• 한국소음진동공학회논문집
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• 제20권2호
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• pp.166-171
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• 2010

Wired monitoring systems have been used for damage detection and dynamic analysis of large structures(bridges, dams, plants, etc.). However, the real-world applications still remain limited, mainly due to time and cost issues inherent to wired systems. In recent years, an increasing number of researchers have adopted WSN(wireless sensor network) technologies to the field of SHM(structural health monitoring). Accurate time synchronization is most critical for the wireless approach to be feasible for SHM purpose, along with sufficient wireless bandwidth and highly precise measuring resolution. To satisfy technical criteria stated above, a wireless vibration monitoring system that uses high-precision MEMS(micro-electro-mechanical system) sensors and A/D convertor is discussed in detail. It was found experimentally that the level of time synchronization fell within $200\;{\mu}sec$.

• Smart Structures and Systems
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• 제6권5_6호
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• pp.711-730
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• 2010

This study presents the design of autonomous smart sensor nodes for damage monitoring of tendons and girders in prestressed concrete (PSC) bridges. To achieve the objective, the following approaches are implemented. Firstly, acceleration-based and impedance-based smart sensor nodes are designed for global and local structural health monitoring (SHM). Secondly, global and local SHM methods which are suitable for damage monitoring of tendons and girders in PSC bridges are selected to alarm damage occurrence, to locate damage and to estimate severity of damage. Thirdly, an autonomous SHM scheme is designed for PSC bridges by implementing the selected SHM methods. Operation logics of the SHM methods are programmed based on the concept of the decentralized sensor network. Finally, the performance of the proposed system is experimentally evaluated for a lab-scaled PSC girder model for which a set of damage scenarios are experimentally monitored by the developed smart sensor nodes.

• Smart Structures and Systems
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• 제15권4호
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• pp.1019-1039
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• 2015

Shanghai Tower is a composite structure building with a height of 632 m. In order to verify the structural properties and behaviors in construction and operation, a structural health monitoring project was conducted by Tongji University. The monitoring system includes sensor system, data acquisition system and a monitoring software system. Focusing on the health monitoring in construction, this paper introduced the monitoring parameters in construction, the data acquisition strategy and an integration structural health monitoring (SHM) software. The integration software - Structural Monitoring/ Analysis/ Evaluation System (SMAE) is designed based on integration and modular design idea, which includes on-line data acquisition, finite elements and dynamic property analysis functions. With the integration and modular design idea, this SHM system can realize the data exchange and results comparison from on-site monitoring and FEM effectively. The analysis of the monitoring data collected during the process of construction shows that the system works stably, realize data acquirement and analysis effectively, and also provides measured basis for understanding the structural state of the construction. Meanwhile, references are provided for the future automates construction monitoring and implementation of high-rise building structures.

• Smart Structures and Systems
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• 제5권4호
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• pp.369-379
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• 2009

The sudden collapse of Interstate 35 Bridge in Minneapolis gave a wake-up call to US municipalities to re-evaluate aging bridges. In this situation, structural health monitoring (SHM) technology can provide the essential help needed for monitoring and maintaining the nation's infrastructure. Monitoring long span bridges such as cable-stayed bridges effectively requires the use of a large number of sensors. In this article, we introduce a probabilistic approach to identify optimal locations of sensors to enhance damage detection. Probability distribution functions are established using an artificial neural network trained using a priori knowledge of damage locations. The optimal number of sensors is identified using multi-objective optimization that simultaneously considers information entropy and sensor cost-objective functions. Luling Bridge, a cable-stayed bridge over the Mississippi River, is selected as a case study to demonstrate the efficiency of the proposed approach.

• 한국전산구조공학회논문집
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• 제31권6호
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• pp.359-366
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• 2018

국내 고도성장기 이후 본격 건설되기 시작한 사회 기반 시설물은 노후화가 빠르게 진행되고 있다. 특히 사고 발생 시 대량 인명 피해로 직결될 수 있는 교량, 터널 등의 대형 구조물들에 대한 안전성 평가가 필요하다. 하지만, 기존의 유선 센서 기반의 SHM을 개선한 무선 스마트 센서네트워크는 짧은 신호도달거리로 인해 경제적이고 효율적인 시스템 구축이 힘들다. 따라서 LoRa LPWAN시스템은 사물인터넷의 확산과 더불어 저전력 장거리통신이 각광을 받고 있으며, 이를 구조건전성 모니터링에 응용함으로써 경제적이면서도 효율적인 SHM 구축이 가능하다. 본 연구에서는 LoRa LPWAN의 구조건전성 모니터링에 적용 가능성을 검토하고 비면허 통신 대역을 사용함으로 인해 발생하는 채널간의 충돌을 해결하면서 대역폭을 효율적으로 활용할 수 있는 채널 기반의 LoRa 네트워크 운영방법을 제안한다.

• Structural Monitoring and Maintenance
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• 제7권2호
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• pp.109-124
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• 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 Communications and Networks
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• 제11권4호
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• pp.350-358
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• 2009

In a wireless sensor network (WSN) setting, this paper presents a distributed decision-making framework and illustrates its application in an online structural health monitoring (SHM) system. The objective is to recover a damage severity vector, which identifies, localizes, and quantifies damages in a structure, via distributive and collaborative decision-making among wireless sensors. Observing the fact that damages are generally scarce in a structure, this paper develops a nonlinear 0-norm minimization formulation to recover the sparse damage severity vector, then relaxes it to a linear and distributively tractable one. An optimal algorithm based on the alternating direction method of multipliers (ADMM) and a heuristic distributed linear programming (DLP) algorithm are proposed to estimate the damage severity vector distributively. By limiting sensors to exchange information among neighboring sensors, the distributed decision-making algorithms reduce communication costs, thus alleviate the channel interference and prolong the network lifetime. Simulation results in monitoring a steel frame structure prove the effectiveness of the proposed algorithms.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2008년도 추계학술대회 논문집
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• pp.1545-1561
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• 2008

Among all transportations, railway transports have been promisingly offering excellent energy conservation and travelling time. Inevitably, they become a main role in not only transport goods but also passengers. With leap in development of technology, trains have tremendously enhanced their services in terms of speed, accessibility and comfort. However, the safety and ride quality have become a main issue as the train speed increased. The higher speeds have led the structural dynamics and health must be monitored from time to time to ensure that they are in good condition to provide reliable ride. Among all monitoring systems, the structural health monitoring (SHM) systems are imperative important due to its capability of in-situ monitoring and inherently reduce the maintenance frequencies and the huge associated cost. In this paper, SHM systems and the related non-destructive test and evaluation methods were discussed. The types of damages related to train vehicles as well as the damage hot spots are also included in this paper.

• Smart Structures and Systems
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• 제16권5호
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• pp.835-851
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• 2015

This paper presents theoretical and experimental evaluation of the structural health monitoring (SHM) capability of piezoelectric wafer active sensors (PWAS) at elevated temperatures. This is important because the technologies for structural sensing and monitoring need to account for the thermal effect and compensate for it. Permanently installed PWAS transducers have been One of the extensively employed sensor technologies for in-situ continuous SHM. In this paper, the electro-mechanical impedance spectroscopy (EMIS) method has been utilized as a dynamic descriptor of PWAS behavior and as a high frequency standing wave local modal technique. Another SHM technology utilizes PWAS as far-field transient transducers to excite and detect guided waves propagating through the structure. This paper first presents how the EMIS method is used to qualify and quantify circular PWAS resonators in an increasing temperature environment up to 230 deg C. The piezoelectric material degradation with temperature was investigated and trends of variation with temperature were deduced from experimental measurements. These effects were introduced in a wave propagation simulation software called Wave Form Revealer (WFR). The thermal effects on the substrate material were also considered. Thus, the changes in the propagating guided wave signal at various temperatures could be simulated. The paper ends with summary and conclusions followed by suggestions for further work.

• Smart Structures and Systems
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• 제20권2호
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• pp.197-205
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• 2017

The dynamic performance of railway bridges under high-speed trains draws the attention of bridge engineers. The vibration issue for long-span bridges under high-speed trains is still not well understood due to lack of validations through structural health monitoring (SHM) data. This paper investigates the correlation between bridge acceleration and train speed based on structural dynamics theory and SHM system from three foci. Firstly, the calculated formula of acceleration response under a series of moving load is deduced for the situation that train length is near the length of the bridge span, the correlation between train speed and acceleration amplitude is analyzed. Secondly, the correlation scatterplots of the speed-acceleration is presented and discussed based on the transverse and vertical acceleration response data of Dashengguan Yangtze River Bridge SHM system. Thirdly, the warning indexes of the bridge performance for correlation scatterplots of speed-acceleration are established. The main conclusions are: (1) The resonance between trains and the bridge is unlikely to happen for long-span bridge, but a multimodal correlation curve between train speed and acceleration amplitude exists after the resonance speed; (2) Based on SHM data, multimodal correlation scatterplots of speed-acceleration exist and they have similar trends with the calculated formula; (3) An envelope line of polylines can be used as early warning indicators of the changes of bridge performance due to the changes of slope of envelope line and peak speed of amplitude. This work also gives several suggestions which lay a foundation for the better design, maintenance and long-term monitoring of a long-span high-speed bridge.