• Smart Structures and Systems
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• v.15 no.3
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• pp.555-576
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• 2015

The large number of long-span bridges constructed in China motivates the applications of structural health monitoring (SHM) technology. Many bridges have been equipped with sophisticated SHM systems in the mainland of China and in Hong Kong of China. Recently, SHM technology has been extended to field test systems. In this view, SHM can serve as a tool to develop the methods of life-cycle performance design, evaluation, maintenance and management of bridges; to develop new structural analysis methods through validation and feedback from SHM results; and to understand the behavior of bridges under natural and man-made disasters, rapidly assess the damage and loss of structures over large regions after disasters, e.g., earthquake, typhoon, flood, etc. It is hoped that combining analytical methods, numerical simulation, small-scale tests and accelerated durability tests with SHM could become the main engine driving the development of bridge engineering. This paper demonstrates the above viewpoint.

• Smart Structures and Systems
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• v.6 no.8
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• pp.939-951
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• 2010

A low-cost wireless sensor network (WSN) solution with highly expandable super and simple nodes was developed. The super node was designed as a sensing unit as well as a receiving terminal with low energy consumption. The simple node was designed to serve as a cheaper alternative for large-scale deployment. A 12-bit ADC inputs and DAC outputs were reserved for sensor boards to ease the sensing integration. Vibration and thermal field tests of the Chi-Lu Bridge were conducted to evaluate the WSN's performance. Integral acceleration, temperature and tilt sensing modules were constructed to simplify the task of long-term environmental monitoring on this bridge, while a star topology was used to avoid collisions and reduce power consumption. We showed that, given sufficient power and additional power amplifier, the WSN can successfully be active for more than 7 days and satisfy the half bridge 120-meter transmission requirement. The time and frequency responses of cables shocked by external force and temperature variations around cables in one day were recorded and analyzed. Finally, guidelines on power characterization of the WSN platform and selection of acceleration sensors for structural health monitoring applications were given.

• Structural Monitoring and Maintenance
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• v.10 no.1
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• pp.87-105
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• 2023

Seismic regulations have been updated from time to time to accommodate an increase in seismic hazards. Comparison of seismic fragility of the existing bridges in Indonesia from different historical periods since the era before 1990 will be the basis for seismic assessment of the bridge stock in Indonesia, most of which are located in earthquake-prone areas, especially those built many years ago with outdated regulations. In this study, seismic fragility curves were developed using incremental non-linear time history analysis and more holistically according to the actual strength of concrete and steel material in Indonesia to determine the uncertainty factor of structural capacity, βc. From the research that has been carried out, based on the current seismic load in SNI 2833:2016/Seismic Map 2017 (7% probability of exceedance in 75 years), the performance level of the bridge in the era before SNI 2833:2016 was Operational-Life Safety whereas the performance level of the bridge designed with SNI 2833:2016 was Elastic - Operational. The potential for more severe damage occurs in greater earthquake intensity. Collapse condition occurs at As = F_PGA x PGA value of bridge Era I = 0.93 g; Era II = 1.03 g; Era III = 1.22 g; Era IV = 1.54 g. Furthermore, the fragility analysis was also developed with geometric variations in the same bridge class to see the effect of these variations on the fragility, which is the basis for making bridge risk maps in Indonesia.

• Corrosion Science and Technology
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• v.3 no.2
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• pp.39-46
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• 2004

Development and application of structural health monitoring system in Korea have become active since the early 1990's. In earlier applications, health monitoring systems were installed in several existing bridges in order to collect initial field data by full scale load capacity test for design verification and subsequently monitor long-term performance and durability of the bridge as part of an inspection and maintenance program. Recently, modem and integrated monitoring systems have been introduced in most of the newly constructed long-span bridges since the design stage. This paper outlines the progresses and applications of monitoring systems in Korea for both existing and newly constructed bridges and describes their aims and characteristics.

• Wind and Structures
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• v.27 no.6
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• pp.357-368
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• 2018

For a long cable-stayed bridge, stay cables are its most important load-carrying components. In this paper, long-term monitoring of super-long stay cables of Sutong Bridge is introduced. A comprehensive data analysis procedure is presented, in which time domain and frequency domain based analyses are carried out. In time domain, the vibration data of several long stay cables are firstly analyzed and the standard deviation of the acceleration of stay cables, and its variation with time are obtained, as well as the relationship between in-plane vibration and out-plane vibration. Meanwhile, some vibrations such as wind and rain induced vibration are detected. Through frequency domain analysis, the basic frequencies of the stay cables are identified. Furthermore, the axial forces and their statistical parameters are acquired. To investigate the vibration deflection, an FFT-based decomposition method is used to get the modal deflection. In the end, the relationship between the vibration amplitude of stay cables and the wind speed is investigated based on correlation analysis. Through the adopted procedure, some structural parameters of the stay cables have been derived, which can be used for evaluating the component performance and corresponding management of stay cables.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.5
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• pp.87-95
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• 2010

Lots of bridge structure generated the change of outward formation according to durability capability and decrepitude by long use. Especially, in case of the typhoon, snowing and earthquake is going to make rapidly more worse formation about the old structural facilitys. Also, outward formation by irregular and micro-distortion in bridge structure could not easily checked by normal diagnostics method. As a result, performance-capability of structure facility is getting to make a decline in standard of structure performance. Recently, real-time monitoring technology by wireless environment go into the study of irregular movement for structure facility. This paper presents the development of sensor to displacement checking about bridge structure. Sensing method of developed sensor put bring into the gyroscope technology using the acceleration speed and angular acceleration speed. This paper also will simulated to verified the monitoring capability of developed sensor against random vibration, frequency and distortion in simulated equipment.

• 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.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.3
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• pp.56-64
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• 2013

The wind resistant capacity of bridges with a span of less than 200m is typically evaluated by Wind Resistant Design Manual for Highway Bridges in Japan. Also, the first vertical frequency plays an important role in the evaluation of their aerodynamic performance. An unexpected vortex-induced vibration of Nielsen arch bridge with span of 183m designed by this manual has been measured by monitoring system during typhoon. The amplitude of vibrations was about 2 times than the allowable vibration displacement. This paper presents the feature of vortex-induced vibration of this Nielsen arch bridge based on measured wind velocity, wind direction, and responses at midspan of main girder. From the result of FFT, the $1^{st}$ mode shape of the bridge is antisymmetric and the $2^{nd}$ is symmetric. Also, the dominant vibration of the bridge is the $2^{nd}$ vertical mode. According to these results, the $2^{nd}$ vertical vibration mode of this Nielsen arch bridge is prior to the first for the estimation of wind resistance capacity.

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Structural Engineering and Mechanics
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• v.54 no.2
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• pp.337-362
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• 2015

This paper presents a feasibility study on structural damage alarming and localization of long-span cable-supported bridges using multi-novelty indices formulated by monitoring-derived modal parameters. The proposed method which requires neither structural model nor damage model is applicable to structures of arbitrary complexity. With the intention to enhance the tolerance to measurement noise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in terms of auto-associative neural networks (ANNs) where the output vector is designated to differ from the input vector while the training of the ANNs needs only the measured modal properties of the intact structure under in-service conditions. After validating the enhanced capability of the improved novelty index for structural damage alarming over the commonly configured novelty index, the performance of the improved novelty index for damage occurrence detection of large-scale bridges is examined through numerical simulation studies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurred with different types of structural damage. Then the improved novelty index is extended to formulate multi-novelty indices in terms of the measured modal frequencies and incomplete modeshape components for damage region identification. The capability of the formulated multi-novelty indices for damage region identification is also examined through numerical simulations of the TMB and TKB.