• Structural Engineering and Mechanics
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• v.41 no.1
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• pp.139-155
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• 2012

Structural health monitoring systems are often installed on bridges to provide assessments of the need for structural maintenance and repair. Damage or deterioration may be detected by observation of changes in bridge characteristics evaluated from measured structural responses. However, construction materials such as concrete and steel cables exhibit certain time-dependent behaviour, which also results in changes in structural characteristics. If these are not accounted for properly, false alarms may arise. This paper proposes a systematic and efficient method to study the time-dependent effects on the dynamic properties of cable-stayed bridges. After establishing the finite element model of a cable-stayed bridge taking into account geometric nonlinearities and time-dependent behaviour, long-term time-dependent analysis is carried out by time integration. Then the dynamic properties of the bridge after a certain period can be obtained. The effects of time-dependent behaviour of construction materials on the dynamic properties of typical cable-stayed bridges are investigated in detail.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.661-673
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• 2010

This paper presents recent developments in an extremely compact, wireless impedance sensor node (the WID3, $\underline{W}$ireless $\underline{I}$mpedance $\underline{D}$evice) for use in high-frequency impedance-based structural health monitoring (SHM), sensor diagnostics and validation, and low-frequency (< ~1 kHz) vibration data acquisition. The WID3 is equipped with an impedance chip that can resolve measurements up to 100 kHz, a frequency range ideal for many SHM applications. An integrated set of multiplexers allows the end user to monitor seven piezoelectric sensors from a single sensor node. The WID3 combines on-board processing using a microcontroller, data storage using flash memory, wireless communications capabilities, and a series of internal and external triggering options into a single package to realize a truly comprehensive, self-contained wireless active-sensor node for SHM applications. Furthermore, we recently extended the capability of this device by implementing low-frequency analog-to-digital and digital-to-analog converters so that the same device can measure structural vibration data. The compact sensor node collects relatively low-frequency acceleration measurements to estimate natural frequencies and operational deflection shapes, as well as relatively high-frequency impedance measurements to detect structural damage. Experimental results with application to SHM, sensor diagnostics and low-frequency vibration data acquisition are presented.

• Structural Engineering and Mechanics
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• v.73 no.3
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• pp.331-337
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• 2020

This paper proposes a new approximate analytical solution for highly nonlinear vibration of mechanical systems called Hamiltonian Approach (HA) that can be widely use for structural health monitoring systems. The complete procedure of the HA approach is studied, and the precise application of the presented approach is surveyed by two familiar nonlinear partial differential problems. The nonlinear frequency of the considered systems is obtained. The results of the HA are verified with the numerical solution using Runge-Kutta's [RK] algorithm. It is established the only one iteration of the HA leads us to the high accurateness of the solution.

• Smart Structures and Systems
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• v.10 no.3
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• pp.271-298
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• 2012

Researchers have made significant progress in recent years towards realizing effective structural health monitoring (SHM) utilizing wireless smart sensor networks (WSSNs). These efforts have focused on improving the performance and robustness of such networks to achieve high quality data acquisition and distributed, in-network processing. One of the primary challenges still facing the use of smart sensors for long-term monitoring deployments is their limited power resources. Periodically accessing the sensor nodes to change batteries is not feasible or economical in many deployment cases. While energy harvesting techniques show promise for prolonging unattended network life, low power design and operation are still critically important. This research presents the WiSeMote: a new, fully integrated ultra-low power wireless smart sensor node and a flexible base station, both designed for long-term SHM deployments. The power consumption of the sensor nodes and base station has been minimized through careful hardware selection and the implementation of power-aware network software, without sacrificing flexibility and functionality.

• Smart Structures and Systems
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• v.11 no.5
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• pp.477-496
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• 2013

Due to their cost-effectiveness and ease of installation, wireless smart sensors (WSS) have received considerable recent attention for structural health monitoring of civil infrastructure. Though various wireless smart sensor networks (WSSN) have been successfully implemented for full-scale structural health monitoring (SHM) applications, monitoring of low-level ambient strain still remains a challenging problem for WSS due to A/D converter (ADC) resolution, inherent circuit noise, and the need for automatic operation. In this paper, the design and validation of high-precision strain sensor board for the Imote2 WSS platform and its application to SHM of a cable-stayed bridge are presented. By accurate and automated balancing of the Wheatstone bridge, signal amplification of up to 2507-times can be obtained, while keeping signal mean close to the center of the ADC span, which allows utilization of the full span of the ADC. For better applicability to SHM for real-world structures, temperature compensation and shunt calibration are also implemented. Moreover, the sensor board has been designed to accommodate a friction-type magnet strain sensor, in addition to traditional foil-type strain gages, facilitating fast and easy deployment. The wireless strain sensor board performance is verified through both laboratory-scale tests and deployment on a full-scale cable-stayed bridge.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.345-365
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• 2020

At present, many machine leaning and data mining methods are used for analyzing and predicting structural response characteristics. However, the platform that combines big data analysis methods with online and offline analysis modules has not been used in actual projects. This work is dedicated to developing a multifunctional Hadoop-Spark big data platform for bridges to monitor and evaluate the serviceability based on structural health monitoring system. It realizes rapid processing, analysis and storage of collected health monitoring data. The platform contains offline computing and online analysis modules, using Hadoop-Spark environment. Hadoop provides the overall framework and storage subsystem for big data platform, while Spark is used for online computing. Finally, the big data Hadoop-Spark platform computational performance is verified through several actual analysis tasks. Experiments show the Hadoop-Spark big data platform has good fault tolerance, scalability and online analysis performance. It can meet the daily analysis requirements of 5s/time for one bridge and 40s/time for 100 bridges.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.3
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• pp.241-247
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• 2011

Although intensive development continues on innovative sensor systems, there is still considerable uncertainty in deciding on the number of sensors required and their locations in order to obtain adequate information on structural behavior. This paper is concerned with the sensor locations on a beam-structure for prognostic structural health monitoring. The purpose of this study is to investigate how to determine optimal sensor placement(OSP) from the sensitivity information of a known failure mode. The sensitivity of the forced vibration response of a beam to the variation of stiffness due to a crack is calculated analytically and used to determine the optimal sensor locations for the specified failure mode. The results of this method compared with the results of different OSP methods. The results have shown that the proposed method on optimal sensor placement is very effective in structural health monitoring.

• Smart Structures and Systems
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• v.25 no.3
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• pp.285-299
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• 2020

Long term structural health monitoring has gained wide attention among civil engineers in recent years due to the scale and severity of infrastructure deterioration. Establishing effective damage indicators and proposing enhanced monitoring methods are of great interests to the engineering practices. In the case of bridge health monitoring, long term structural vibration measurement has been acknowledged to be quite useful and utilized in the planning of maintenance works. Previous researches are majorly concentrated on linear time series models for the measurement, whereas nonlinear dependences among the measurement are not carefully considered. In this paper, a new bridge health monitoring method is proposed based on the use of long term vibration measurement. A combination of the fundamental ARMA model and copula theory is investigated for the first time in detecting bridge structural damages. The concept is applied to a real engineering practice in Japan. The efficiency and accuracy of the copula based damage indicator is analyzed and compared in different window sizes. The performance of the copula based indicator is discussed based on the damage detection rate between the intact structural condition and the damaged structural condition.

• Smart Structures and Systems
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• v.21 no.5
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• pp.583-590
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• 2018

Nowadays, many tunnels have been commissioned for several decades, which require effective inspection methods to assess their health conditions. The ambient vibration test has been widely adopted for the damage identification of concrete structures. In this study, the vibration characters of tunnel lining shells built with forepoling method was analyzed based on the analytical solutions of the Donnell-Mushtari shell theory. The broken rock, foreploing, rock-concrete contacts between rock mass and concrete lining, was represented by elastic boundaries with normal and shear stiffness. The stiffness of weak contacts has significant effects on the natural frequency of tunnel lining. Numerical simulations were also carried out to compare with the results of the analytical methods, showing that even though the low nature frequency is difficult to distinguish, the presented approach is convenient, effective and accurate to estimate the natural frequency of tunnel linings. Influences of the void, the lining thickness and the concrete type on natural frequencies were evaluated.

• Smart Structures and Systems
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• v.15 no.3
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• pp.555-576
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• 2015

The large number of long-span bridges constructed in China motivates the applications of structural health monitoring (SHM) technology. Many bridges have been equipped with sophisticated SHM systems in the mainland of China and in Hong Kong of China. Recently, SHM technology has been extended to field test systems. In this view, SHM can serve as a tool to develop the methods of life-cycle performance design, evaluation, maintenance and management of bridges; to develop new structural analysis methods through validation and feedback from SHM results; and to understand the behavior of bridges under natural and man-made disasters, rapidly assess the damage and loss of structures over large regions after disasters, e.g., earthquake, typhoon, flood, etc. It is hoped that combining analytical methods, numerical simulation, small-scale tests and accelerated durability tests with SHM could become the main engine driving the development of bridge engineering. This paper demonstrates the above viewpoint.