• Smart Structures and Systems
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• v.10 no.3
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• pp.209-228
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• 2012

Structural health monitoring with wireless sensor networks has received much attention in recent years due to the ease of sensor installation and low deployment and maintenance costs. However, sensor network technology needs to solve numerous challenges in order to substitute conventional systems: large amounts of data, remote configuration of measurement parameters, on-site calibration of sensors and robust networking functionality for long-term deployments. We present a structural health monitoring network that addresses these challenges and is used in several deployments for monitoring of bridges and buildings. Our system supports a diverse set of sensors, a library of highly optimized processing algorithms and a lightweight solution to support a wide range of network runtime configurations. This allows flexible partitioning of the application between the sensor network and the backend software. We present an analysis of this partitioning and evaluate the performance of our system in three experimental network deployments on civil structures.

• Structural Monitoring and Maintenance
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• v.7 no.2
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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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.4
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• pp.469-475
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• 2019

A long-term bridge monitoring system has been introduced and is under operation for long-term safety management of the structure. However, it is difficult to assess the condition of the quantitative structural system as it only measures responses and does not measure input loads. To overcome these shortcomings, FBG (Fiber Bragg Grating)-based input load measurement sensors were developed in this paper for measuring highway bridge input loads and their validity was verified through laboratory tests.

• Smart Structures and Systems
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• v.21 no.5
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• pp.695-703
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• 2018

Based on monitoring data collected from the Nanjing Dashengguan Bridge over the last five years, this paper systematically investigates the effects of temperature field and train loadings on the structural responses of this long-span high-speed railway bridge, and establishes the early warning thresholds for various structural responses. Then, some lessons drawn from the structural health monitoring system of this bridge are summarized. The main context includes: (1) Polynomial regression models are established for monitoring temperature effects on modal frequencies of the main girder and hangers, longitudinal displacements of the bearings, and static strains of the truss members; (2) The correlation between structural vibration accelerations and train speeds is investigated, focusing on the resonance characteristics of the bridge at the specific train speeds; (3) With regard to various static and dynamic responses of the bridge, early warning thresholds are established by using mean control chart analysis and probabilistic analysis; (4) Two lessons are drawn from the experiences in the bridge operation, which involves the lacks of the health monitoring for telescopic devices on the beam-end and bolt fractures in key members of the main truss.

• Structural Monitoring and Maintenance
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• v.4 no.4
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• pp.301-329
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• 2017

Impedance-based damage detection method has been known as an innovative tool with various successful implementations for structural health monitoring of civil structures. To monitor the local critical area of a structure, the impedance-based method utilizes the high-frequency impedance responses sensed by piezoelectric sensors as the local dynamic features. In this paper, current advances and future challenges of the impedance-based structural health monitoring are presented. Firstly, theoretical background of the impedance-based method is outlined. Next, an overview is given to recent advances in the wireless impedance sensor nodes, the interfacial impedance sensing devices, and the temperature-effect compensation algorithms. Various research works on these topics are reviewed to share up-to-date information on research activities and implementations of the impedance-based technique. Finally, future research challenges of the technique are discussed including the applicability of wireless sensing technology, the predetermination of effective frequency bands, the sensing region of impedance responses, the robust compensation of noise and temperature effects, the quantification of damage severity, and long-term durability of sensors.

• Smart Structures and Systems
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• v.15 no.2
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• pp.283-297
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• 2015

Jacket-type offshore structures are always exposed to severe environmental conditions such as salt, high speed of current, wave, and wind compared with other onshore structures. In spite of the importance of maintaining the structural integrity for an offshore structure, there are few cases to apply a structural health monitoring (SHM) system in practice. The impedance-based SHM is a kind of local SHM techniques and to date, numerous techniques and algorithms have been proposed for local SHM of real-scale structures. However, it still requires a significant challenge for practical applications to compensate unknown environmental effects and to extract only damage features from impedance signals. In this study, the impedance-based SHM was carried out on a 1/20-scaled model of an Uldolmok current power plant structure in Korea under changes in temperature and transverse loadings. Principal component analysis (PCA)-based approach was applied with a conventional damage index to eliminate environmental changes by removing principal components sensitive to them. Experimental results showed that the proposed approach is an effective tool for long-term SHM under significant environmental changes.

• Smart Structures and Systems
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• v.6 no.3
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• pp.263-275
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• 2010

In the last decade, wireless sensor networks have emerged as a promising technology that could accelerate progress in the field of structural monitoring. The main advantages of wireless sensor networks compared to conventional monitoring technologies are fast deployment, small interference with the surroundings, self-organization, flexibility and scalability. These features could enable mass application of monitoring systems, even on smaller structures. However, since wireless sensor network nodes are battery powered and data communication is the most energy consuming task, transferring all the acquired raw data through the network would dramatically limit system lifetime. Hence, data reduction has to be achieved at the node level in order to meet the system lifetime requirements of real life applications. The objective of this paper is to discuss some general aspects of data processing and management in monitoring systems based on wireless sensor networks, to present a prototype monitoring system for civil engineering structures, and to illustrate long-term field test results.

• Structural Engineering and Mechanics
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• v.87 no.5
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• pp.451-460
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• 2023

Recently, the use of smartphones for structural health monitoring in civil engineering has drawn increasing attention due to their rapid development and popularization. In this study, the structural responses and dynamic characteristics of a 421-m-tall skyscraper during the landfall of Typhoon Muifa are monitored using an iPhone 13. The measured building acceleration responses are first corrected by the resampling technique since the sampling rate of smartphone-based measurement is unstable. Then, based on the corrected building acceleration, the wind-induced responses (i.e., along-wind and across-wind responses) are investigated and the serviceability performance of the skyscraper is assessed. Next, the amplitude-dependency and time-varying structural dynamic characteristics of the monitored supertall building during Typhoon Muifa are investigated by employing the random decrement technique and Bayesian spectral density approach. Moreover, the estimated results during Muifa are further compared with those of previous studies on the monitored building to discuss its long-term time-varying structural dynamic characteristics. The paper aims to demonstrate the applicability and effectiveness of smartphones for structural health monitoring of high-rise buildings.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.11 no.5
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• pp.108-122
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• 2001

As bridge design is advancing toward the performance-based design. it becomes increasingly important to monitor and re-evaluate the long-term structural performance of bridges. Such information is essential in developing performance criteria for design. In this research. sensor systems for long-term structural performance monitoring have been installed on two highway bridges. Pre1iminary vibration measurement and data analysis have been performed on these instrumented bridges. On one bridge, ambient vibration data have been collected. based on which natural frequencies and mode shapes have been extracted using various methods and compared with those obtained by the preliminary finite element analysis. On the other bridge, braking and bumping vibration tests have been carried out using a water truck In addition to ambient vibration tests. Natural frequencies and mode shapes have been derived and the results by the breaking and bumping vibration tests have been compared. For the development of a three dimensional baseline finite element model, the new methodology using a neural network is proposed. The proposed one have been verified and applied to develop the baseline model of the bridge.

• Smart Structures and Systems
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• v.4 no.6
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• pp.711-726
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• 2008

A number of efforts had been sought to instrument bridges for the purpose of structural monitoring and assessment. The outcome of these efforts, as gauged by advances in the understanding of the definition of structural damage and their role in sensor selection as well as in the design of cost and data-effective monitoring systems, has itself been difficult to assess. The authors' experience with the design, calibration, and operation of a monitoring system for the Kishwaukee Bridge in Illinois has provided several lessons that bear upon these concerns. The systems have performed well in providing a continuous, low-cost monitoring platform for bridge engineers with immediate relevant information.