• Smart Structures and Systems
• /
• 제25권5호
• /
• pp.593-604
• /
• 2020

Temperature may have more significant influences on structural responses than operational loads or structural damage. Therefore, a comprehensive understanding of temperature distributions has great significance for proper design and maintenance of bridges. In this study, the temperature distribution of the steel box girder is systematically investigated based on the structural health monitoring system (SHMS) of the Sutong Cable-stayed Bridge. Specifically, the characteristics of the temperature and temperature difference between different measurement points are studied based on field temperature measurements. Accordingly, the probability density distributions of the temperature and temperature difference are calculated statistically, which are further described by the general formulas. The results indicate that: (1) the temperature and temperature difference exhibit distinct seasonal characteristics and strong periodicity, and the temperature and temperature difference among different measurement points are strongly correlated, respectively; (2) the probability density of the temperature difference distribution presents strong non-Gaussian characteristics; (3) the probability density function of temperature can be described by the weighted sum of four Normal distributions. Meanwhile, the temperature difference can be described by the weighted sum of Weibull distribution and Normal distribution.

• Smart Structures and Systems
• /
• 제24권6호
• /
• pp.733-744
• /
• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Structural Monitoring and Maintenance
• /
• 제3권4호
• /
• pp.315-333
• /
• 2016

This study aims to perform damage identification for Da-Sheng-Guan (DSG) high-speed railway truss arch bridge using fuzzy clustering analysis. Firstly, structural health monitoring (SHM) system is established for the DSG Bridge. Long-term field monitoring strain data in 8 different cases caused by high-speed trains are taken as classification reference for other unknown cases. And finite element model (FEM) of DSG Bridge is established to simulate damage cases of the bridge. Then, effectiveness of one fuzzy clustering analysis method named transitive closure method and FEM results are verified using the monitoring strain data. Three standardization methods at the first step of fuzzy clustering transitive closure method are compared: extreme difference method, maximum method and non-standard method. At last, the fuzzy clustering method is taken to identify damage with different degrees and different locations. The results show that: non-standard method is the best for the data with the same dimension at the first step of fuzzy clustering analysis. Clustering result is the best when 8 carriage and 16 carriage train in the same line are in a category. For DSG Bridge, the damage is identified when the strain mode change caused by damage is more significant than it caused by different carriages. The corresponding critical damage degree called damage threshold varies with damage location and reduces with the increase of damage locations.

• 한국구조물진단유지관리공학회 논문집
• /
• 제26권4호
• /
• pp.20-29
• /
• 2022

상시진동을 이용하여 구조계의 동특성을 추출하는 운용모드해석 기법은 케이블교량 구조건전성모니터링의 한 분야로써 다양한 연구와 실험적 검증이 수행되어왔다. 본 연구에서는 두 번에 걸친 상시진동실험과 함께 3년간의 장기 계측을 통해 수집된 가속도 데이터를 이용하여 공용 중인 사장교의 장단기 동특성을 평가하였다. 교량 준공 이후 6년과 19년이 경과한 시기에 실시한 고해상도 상시진동실험으로부터 0.1 ~ 2.5 Hz 대역에서 27개 수직모드(휨, 비틈)와 1개 수평모드를 추출하였다. 운용모드해석에 기반한 동특성 추출은 PP기법, ERADC기법, FDD기법, TDD기법을 적용하였으며, 적용한 기법들 간에 유의미한 차이가 없는 것을 확인하였다. 장기 계측 고유진동수와 환경 요인(온도, 바람)에 대한 상관성 분석으로부터 온도 변화가 고유진동수 변동에 지배적인 영향인자임을 확인하였다. 대상교량의 고유진동수 감소 경향은 구조성능과 일체성이 변한 것이 아니라 두 번의 상시진동실험 간 온도 차이에 의한 환경영향이 컸음을 밝혔다. 또한 TDD기법 적용 시, 지연이 0에서 자기상관이 1이 되도록 시퀀스를 정규화하는 알고리즘을 추가하여 모드형상 추출의 정확도를 개선하였다.

• Smart Structures and Systems
• /
• 제20권2호
• /
• pp.139-150
• /
• 2017

The structural strain plays a significant role in structural condition assessment of in-service bridges in terms of structural bearing capacity, structural reliability level and entire safety redundancy. Therefore, it has been one of the most important parameters concerned by researchers and engineers engaged in structural health monitoring (SHM) practices. In this paper, an SHM system instrumented on the Jiubao Bridge located in Hangzhou, China is firstly introduced. This system involves nine subsystems and has been continuously operated for five years since 2012. As part of the SHM system, a total of 166 fiber Bragg grating (FBG) strain sensors are installed on the bridge to measure the dynamic strain responses of key structural components. Based on the strain monitoring data acquired in recent two years, the strain-based structural condition assessment of the Jiubao Bridge is carried out. The wavelet multi-resolution algorithm is applied to separate the temperature effect from the raw strain data. The obtained strain data under the normal traffic and wind condition and under the typhoon condition are examined for structural safety evaluation. The structural condition rating of the bridge in accordance with the AASHTO specification for condition evaluation and load and resistance factor rating of highway bridges is performed by use of the processed strain data in combination with finite element analysis. The analysis framework presented in this study can be used as a reference for facilitating the assessment, inspection and maintenance activities of in-service bridges instrumented with long-term SHM system.

• Smart Structures and Systems
• /
• 제15권2호
• /
• pp.335-353
• /
• 2015

Previous long-term measurements of the Uldolmok tidal current power plant showed that the structure's natural frequencies fluctuate with a constant cycle-i.e., twice a day with changes in tidal height and tidal current velocity. This study aims to improve structural health monitoring (SHM) techniques for offshore structures under a harsh tidal environment like the Uldolmok Strait. In this study, lab-scale experiments on a simplified offshore structure as a lab-scale test structure were conducted in a circulating water channel to thoroughly investigate the causes of fluctuation of the natural frequencies and to validate the displacement estimation method using multimetric data fusion. To this end, the numerical study was additionally carried out on the simplified offshore structure with damage scenarios, and the corresponding change in the natural frequency was analyzed to support the experimental results. In conclusion, (1) the damage that occurred at the foundation resulted in a more significant change in natural frequencies compared with the effect of added mass; moreover, the structural system became nonlinear when the damage was severe; (2) the proposed damage index was able to indicate an approximate level of damage and the nonlinearity of the lab-scale test structure; (3) displacement estimation using data fusion was valid compared with the reference displacement using the vision-based method.

• 한국터널지하공간학회 논문집
• /
• 제10권4호
• /
• pp.361-369
• /
• 2008

지중터널은 대부분 지하수위 하부에 위치하므로 지하수 처리문제는 터널의 장기운영에 있어 매우 중요하다. 배수형터널의 경우 수리기능이 원활하면 라이닝에 수압이 작용하지 않으나 장기 운영으로 인해 배수시스템의 열화가 진행되면서 라이닝 배면에 설계 시 고려하지 않았던 간극수압이 작용하게 되는데, 이를 잔류수압이라 한다. 잔류수압은 피에조미터로 그 측정이 가능하나 이는 라이닝 및 배수시스템을 훼손할 염려가 있어 부적합하기 때문에 라이닝을 손상시키지 않으면서 작용수압을 평가할 수 있고, 운영 중 라이닝의 건전도 평가(health monitoring) 시 수압상태의 파악이 가능한 비파피 예측기법이 요구된다. 본 논문에서는 이론적 및 수치해석적 방법을 사용하여 운영 중 터널에 작용하는 간극구압(잔류수압) 예측기법을 제시하였으며, 본 해석방법을 이용하면 비파괴 방법으로 라이닝에 작용하는 간극수압의 파악이 가능하다. 제안된 방법은 이론적 예측기법과 수치해석 결과인 정규화 간극수압 분포곡선과를 병용함으로써 터널 운영단계에서의 잔류수압에 대한 안정성 검토에 유용하게 활용될 수 있다.

• 산업경영시스템학회지
• /
• 제46권3호
• /
• pp.123-138
• /
• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

• Journal of Korean Biological Nursing Science
• /
• 제11권2호
• /
• pp.120-127
• /
• 2009

Purpose: The study aimed at monitoring the immune status of health care workers (HCWs) of a tertiary hospital after accidental exposure to Hepatitis B virus (HBV). Methods: Between January 2004 and December 2006, 353 cases of exposure to Hepatitis B virus were reported. The HBV-exposed HCWs were required to undergo follow-up serum tests to analyze their immune status one year after the exposure. The obtained data were then analyzed to determine the incidence of exposure and of sero-conversion. Results: In this hospital, an average of 9.8 cases of Hepatitis B exposure among HCWs was reported in a month. Follow-up tests conducted after exposure revealed that 90.4% of the HBV-exposed HCWs were positive for Hepatitis B antibody and 66.9% of the HBV-exposed HCWs were reported to have antibody levels exceeding 10 mIU/mL. Results of serum tests for the HBV antigen conducted one year after exposure were negative for all the exposed HCWs. Conclusion: Among the 79.6% of the HCWs who underwent serum tests one year after exposure the HBV sero-conversion rate was 0.0%. However, a further investigation in the form of long-term and multi-center studies is required to confirm this result. Furthermore, an active system should be established to ensure that all exposed HCWs undergo follow-up serum tests.

• 상하수도학회지
• /
• 제32권4호
• /
• pp.337-348
• /
• 2018

As the issue of microplastics (MPs) detection in tap water was raised in other countries in 2017, monitoring of MPs in drinking and source water, and sewage treatment plant (STP) effluents was initiated. This study intends to look into other studies on MPs in STPs at home and abroad, and review the characteristics of MPs and their removal efficiencies in the STPs, the risk and effect of MPs on watersheds, and management practices in order to help better understand MPs in STPs. To manage MPs effectively in STPs, it is necessary to investigate the detection of MPs discharged from STPs, do research on human health risk and control measures, and build a monitoring system including standardized analytical methods.