• Proceedings of the KSR Conference
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• 2005.05a
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• pp.530-535
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• 2005

To monitor the flood level under heavy rainfall and the scour at railroad bridge, the system, which can effectively collect, store and transmit the data, is developed and applied to the field. The results in this study are as follows. 1) Monitoring for water level and scour depth is well done in view of the recording velocity and the accuracy of data which are measured. 2) This system is based on the web, internet and it is able to collect the realtime data and to analyze the risk. 3) When water level excesses the limit of danger level of a river on which railroad bridge is located, or when scour depth and angle of inclination of pier is increased, the scenario for early warning signal which sends to managers at central traffic control and drivers of trains is automatically made. It is judged that this system secures the safety of railroad and protects lives of train passengers as the warning signal sends to running train in advance at risky situation of railroad bridge under heavy rainfall.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1996.10a
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• pp.131-137
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• 1996

To perform condition monitoring of P.C. Box girder bridge under ambient traffic, dynamic characteristics were identified using the results of load test an analysis. It was found that natural frequencies obtained from the measured acceleration data for the forced vibration part and free vibration part were nearly identical. Thus it can be concluded that dynamic parameters are properly determined under ambient traffic condition. Finite element model for analysis was calibrated using measured frequencies. Change of dynamic characteristics were predicted through analysis of the established finite element model with anticipated change.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.1
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• pp.79-84
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• 2010

In this paper, we developed the BMSWare(Bridge Management System Middleware) for bridge safety surveillance in real time processing. The developed system operates on web and considers the general monitoring application for bridges. In various environments in geographical location of bridge, it can obtain reliably data from various logger and sensors without re-programming. The main functions of the developed system include the acquisition, processing, backup and transmission of the collected sensor data. It was proved to be the safety and effectiveness by application of Mooyeong bridge.

• Smart Structures and Systems
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• v.29 no.4
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• pp.599-616
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• 2022

Bridge displacement contains vital information for bridge condition and performance. Due to the limits of direct displacement measurement methods, the indirect displacement reconstruction methods based on the strain or acceleration data are also developed in engineering applications. There are still some deficiencies of the displacement reconstruction methods based on strain or acceleration in practice. This paper proposed a novel method based on long short-term memory (LSTM) networks to reconstruct the bridge dynamic displacements with the strain and acceleration data source. The LSTM networks with three hidden layers are utilized to map the relationships between the measured responses and the bridge displacement. To achieve the data fusion, the input strain and acceleration data need to be preprocessed by normalization and then the corresponding dynamic displacement responses can be reconstructed by the LSTM networks. In the numerical simulation, the errors of the displacement reconstruction are below 9% for different load cases, and the proposed method is robust when the input strain and acceleration data contains additive noise. The hyper-parameter effect is analyzed and the displacement reconstruction accuracies of different machine learning methods are compared. For experimental verification, the errors are below 6% for the simply supported beam and continuous beam cases. Both the numerical and experimental results indicate that the proposed data fusion method can accurately reconstruct the displacement.

• 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.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.15-16
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• 2015

Measuring sensor, equipment, ICT facilities and their software have relatively short life time comparing to constructional structure so that we should exchange or fix them continuously in the process of maintenance and management. In this paper, we propose a novel design of integrated maintenance, management, and measuring monitoring system applying the concept of mobile cloud. For the sake of disaster prevention for constructional structures such as bridge, tunnel, and other traditional buildings in the village of local heritage, we analyze status of these structures in the long term or short term period as well as disaster situations. Collecting data based on mobile cloud and analyzing future expectations based on probabilistic and statistical techniques, we implement our integrated monitoring system for constructional structures to solve these existing problems. Final results of this design and implementation are basically applied to the monitoring system for more than 10,000 structures spread over national land in Korea. In addition, we can specifically apply the monitoring system presented here to a bridge of timber structure in Asan Oeam Village and a traditional house in Andong Hahoe Village to watch them from possible disasters. Total procedure of system design and implementation as well as development of the platform LinkSaaS and application services of monitoring functions implemented on the platform. We prove a good performance of our system by fulfilling TTA authentication test, web accommodation test, and operation test using real measuring data.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.857-862
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• 2003

In this paper, we concern for development of precise measuement system for civil structure by using one chip microprocessor based on communication system. The datalogger is developed by using 80C196KC microprocessor. It has RF communication modul and data acquisition function for several kinds of sensors such as load cell, tilt-meter, strain gauge, tension meter, themocouple and so on. The data is acquisited by datalogger is sent to a main server which is located at central monitoring office. The server has a function suppervising several data points located at civil structures, it analyzes the data and shows the data graphically. The effectiveness of the developed system is proven through experimental results by an application for the bridge of Nakdong river edge in Busan, Korea.

• Smart Structures and Systems
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• v.17 no.3
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• pp.431-444
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• 2016

This paper was written in the context of a benchmark study promoted by The Hong Kong Polytechnic University using data samples collected in an instrumented cable-stayed bridge. The main goal of the benchmark test was to study the identification of the bridge modes of vibration under different wind conditions. In this contribution, the tools developed at ViBest/FEUP for automated data processing of setups collected by dynamic monitoring systems are presented and applied to the data made available in the context of the benchmark study. The applied tools are based on parametric output only modal identification methods combined with clustering algorithms. The obtained results demonstrate that the proposed algorithms succeeded to automatically identify the modes with relevant contribution for the bridge response under different wind conditions.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.4
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• pp.137-147
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• 1998

The neural net application was tried to develop the technique for monitoring the health status of a steel truss bridge which was scaled down to 1/15 of the real bridge for the laboratory experiments. The damage scenarios were chosen as 7 cases. The dynamic behavior, which was changed due to the breakage of the members, of the bridge was investigated by finite element analysis. The bridge consists of single spam, and eight (8) main structural subsystems. The loading vehicle, which weighs as 100 kgf, was operated by the servo-motor controller. The accelerometers were bonded on the surface of 7 cross-beams to measure the dynamic behavior induced by the abnormal structural condition. Artificial neural network technique was used to determine the severity of the damage. At first, the neural net was learnt by the results of finite element analysis, and also, the maximum detection error was 3.65 percents. Another neural net was also learnt, and verified by the experimental results, and in this case, the maximum detection error was 1.05 percents. In future study, neural net is necessary to be learnt and verified by various data from the real bridge.

• 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.