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

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.586-589
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• 2006

Recently, the application area of wireless LAN(Internet) and CDMA have been increased, rapidly. Bridge monitoring system using this ubiquitous Computing Technology can bring more convenience of bridge maintenance and management. This paper present a automatic data acquisition, control and processing technology through this concept, and check system applicability to the concrete bridge completed. Finally, The preventive bridge monitoring through the application of this system will progress in technology and make civil infrastructure more safe and useful.

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.3
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• pp.194-204
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• 1998

This study was performed to suggest the proper maintenance method for the deteriorated gerber type PC box girder bridge by using the long term displacement monitoring data. For this study, the monitoring system which can measure the long term displacement and the concrete surface temperature was designed and operated. From the measurement and structural analysis results, the cause of the permanent deformation which the bridge has already was estimated, and based on this result, the allowable permanent displacement value at the hinge was suggested. From this study, it was known that the long term monitoring system can be applied to the active maintenance of the deteriorated bridge and the suggested allowable permanent displacement could be used for the maintenance of the bridge.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.3
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• pp.526-531
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• 2003

In this paper, we implement embedded Linux intelligent controller and remote monitoring system for Bridge Diagnosis. Embedded controller as the hard core is consisted of 32 bit CPU and is designed to have processing of real time monitoring and FFT for Bridge Diagnosis. The prototype monitoring system can operate with world wide web in GUI environment by Java. Detailed design and functional analysis for monitoring system are performed by systems approach.

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

• Smart Structures and Systems
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• v.20 no.2
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• pp.197-205
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• 2017

The dynamic performance of railway bridges under high-speed trains draws the attention of bridge engineers. The vibration issue for long-span bridges under high-speed trains is still not well understood due to lack of validations through structural health monitoring (SHM) data. This paper investigates the correlation between bridge acceleration and train speed based on structural dynamics theory and SHM system from three foci. Firstly, the calculated formula of acceleration response under a series of moving load is deduced for the situation that train length is near the length of the bridge span, the correlation between train speed and acceleration amplitude is analyzed. Secondly, the correlation scatterplots of the speed-acceleration is presented and discussed based on the transverse and vertical acceleration response data of Dashengguan Yangtze River Bridge SHM system. Thirdly, the warning indexes of the bridge performance for correlation scatterplots of speed-acceleration are established. The main conclusions are: (1) The resonance between trains and the bridge is unlikely to happen for long-span bridge, but a multimodal correlation curve between train speed and acceleration amplitude exists after the resonance speed; (2) Based on SHM data, multimodal correlation scatterplots of speed-acceleration exist and they have similar trends with the calculated formula; (3) An envelope line of polylines can be used as early warning indicators of the changes of bridge performance due to the changes of slope of envelope line and peak speed of amplitude. This work also gives several suggestions which lay a foundation for the better design, maintenance and long-term monitoring of a long-span high-speed bridge.

• 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.

• Smart Structures and Systems
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• v.19 no.3
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• pp.259-268
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• 2017

System identification of structures is one of the important aspects of structural health monitoring. The accuracy and efficiency of identification results is affected severely by measurement noises, especially when the structure system is large, such as bridge structures, and when online system identification is required. In this paper, the least square estimation (LSE) method is used combined with the substructure approach for identifying structural parameters of a cable-stay bridge with large degree of freedoms online. Numerical analysis is carried out by first dividing the bridge structure into smaller substructures and then estimates the parameters of each substructure online using LSE method. Simulation results demonstrate that the proposed approach is capable of identifying structural parameters, however, the accuracy and efficiency of identification results depend highly on the noise sensitivities of loading region, loading pattern as well as element size.