive typhoons is presented. Firstly, the wireless monitoring system for cable-stayed bridge is described. Multi-scale vibration sensor nodes are utilized to measure both acceleration and PZT dynamic strain from stay cables. Also, cable forces are estimated by a tension force monitoring software based on vibration properties. Secondly, the cable-stayed bridge with the wireless monitoring system is described and its wireless monitoring capacities for deck and cables are evaluated. Finally, the structural health monitoring of stay cables under the attack of the two typhoons is described. Wind-induced deck vibration, cable vibration and cable force variation are examined based on the field measurements in the cable-stayed bridge under the two consecutive typhoons.

• Smart Structures and Systems
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• v.14 no.5
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• pp.917-942
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• 2014

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

• Structural Monitoring and Maintenance
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• v.1 no.3
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• pp.249-271
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• 2014

This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges.

• Smart Structures and Systems
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• v.21 no.5
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• pp.695-703
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• 2018

Based on monitoring data collected from the Nanjing Dashengguan Bridge over the last five years, this paper systematically investigates the effects of temperature field and train loadings on the structural responses of this long-span high-speed railway bridge, and establishes the early warning thresholds for various structural responses. Then, some lessons drawn from the structural health monitoring system of this bridge are summarized. The main context includes: (1) Polynomial regression models are established for monitoring temperature effects on modal frequencies of the main girder and hangers, longitudinal displacements of the bearings, and static strains of the truss members; (2) The correlation between structural vibration accelerations and train speeds is investigated, focusing on the resonance characteristics of the bridge at the specific train speeds; (3) With regard to various static and dynamic responses of the bridge, early warning thresholds are established by using mean control chart analysis and probabilistic analysis; (4) Two lessons are drawn from the experiences in the bridge operation, which involves the lacks of the health monitoring for telescopic devices on the beam-end and bolt fractures in key members of the main truss.

• Smart Structures and Systems
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• v.15 no.2
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• pp.395-408
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• 2015

This study aims to propose a Bayesian approach to consider changes in temperature and vehicle weight as environmental and operational factors for vibration-based long-term bridge health monitoring. The Bayesian approach consists of three steps: step 1 is to identify damage-sensitive features from coefficients of the auto-regressive model utilizing bridge accelerations; step 2 is to perform a regression analysis of the damage-sensitive features to consider environmental and operational changes by means of the Bayesian regression; and step 3 is to make a decision on the bridge health condition based on residuals, differences between the observed and predicted damage-sensitive features, utilizing 95% confidence interval and the Bayesian hypothesis testing. Feasibility of the proposed approach is examined utilizing monitoring data on an in-service bridge recorded over a one-year period. Observations through the study demonstrated that the Bayesian regression considering environmental and operational changes led to more accurate results than that without considering environmental and operational changes. The Bayesian hypothesis testing utilizing data from the healthy bridge, the damage probability of the bridge was judged as no damage.

• Journal of Ocean Engineering and Technology
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• v.22 no.5
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• pp.142-147
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• 2008

The cases of using new methods of big blocks are largely increasing on Recent large-scale bridge structures. So the accurate data of responses of bridges following environmental causes are required to be quickly recorded in order to predict. For this reason described above, the research on measuring system should be conducted for more knowledge of the details on application and stability of new methods. In this study, the new health monitoring system that can monitor the real behavior and damages of the bridge during all processes of construction is presented by analyzing cases of domestic and overseas bridge health monitoring system, and applied methods of following bridges.

• Earthquakes and Structures
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• v.2 no.2
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• pp.189-206
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• 2011

In this study, the subspace stochastic realization theories (SSR model I and SSR model II) have been applied to a real bridge for estimating its dynamic characteristics (natural frequencies, damping constants, and vibration modes) under ambient vibration. A numerical simulation is carried out for an arch-type steel truss bridge using a white noise excitation. The estimates obtained from this simulation are compared with those obtained from the Finite Element (FE) analysis, demonstrating good agreement and clarifying the excellent performance of this method in estimating the structural dynamic characteristics. Subsequently, these methods are applied to the vibration induced by both strong and weak winds as obtained by remote monitoring of the Kabashima bridge (an arch-type steel truss bridge of length 136 m, and situated in Nagasaki city). The results obtained with this experimental data reveal that more accurate estimates are obtained when strong wind vibration data is used. In contrast, the vibration data obtained from weak wind provides accurate estimates at lower frequencies, and inaccurate accuracy for higher modes of vibration that do not get excited by the wind of lower intensity. On the basis of the identified results obtained using both simulated data and monitored data from a real bridge, it is determined that the SSR model II realizes more accurate results than the SSR model I. In general, the approach investigated in this study is found to provide acceptable estimates of the dynamic characteristics of highway bridges as well as for the vibration monitoring of bridges.

• Smart Structures and Systems
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• v.18 no.2
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• pp.317-334
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• 2016

Structural Health Monitoring System (SHMS) works as an efficient platform for monitoring the health status and performance deterioration of engineering structures during long-term service periods. The objective of its installation is to provide reasonable suggestions for structural maintenance and management, and therefore ensure the structural safety based on the information extracted from the real-time measured data. In this paper, the SHMS implemented on a world-famous kilometer-level cable-stayed bridge, named as Sutong Cable-stayed Bridge (SCB), is introduced in detail. The composition and core functions of the SHMS on SCB are elaborately presented. The system consists of four main subsystems including sensory subsystem, data acquisition and transmission subsystem, data management and control subsystem and structural health evaluation subsystem. All of the four parts are decomposed to separately describe their own constitutions and connected to illustrate the systematic functions. Accordingly, the main techniques and strategies adopted in the SHMS establishment are presented and some extension researches based on structural health monitoring are discussed. The introduction of the SHMS on SCB is expected to provide references for the establishment of SHMSs on long-span bridges with similar features as well as the implementation of potential researches based on structural health monitoring.