• Smart Structures and Systems
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• v.24 no.2
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• pp.243-256
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• 2019

Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure.

• Wind and Structures
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• v.25 no.2
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• pp.195-214
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• 2017

Full-scale wind characteristics based on the field measurements is an essential element in structural wind engineering. Statistical analysis of the wind characteristics at Sutong Cable-stayed Bridge (SCB) site is conducted in this study with the recorded long-term wind data from structural health monitoring system (SHMS) between 2008 and 2015. Both the mean and turbulent wind characteristics and power spectra are comprehensively investigated and compared with those in the current codes of practice, such as the measured wind rose diagram, monthly maximum mean wind speed, turbulence intensity, integral length scale. Measurement results based on the monitoring data show that winds surrounding the SCB site are substantially influenced by the southeast monsoon in summer and strong northern wind in winter. The measured turbulence intensity is slightly higher than the recommended values in specifications, while the measured ratio of lateral to longitudinal turbulence intensity is slightly lower. An approximately linear relationship between the measured turbulence intensities and gust factors is obtained. The mean value of the turbulence integral length scale is smaller than that of typical typhoon events. In addition, it is found that the Kaimal spectrum is suitable to be adopted as the power spectrum for longitudinal wind component at the SCB site. This contribution would provide important wind characteristic references for the wind performance evaluation of SCB and other civil infrastructures in adjacent regions.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.1
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• pp.7-12
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• 2016

With due to the recent development of USN (Ubiquitous Sensor Network) technology, a monitoring system has been developing for assuring the structural integrity of infrastructure through normal or long term measurements during their lifetime. An accident such as a collapse of infrastructure may cause not only loss of life but also damage to the economy of the nation. In order to enhance the availability of infrastructure and to be able to maintain their lifetime, it is necessary to monitor and to evaluate continuously the structural integrity throughout their entire lifetime. The purpose of this paper is to develop a monitoring system integrated with evaluation function based on the ubiquitous technology. The most essential part of this study is focusing more on developing a specific module convertible to A/D, which is to enhance the applicability of sensors that had not been applied to existing monitoring systems. Conclusively it has been successfully enhanced to make more diverse the number of sensors and measuring techniques for the monitoring system.

• Smart Structures and Systems
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• v.24 no.4
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.

• Smart Structures and Systems
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• v.20 no.2
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• pp.163-173
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• 2017

Structural health monitoring (SHM) of civil infrastructure using fiber Bragg grating sensor networks (FBGSNs) has received significant public attention in recent years. However, there is currently little research on the health-monitoring technology of high-piled wharfs in coastal ports using the fiber Bragg grating (FBG) sensor technique. The benefits of FBG sensors are their small size, light weight, lack of conductivity, resistance corrosion, multiplexing ability and immunity to electromagnetic interference. Based on the properties of high-piled wharfs in coastal ports and servicing seawater environment and the benefits of FBG sensors, the SHM system for a high-piled wharf in the Tianjin Port of China is devised and deployed partly using the FBG sensor technique. In addition, the health-monitoring parameters are proposed. The system can monitor the structural mechanical properties and durability, which provides a state-of-the-art mean to monitor the health conditions of the wharf and display the monitored data with the BIM technique. In total, 289 FBG stain sensors, 87 FBG temperature sensors, 20 FBG obliquity sensors, 16 FBG pressure sensors, 8 FBG acceleration sensors and 4 anode ladders are installed in the components of the back platform and front platform. After the installation of some components in the wharf construction site, the good signal that each sensor measures demonstrates the suitability of the sensor setup methods, and it is proper for the full-scale, continuous, autonomous SHM deployment for the high-piled wharf in the costal port. The South 27# Wharf SHM system constitutes the largest deployment of FBG sensors for wharf structures in costal ports to date. This deployment demonstrates the strong potential of FBGSNs to monitor the health of large-scale coastal wharf structures. This study can provide a reference to the long-term health-monitoring system deployment for high-piled wharf structures in coastal ports.

• Smart Structures and Systems
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• v.14 no.5
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• pp.917-942
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• 2014

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.5
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• pp.301-308
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• 2021

It is important to develop a digital SOC (Social Overhead Capital) maintenance system for preemptive maintenance in response to the rapid aging of social infrastructures. Abnormal signals induced from structures can be detected quickly and optimal decisions can be made promptly using IoT sensors deployed on the structures. In this study, a digital SOC monitoring system incorporating a multimetric IoT sensor was developed for long-term monitoring, for use in cloud-computing server for automated and powerful data analysis, and for establishing databases to perform : (1) multimetric sensing, (2) long-term operation, and (3) LTE-based direct communication. The developed sensor had three axes of acceleration, and five axes of strain sensing channels for multimetric sensing, and had an event-driven power management system that activated the sensors only when vibration exceeded a predetermined limit, or the timer was triggered. The power management system could reduce power consumption, and an additional solar panel charging could enable long-term operation. Data from the sensors were transmitted to the server in real-time via low-power LTE-CAT M1 communication, which does not require an additional gateway device. Furthermore, the cloud server was developed to receive multi-variable data from the sensor, and perform a displacement fusion algorithm to obtain reference-free structural displacement for ambient structural assessment. The proposed digital SOC system was experimentally validated on a steel railroad and concrete girder bridge.

• Computational Structural Engineering
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• v.10 no.3
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• pp.157-165
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• 1997

This research aims to develop an algorithm of optimal transducer placement for health monitoring of large 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 for continuous health monitoring. 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 Indefence 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 for monitoring purpose.

• Computers and Concrete
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• v.8 no.3
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• pp.293-309
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• 2011

A long-term structural health monitoring (SHM) system comprising over 700 sensors of sixteen types has been implemented on the Guangzhou Television and Sightseeing Tower (GTST) of 610 m high for real-time monitoring of the structure at both construction and service stages. As part of this sophisticated SHM system, 48 temperature sensors have been deployed at 12 cross-sections of the reinforced concrete inner structure of the GTST to provide on-line monitoring via a wireless data transmission system. In this paper, the differential temperature profiles in the reinforced concrete inner structure of the GTST, which are mainly caused by solar radiation, are recognized from the monitoring data with the purpose of understanding the temperature-induced structural internal forces and deformations. After a careful examination of the pre-classified temperature measurement data obtained under sunny days and non-sunny days, common characteristic of the daily temperature variation is observed from the data acquired in sunny days. Making use of 60-day temperature measurement data obtained in sunny days, statistical patterns of the daily rising temperature and daily descending temperature are synthesized, and temperature distribution models of the reinforced concrete inner structure of the GTST are formulated using linear regression analysis. The developed monitoring-based temperature distribution models will serve as a reliable input for numerical prediction of the temperature-induced deformations and provide a robust basis to facilitate the design and construction of similar structures in consideration of thermal effects.

• Wind and Structures
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• v.17 no.1
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• pp.43-68
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• 2013

The fluctuating wind induced vibration is one of the most important factors which has been taken into account in the design of long-span bridge due to the low stiffness and low natural frequency. Field measurement characteristics of sustained wind on structure site can provide accurate wind load parameters for wind field simulation and structural wind resistance design. As a suspension bridge with 1490 m main span, the Runyang Suspension Bridge (RSB) has high sensitivity to fluctuating wind. The simultaneous and continuously wind environment field measurement both in mid-span and on tower top is executed from 2005 up to now by the structural health monitoring system installed on this bridge. Based on the recorded data, the wind characteristic parameters, including mean wind speed, wind direction, the turbulence intensity, the gust factors, the turbulence integral length, power spectrum and spatial correlation, are analyzed in detail and the coherence functions of those parameters are evaluated using statistical method in this paper. The results indicate that, the turbulence component of sustain wind is larger than extremely strong winds although its mean wind speed is smaller; the correlation between turbulence parameters is obvious; the power spectrum is special and not accord with the Simiu spectrum and von Karman spectrum. Results obtained in this study can be used to evaluate the long term reliability of the Runyang Suspension Bridge and provide reference values for wind resistant design of other structures in this region.