• Smart Structures and Systems
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• 제6권3호
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• pp.309-333
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• 2010

Civil infrastructure, in both its construction and maintenance, represents the largest societal investment in this country, outside of the health care industry. Despite being the lifeline of US commerce, civil infrastructure has scarcely benefited from the latest sensor technological advances. Our future should focus on harnessing these technologies to enhance the robustness, longevity and economic viability of this vast, societal investment, in light of inherent uncertainties and their exposure to service and even extreme loadings. One of the principal means of insuring the robustness and longevity of infrastructure is to strategically deploy smart sensors in them. Therefore, the objective is to develop novel, durable, smart sensors that are especially applicable to major infrastructure and the facilities to validate their reliability and long-term functionality. In some cases, this implies the development of new sensing elements themselves, while in other cases involves innovative packaging and use of existing sensor technologies. In either case, a parallel focus will be the integration and networking of these smart sensing elements for reliable data acquisition, transmission, and fusion, within a decision-making framework targeting efficient management and maintenance of infrastructure systems. In this paper, prudent and viable sensor and health monitoring technologies have been developed and used in several large structural systems. Discussion will also include several practical bridge health monitoring applications including their design, construction, and operation of the systems.

• Smart Structures and Systems
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• 제9권3호
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• pp.287-301
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• 2012

It is well known that overloaded vehicles may cause severe damages to bridges, and how to estimate and evaluate the status of the overloaded vehicles passing through bridges become a challenging problem. Therefore, based on the monitored strain data from a structural health monitoring system (SHM) installed on a bridge, a method is recommended to identify and analyze the probability of overloaded vehicles. Overloaded vehicle loads can cause abnormity in the monitored strains, though the abnormal strains may be small in a concrete continuous rigid frame bridge. Firstly, the abnormal strains are identified from the abundant strains in time sequence by taking the advantage of wavelet transform in abnormal signal identification; secondly, the abnormal strains induced by heavy vehicles are picked up by the comparison between the identified abnormal strains and the strain threshold gotten by finite element analysis of the normal heavy vehicle; finally, according to the determined abnormal strains induced by overloaded vehicles, the statistics of the overloaded vehicles passing through the bridge are summarized and the whole probability of the overloaded vehicles is analyzed. The research shows the feasibility of using the monitored strains from a long-term SHM to identify the information of overloaded vehicles passing through a bridge, which can help the traffic department to master the heavy truck information and do the damage analysis of bridges further.

• 한국구조물진단유지관리공학회 논문집
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• 제1권2호
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• pp.87-94
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• 1997

This research aims to develop an algorithm of optimal transducer placement using Kinetic Energy of the structural system. The structural vibration response-based health monitoring is considered one of the best for the system which requires a long-term, continuous monitoring. In its experimental modal testing, however, it is difficult to decide on the measurement locations and their number, especially for complex structures, which have a major influence on the quality of the results. In order to minimize the number of sensing operations and optimize the transducer location while maximizing the accuracy of results, this paper discusses about an optimum transducer placement criterion suitable for the identification of structural damage. As a criterion algorithm, it proposes the Kinetic Energy Optimization Technique (EOT), and then addresses the numerical issues which are subsequently applicable to actual experiment where a bridge model is used. By using the experimental data, it compares the EOT with the EIM (Effective Independence Method) which is generally used to optimize the transducer placement for the damage identification and control purposes. The comparison conclusively shows that the EOT algorithm proposed in this paper is preferable when a structure is to be instrumented with fewer sensors.

• Smart Structures and Systems
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• 제14권2호
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• pp.209-224
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• 2014

With more and more built long-term structural health monitoring (SHM) systems, it has been considered to apply monitored data to learn the reliability of bridges. In this paper, based on a long-term SHM system, especially in which the sensors were embedded from the beginning of the construction of the bridge, a method to calculate the localized reliability around an embedded sensor is recommended and implemented. In the reliability analysis, the probability distribution of loading can be the statistics of stress transferred from the monitored strain which covered the effects of both the live and dead loads directly, and it means that the mean value and deviation of loads are fully derived from the monitored data. The probability distribution of resistance may be the statistics of strength of the material of the bridge accordingly. With five years' monitored strains, the localized reliabilities around the monitoring sensors of a bridge were computed by the method. Further, the monitored stresses are classified into two time segments in one year period to count the loading probability distribution according to the local climate conditions, which helps us to learn the reliability in different time segments and their evolvement trends. The results show that reliabilities and their evolvement trends in different parts of the bridge are different though they are all reliable yet. The method recommended in this paper is feasible to learn the localized reliabilities revealed from monitored data of a long-term SHM system of bridges, which would help bridge engineers and managers to decide a bridge inspection or maintenance strategy.

• Smart Structures and Systems
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• 제20권2호
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• pp.139-150
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• 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
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• 제25권5호
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• pp.593-604
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• 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
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• 제6권8호
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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.

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

• 한국전산구조공학회논문집
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• 제34권4호
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• pp.183-189
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• 2021

구조물에 장기적으로 발생하는 노후화를 정량적으로 파악하기 위해 상시진동 데이터를 활용한 일반화된 모니터링 시스템에 관한 연구가 세계적으로 활발히 수행중이다. 본 연구에서는 구조물에서 장기적으로 취득되는 동특성을 앙상블 학습에 활용하여 구조물의 이상을 감지하기 위한 보급형 엣지 컴퓨팅 시스템을 구축하였다. 시스템의 하드웨어는 라즈베리파이와 보급형 가속도계, 기울기센서, GPS RTK 모듈, 로라 모듈로 구성됐다. 실험실 규모의 구조물 모형 진동실험을 통해 동특성을 활용한 앙상블 학습의 구조물 이상감지를 검증하였으며, 실험을 기반으로 한 실시간 동특성 추출 분산처리 알고리즘을 라즈베리파이에 탑재하였다. 구축된 시스템을 하우징하고 포항시 행정복지센터에 설치하여 데이터를 취득함으로써 개발된 시스템의 현장 적용성을 검증하였다.

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