• Structural Monitoring and Maintenance
• /
• v.7 no.2
• /
• pp.125-147
• /
• 2020

Damage detection of structures is one of the most important topics in structural health monitoring. In practice, the response is not available at all structural degrees of freedom, and due to the installation of sensors at some degrees of freedom, responses exist only in limited number of degrees of freedom. This paper is investigated the damage detection of structures by applying two approaches, AllDOF and Dynamic Condensation Method (DCM), based on the Modified Modal Strain Energy Method (MMSEBI). In the AllDOF method, mode shapes in all degrees of freedom is available, but in the DCM the mode shapes only in some degrees of freedom are available. Therefore by methods like the DCM, mode shapes are obtained in slave degrees of freedom. So, in the first step, the responses at slave degrees of freedom extracted using the responses at master degrees of freedom. Then, using the reconstructed mode shape and obtaining the modified modal strain energy, the damages are detected. Two standard examples are used in different damage cases to evaluate the accuracy of the mentioned method. The results showed the capability of the DCM is acceptable for low mode shapes to detect the damage in structures. By increasing the number of modes, the AllDOF method identifies the locations of the damage more accurately.

• Structural Monitoring and Maintenance
• /
• v.7 no.2
• /
• pp.109-124
• /
• 2020

Benefiting from the massive monitoring data collected by the Structural health monitoring (SHM) system, scholars can grasp the complex environmental effects and structural state during structure operation. However, the monitoring data is often missing due to sensor faults and other reasons. It is necessary to study the recovery method of missing monitoring data. Taking the structural temperature monitoring data of Nanjing Dashengguan Yangtze River Bridge as an example, the long short-term memory (LSTM) network-based recovery method for missing structural temperature data is proposed in this paper. Firstly, the prediction results of temperature data using LSTM network, support vector machine (SVM), and wavelet neural network (WNN) are compared to verify the accuracy advantage of LSTM network in predicting time series data (such as structural temperature). Secondly, the application of LSTM network in the recovery of missing structural temperature data is discussed in detail. The results show that: the LSTM network can effectively recover the missing structural temperature data; incorporating more intact sensor data as input will further improve the recovery effect of missing data; selecting the sensor data which has a higher correlation coefficient with the data we want to recover as the input can achieve higher accuracy.

• Journal of Korean Public Health Nursing
• /
• v.29 no.1
• /
• pp.79-89
• /
• 2015

Purpose: The aim of the study was to determine the effects of a community based participatory program in obese middle-aged women. Methods: One-group pretest-posttest design was used. The subjects were 35 middle-aged women. Data were collected at public health centers in Chungcheongnam-Do from March to May, 2013. To evaluate the effect of the program, physiological indexes(body mass index, skeletal muscle mass, body fat mass, visceral fat area) and health behavior indexes(dietary practice guidelines score, moderate physical activity, drinking frequency) were measured. Analysis was performed using a Wilcoxon Signed Rank Test. Results: After the program, physiological indexes (BMI, BFM, SMM, VFA) and health behavior indexes (dietary guidelines scores, frequency of physical activity, drinking frequency) were significantly improved. Conclusion: The community based participatory obesity program by public health centers is considered to be effective. Therefore, greater effort is needed for better participatory program development of several health promoting fields, and more research is needed in order to examine a continuous effect.

• The Optical Journal
• /
• s.158
• /
• pp.58-60
• /
• 2015

드론이 인기다. 드론이 뜨고 있다. 드론은 왜 인기인가? 왜 드론이 인기인지를 인식해보아야 한다. 결론부터 이야기하면 드론이 인기인 이유는 드론이 MMM이기 때문이다. MMM은 필자가 2년 전 쯤에 만든 신조어로 Multipurpose Mobile Machine의 약자다. 다목적 이동 기계라는 말이다. 우리말로 줄이면 다이기(多移機)쯤 된다. 드론은 다목적 이동 기계, 즉 다이기이다. 인터넷이 군용으로 사용되다가 민간이 사용하게 된 것처럼 드론 역시 정찰, 감시, 폭격 등의 군사용으로 출발했지만, 민간이 사용하는 드론의 용도는 무척 다양하다. 사진 촬영, 영화 제작, 드라마 촬영, 음식 배달, 씨앗의 파종과 농약의 살포, 현장 탐사, 경계 근무 등 정말 다용도에 사용되고 있다. 중요한 것은 드론 각각이 다목적으로 사용될 수 있다는 것이다. 이는 드론이 단지 물리적 기계인 것이 아니라 스마트 기계로, 스마트폰과 같은 SW중심 기기와 드론에 내장된 OS와 SW에 의한 제어를 통해 비로소 실현된다. 즉, 드론은 MMM이기도 하지만, 정확히 SMM(Smart Mobile Machine)이기 때문에 그 잠재성이 인정받고 있는 것이다. 지금까지 스마트 폰, 스마트 워치, 스마트 TV 등은 모두 스마트 기기이기는 하지만 스마트 이동 기기는 아니다. 한편, 로봇 청소기 등 기존의 가정용 로봇이나 산업용 로봇은 대부분 단일목적 이동기기이거나 다목적 고정 기계이므로, 로봇이라 부르기가 민망한 기계들이었다. 필자는 드론이 이렇듯 새로운 스마트 기기 산업과 기존의 로봇 산업이 갈 길 또는 가고 있는 길을 보여주고 있다는 점에서 관심을 갖는다. 스마트 기기 산업의 관점에서는 이제 비로소 고정형(스마트TV), 부착형(스마트 워치), 휴대형(스마트 폰) 스마트 기기 산업에서 다목적 스마트 이동 기계라는 새로운 블루 오션 창출 산업이기에 의미가 있다.

• Structural Monitoring and Maintenance
• /
• v.4 no.2
• /
• pp.115-131
• /
• 2017

Classical flutter of wind turbine blades indicates a type of aeroelastic instability with fully attached boundary layer where a torsional blade mode couples to a flapwise bending mode, resulting in a mutual rapid growth of the amplitudes. In this paper the monitoring problem of onset of flutter is investigated from a detection point of view. The criterion is stated in terms of the exceeding of a defined envelope process of a specific maximum torsional vibration threshold. At a certain instant of time, a limited part of the previously measured torsional vibration signal at the tip of blade is decomposed through the Empirical Mode Decomposition (EMD) method, and the 1st Intrinsic Mode Function (IMF) is assumed to represent the response in the flutter mode. Next, an envelope time series of the indicated modal response is obtained in terms of a Hilbert transform. Finally, a flutter onset criterion is proposed, based on the indicated envelope process. The proposed online flutter monitoring method provided a practical and direct way to detect onset of flutter during operation. The algorithm has been illustrated by a 907-DOFs aeroelastic model for wind turbines, where the tower and the drive train is modelled by 7 DOFs, and each blade by means of 50 3-D Bernoulli-Euler beam elements.

• Structural Monitoring and Maintenance
• /
• v.5 no.1
• /
• pp.1-13
• /
• 2018

The Zhuhai Opera House has an external structure consisting of a type of spatial steel, where the stress of steel elements varies with the ambient temperature. A structural health monitoring system was implemented at Zhuhai Opera House, and the temperatures and stresses of the structures were monitored in real time. The relationship between the stress distribution and temperature variations was analysed by measuring the temperature and stresses of the steel elements. In addition to measurements of the structure stresses and temperatures, further simulation analysis was carried out to provide the detailed relationship between the stress distributions and temperature variations. The limited temperature measurements were used to simulate the structure temperature distribution, and the stress distributions of all steel elements of the structure were analysed by building a finite element model of the Zhuhai Opera House spatial steel structure. This study aims to reveal the stress distributions of steel elements in a real-world project based on temperature variations, and to supply a basic database for the optimal construction time of a spatial steel structure. This will not only provide convenient, rapid and safe early warnings and decision-making for the spatial steel structure construction and operation processes, but also improve the structural safety and construction accuracy of steel space structures.

• Structural Monitoring and Maintenance
• /
• v.5 no.1
• /
• pp.79-92
• /
• 2018

The evaluation theory of reliability, availability, maintainability and safety (RAMS) as a mature theory of state evaluation in the railway engineering, can be well used to the evaluation, management, and maintenance of complicated structure like the long-span bridge structures on the high-speed railway. Taking a typical steel-truss arch bridge on the Beijing-Shanghai high-speed railway, the Nanjing Dashengguan Yangtze River Bridge, this paper developed a new method of state evaluation for the existing steel-truss arch high-speed railway bridge. The evaluation framework of serving state for the bridge structure is presented based on the RAMS theory. According to the failure-risk, safety/availability, maintenance of bridge members, the state evaluation method of each monitoring item is presented. The weights of the performance items and the monitoring items in all evaluation levels are obtained using the analytic hierarchy process. Finally, the comprehensive serving state of bridge structure is hierarchical evaluated.

• Structural Monitoring and Maintenance
• /
• v.5 no.1
• /
• pp.39-50
• /
• 2018

Taking three types of bridge bearings with diameter being 100 mm as examples, the theoretical analysis, the experimental research as well as the numerical simulation of these bearings is conducted. Since the normal compression and shear machines cannot be applied to the small-size bearings, an improved equipment to test the properties of these bearings is proposed and fabricated. Besides, the simulation of the bearings is conducted based on the explicit finite element software ANSYS/LS-DYNA, and some parameters of the bearings are modified in the finite element model to reduce the computation cost effectively. Results show that all the research methods are capable of revealing the fundamental properties of the small-size bearings, and a combined use of these methods can better catch both the integral properties and the inner detailed mechanical behaviors of the bearings.

• Structural Monitoring and Maintenance
• /
• v.5 no.1
• /
• pp.51-65
• /
• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.

• Structural Monitoring and Maintenance
• /
• v.5 no.1
• /
• pp.111-127
• /
• 2018

Horizontal displacement of high-rise building is an essential index for assessing the structural performance and safety. In this paper, a novel inclinometer-based method is proposed to address this issue and an algorithm based on three spline interpolation principle is presented to estimate the horizontal displacement of high-rise buildings. In this method, the whole structure is divided into different elements by different measured points. The story drift angle curve of each element is modeled as a three spline curve. The horizontal displacement can be estimated after integration of the story drift angle curve. A numerical example is designed to verify the proposed method and the result shows this method can effectively estimate the horizontal displacement with high accuracy. After that, this method is applied to a practical slender structure - Shanghai Tower. Nature frequencies identification and deformation monitoring are conducted from the signal of inclinometers. It is concluded that inclinometer-based technology can not only be used for spectrum analysis and modal identification, but also for monitoring deformation of the whole structure. This inclinometer-based technology provides a novel method for future structural health monitoring.