• Journal of the Korean Society for Nondestructive Testing
• /
• v.22 no.3
• /
• pp.239-245
• /
• 2002

Many civil and infrastructures continue to be used despite aging and the associated potential for damage accumulation. Therefore, the ability to monitor the health of these systems is becoming increasingly important. The purpose of this paper is to propose a real-time health monitoring system of cable-stayed bridge, based-on non-destructive measurement. And also this paper focuses on the safety assessment for bridge from health monitoring system to accomplish this safety assesment. Using the proposed health monitoring system, it helps bridge maintenance and reduces the economic cost of a life-cycle costs. Also it give important data to develop the design and analysis method for cable-stayed bridges.

• Smart Structures and Systems
• /
• v.31 no.2
• /
• pp.183-197
• /
• 2023

For long-span bridges with a structural health monitoring (SHM) system, environmental temperature-driven responses are proved to be a main component in measurements. However, anomalous structural behavior may be hidden incomplicated recorded data. In order to receive reliable assessment of structural performance, it is important to study therelationship between temperature and monitoring data. This paper presents an application of the cointegration based methodology to detect anomalies that may be masked by temperature effects and then forecast the temperature-induced deflection (TID) of long-span suspension bridges. Firstly, temperature effects on girder deflection are analyzed with fieldmeasured data of a suspension bridge. Subsequently, the cointegration testing procedure is conducted. A threshold-based anomaly detection framework that eliminates the influence of environmental temperature is also proposed. The cointegrated residual series is extracted as the index to monitor anomaly events in bridges. Then, wavelet separation method is used to obtain TIDs from recorded data. Combining cointegration theory with autoregressive moving average (ARMA) model, TIDs for longspan bridges are modeled and forecasted. Finally, in-situ measurements of Xihoumen Bridge are adopted as an example to demonstrate the effectiveness of the cointegration based approach. In conclusion, the proposed method is practical for actual structures which ensures the efficient management and maintenance based on monitoring data.

• Smart Structures and Systems
• /
• v.13 no.6
• /
• pp.1015-1039
• /
• 2014

The widening project on Freeway No.1 in Taiwan has a total length of roughly 14 kilometers, and includes three special bridges, namely a 216 m long-span bridge crossing the original freeway, an F-bent double decked bridge in a co-constructed section, and a steel and prestressed concrete composite bridge. This study employed in-situ monitoring in conjunction with numerical modeling to establish a real-time monitoring system for the three bridges. In order to determine the initial static and dynamic behavior of the real bridges, forced vibration experiments, in-situ static load experiments, and dynamic load experiments were first carried out on the newly-constructed bridges before they went into use. Structural models of the bridges were then established using the finite element method, and in-situ vehicle load weight, arrangement, and speed were taken into consideration when performing comparisons employing data obtained from experimental measurements. The results showed consistency between the analytical simulations and experimental data. After determining a bridge's initial state, the proposed in-situ monitoring system, which is employed in conjunction with the established finite element model, can be utilized to assess the safety of a bridge's members, providing useful reference information to bridge management agencies.

• Wind and Structures
• /
• v.12 no.6
• /
• pp.479-504
• /
• 2009

Structural health monitoring (SHM) systems have been recently embraced in long span cable-supported bridges, in which buffeting-induced stress monitoring is one of the tasks to ensure the safety of the bridge under strong winds. In line with this task, this paper presents a SHM-oriented finite element model (FEM) for the Tsing Ma suspension bridge in Hong Kong so that stresses/strains in important bridge components can be directly computed and compared with measured ones. A numerical procedure for buffeting induced stress analysis of the bridge based on the established FEM is then presented. Significant improvements of the present procedure are that the effects of the spatial distribution of both buffeting forces and self-excited forces on the bridge deck structure are taken into account and the local structural behaviour linked to strain/stress, which is prone to cause local damage, are estimated directly. The field measurement data including wind, acceleration and stress recorded by the wind and structural health monitoring system (WASHMS) installed on the bridge during Typhoon York are analyzed and compared with the numerical results. The results show that the proposed procedure has advantages over the typical equivalent beam finite element models.

• Smart Structures and Systems
• /
• v.17 no.3
• /
• pp.445-470
• /
• 2016

In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2009.03a
• /
• pp.38-47
• /
• 2009

Scour means the erosion of bed material by flow change when a bridge is constructed in a stream. Scour is one of the critical factors of a bridge failure. There are several methods for the monitoring of scour near bridge foundations; Sounding rods, Magnetic sliding collar System, Sonar system, underwater camera system and so on. In general, Sonar system is preferred due to its convenience and good accuracy. In this study, the new scour monitoring system was developed using profiling sonar sensor. The new system can measure a line profile of a seabed and has small size due to the effectively designed data logger. The performance of the new scour monitoring system was evaluated at a bridge pier in tidal environment. The measured local scour depths were discussed with the result of the empirical formulas; CSU, Froehlich, Laursen and Neill.

• Structural Monitoring and Maintenance
• /
• v.1 no.1
• /
• pp.69-87
• /
• 2014

A new output only modal analysis method is developed in this paper. This method uses continuous wavelet transform to modify a popular blind source separation algorithm, second order blind identification (SOBI). The wavelet modified SOBI (WMSOBI) method replaces original time domain signal with selected time-frequency domain wavelet coefficients, which overcomes the shortcomings of SOBI. Both numerical and experimental studies on bridge models are carried out when there are limited number of sensors. Identified modal properties from WMSOBI are analyzed and compared with fast Fourier transform (FFT), SOBI and eigensystem realization algorithm (ERA). The comparison shows WMSOBI can identify as many results as FFT and ERA. Further case study of structural health monitoring (SHM) on an arch bridge verifies the capability to detect damages by combining WMSOBI with incomplete flexibility difference method.

• Structural Engineering and Mechanics
• /
• v.23 no.1
• /
• pp.75-95
• /
• 2006

Two-step identification approaches for effective bridge health monitoring are proposed to alleviate the issues associated with many unknown parameters faced in real structures and to improve the accuracy in the estimate results. It is suitable for on-line monitoring scheme, since the damage assessment is not always needed to be carried out whereas the alarming for damages is to be continuously monitored. In the first step for screening potentially damaged members, a damage indicator method based on modal strain energy, probabilistic neural networks and the conventional neural networks using grouping technique are utilized and then the conventional neural networks technique is utilized for damage assessment on the screened members in the second step. The effectiveness of the proposed methods is investigated through a field test on the northern-most span of the old Hannam Grand Bridge over the Han River in Seoul, Korea.

• Smart Structures and Systems
• /
• v.4 no.6
• /
• pp.711-726
• /
• 2008

A number of efforts had been sought to instrument bridges for the purpose of structural monitoring and assessment. The outcome of these efforts, as gauged by advances in the understanding of the definition of structural damage and their role in sensor selection as well as in the design of cost and data-effective monitoring systems, has itself been difficult to assess. The authors' experience with the design, calibration, and operation of a monitoring system for the Kishwaukee Bridge in Illinois has provided several lessons that bear upon these concerns. The systems have performed well in providing a continuous, low-cost monitoring platform for bridge engineers with immediate relevant information.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2007.04a
• /
• pp.90-95
• /
• 2007

Conventional bridge inspection involves the physical positioning of an inspector by the hydraulic telescoping boom of a "snooper truck" thereby providing visual access to bridge components. The process is time consuming, hazardous, and may be affected by lighting conditions, Therefore, it is of great interest that an automated and/or teleoperated inspection robot be developed to replace the manual inspection procedure. This paper describes the advanced bridge inspection robot system under development and other related activities currently undergoing at the Bridge Inspection Robot Development Interface (BIRDI). BIRDI is a research consortium with its home in the Department of Civil and Environmental System Engineering at Hanyang University at Ansan. Its primary goal is to develop advanced robot systems for bridge inspection and monitoring for immediate field application and commercialization. The research program includes research areas such as advanced inspection robot and motion control system, sensing technologies for monitoring and assessment, and integrated system for bridge maintenance. The center embraces 12 institutions, which consist of 7 universities, 2 research institutes, and 3 private enterprises. Research projects are cross-disciplinary and include experts from structural engineering, mechanical engineering, electronic and control engineering. This research project will contribute to advancement of infrastructure maintenance technology, enhancement of construction industry competitiveness, and promotion of national capacity for technology innovation.