• Structural Engineering and Mechanics
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• v.15 no.1
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• pp.115-134
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• 2003

The Ting Kau Bridge in Hong Kong is a cable-stayed bridge comprising two main spans and two side spans. The bridge deck is supported by three towers, an end pier and an abutment. Each of the three towers consists of a single reinforced concrete mast which reduces its section in steps, and it is strengthened by transverse cables and struts in the transverse vertical plane. The bridge deck is supported by four inclined planes of cables emanating from anchorages at the tower tops. In view of the threat from typhoons, the dynamic behaviour of long-span cable-supported bridges in the region is always an important consideration in their design. This paper is devoted to the ambient vibration measurements of the bridge for evaluation of dynamic characteristics including the natural frequencies and mode shapes. It also describes the modelling of the bridge. A few finite element models are developed and calibrated to match with the field data and the results of subsequent structural health monitoring of the bridge.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.21-32
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• 2003

Developments and applications of the structural health monitoring (SHM) systems have become active particularity for long-span bridges in Korea. They are composed of sensors, data acquisition system, data transmission system, information processing, damage assessment, and information management. In this paper, current status of research and application activities on SHM systems for civil infra-structures in Korea are briefly introduced by 4 parts: (1) current status of bridge monitoring systems on existing and newly constructed bridges, (2) research and development activities on smart sensors such as optical fiber sensors and piezo-electric sensors, (3) structural damage detection methods using measured data, and (4) a test road project for pavement design verification and enhancement by the Korea Highway Corporation. Finally the R＆D activities of a new engineering research center entitled Smart Infra-Structure Technology Center at Korea Advanced Institute of Science and Technology are also briefly described.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.647-664
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• 2022

The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called Sparse Autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations.

• Structural Engineering and Mechanics
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• v.40 no.4
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• pp.471-488
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• 2011

The Ting Kau Bridge in Hong Kong is a cable-stayed bridge comprising two main spans and two side spans. The bridge deck is supported by three towers, an end pier and an abutment. Each of the three towers consists of a single reinforced concrete mast strengthened by transverse cables and struts. The bridge deck is supported by four inclined planes of cables emanating from anchorages at the tower tops. In view of the heavy traffic on the bridge, and threats from typhoons and earthquakes originated in areas nearby, the dynamic behaviour of long-span cable-supported bridges in the region is always an important consideration in their design. Baseline finite element models of various levels of sophistication have been built not only to match the bridge geometry and cable forces specified on the as-constructed drawings but also to be calibrated using the vibration measurement data captured by the Wind and Structural Health Monitoring System. This paper further describes the analysis of axle loading data, as well as the generation of random axle loads and simulation of vibrations of the bridge using the finite element models. Various factors affecting the vehicular loading on the bridge will also be examined.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.4
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• pp.120-130
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• 2019

In this study, based on the correlation between traffic volume data and vertical displacement data developed in previous research using the bridge maintenance big data of 2006, the vertical displacement estimation model using the traffic volume data of Gwangan Bridge for 10 years A comparison of the performance of the developed model with the current applicability is presented. The present applicability of the developed model is analyzed that the estimated displacement is similar to the actual displacement and that the displacement estimation performance of the model based on the structured regression analysis and the principal component analysis is not significantly different from each other. In conclusion, the vertical displacement estimation model using the traffic volume data developed by this study can be effectively used for the analysis of the behavior according to the traffic load of Gwangan Bridge.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.6
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• pp.283-292
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• 2021

In this study, a structural health monitoring system for cable-stayed bridges is developed. In the system, condition assessment of the structure is performed based on measured records from seismic accelerometers. Response indices are defined to monitor structural safety and serviceability and derived from the measured acceleration data. The derivation process of the indices is structured to follow the transformation from the raw data to the outcome. The process includes noise filtering, baseline correction, numerical integration, and calculation of relative differences. The system is packed as a condition assessment program, which consists of four major processes of the structural health evaluation: (i) format conversion of the raw data, (ii) noise filtering, (iii) generation of response indices, and (iv) condition evaluation. An example set of limit states is presented to evaluate the structural condition of the test-bed and cable-stayed bridge.

• Proceedings of the KSR Conference
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• 2002.10b
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• pp.1074-1079
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• 2002

Long term measurement data on the bridge response caused by moving loads are fundamental ingredient to the development or improvement of the new bridge design. In addition, proper establishment of the systematic analysis and diagnosis together with the maintenance system become the essential procedure to the effective repair/reinforcement/retrofit of not only the high speed but also the conventional railway bridges. Therefore, the real time health monitoring system on the important railway bridges should be enhancing the proper maintenance of the structures. The main objective of this study is, therefore, to develop a monitoring device including Weigh-In-Motion (WIM) function and the emphasis is place on the easy and economic installation of the developed system in the field condition.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Smart Structures and Systems
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• v.15 no.3
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• pp.505-522
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• 2015

Variation of temperature is a primary environmental factor that affects the behavior of structures. Therefore, understanding the mechanisms of normal temperature-induced variations of structural behavior would help in distinguishing them from anomalies. In this study, we used the structural health monitoring data of the Shanghai Yangtze River Bridge, a steel girder cable-stayed bridge, to investigate the mechanisms of thermally induced vertical deflection ($D_T$) at mid-span of such bridges. The $D_T$ results from a multisource combination of thermal expansion effects of the cable temperature ($T_{Cab}$), girder temperature ($T_{Gir}$), girder differential temperature ($T_{Dif}$), and tower temperature ($T_{Tow}$). It could be approximated by multiple linear superpositions under operational conditions. The sensitivities of $D_T$ of the Shanghai Yangtze River Bridge to the above temperatures were in the following order: $T_{Cab}$ > $T_{Gir}$ > $T_{Tow}$ > $T_{Dif}$. However, the direction of the effect of $T_{Cab}$ was observed to be opposite to that of the other three temperatures, and the magnitudes of the effects of $T_{Cab}$ and $T_{Gir}$ were found to be almost one order greater than those of $T_{Dif}$ and $T_{Tow}$. The mechanisms of the thermally induced vertical deflection variation at mid-span of a cable-stayed bridge as well as the analytical methodology adopted in this study could be applicable for other long-span cable-stayed bridges.

• Wind and Structures
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• v.6 no.3
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• pp.179-196
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• 2003

A long suspension bridge is often located within a unique wind environment, and strong winds at the site seldom attack the bridge at a right angle to its long axis. This paper thus investigates the buffeting response of long suspension bridges to skew winds. The conventional buffeting analysis in the frequency domain is first improved to take into account skew winds based on the quasi-steady theory and the oblique strip theory in conjunction with the finite element method and the pseudo-excitation method. The aerodynamic coefficients and flutter derivatives of the Tsing Ma suspension bridge deck under skew winds, which are required in the improved buffeting analysis, are then measured in a wind tunnel using specially designed test rigs. The field measurement data, which were recorded during Typhoon Sam in 1999 by the Wind And Structural Health Monitoring System (WASHMS) installed on the Tsing Ma Bridge, are analyzed to obtain both wind characteristics and buffeting responses. Finally, the field measured buffeting responses of the Tsing Ma Bridge are compared with those from the computer simulation using the improved method and the aerodynamic coefficients and flutter derivatives measured under skew winds. The comparison is found satisfactory in general.