• Smart Structures and Systems
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• v.22 no.5
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• pp.509-525
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• 2018

Sensors and systems in Civionics technology play an important role for continuously facilitating real-time structure monitoring systems by detecting and locating damage to or degradation of structures. An advanced materials, design processes, long-term sensing ability of sensors, electromagnetic interference, sensor placement techniques, data acquisition and computation, temperature, harsh environments, and energy consumption are important issues related to sensors for structural health monitoring (SHM). This paper provides a comprehensive survey of various sensor technologies, sensor classes and sensor networks in Civionics research for existing SHM systems. The detailed classification of sensor categories, applications, networking features, ranges, sizes and energy consumptions are investigated, summarized, and tabulated along with corresponding key references. The current challenges facing typical sensors in Civionics research are illustrated with a brief discussion on the progress of SHM in future applications. The purpose of this review is to discuss all the types of sensors and systems used in SHM research to provide a sufficient background on the challenges and problems in optimizing design techniques and understanding infrastructure performance, behavior and current condition. It is observed that the most important factors determining the quality of sensors and systems and their reliability are the long-term sensing ability, data rate, types of processors, size, power consumption, operation frequency, etc. This review will hopefully lead to increased efforts toward the development of low-powered, highly efficient, high data rate, reliable sensors and systems for SHM.

• Smart Structures and Systems
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• v.29 no.1
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• Smart Structures and Systems
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• v.7 no.5
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• pp.365-378
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• 2011

Piezo-based active structural health monitoring (SHM) requires amplifiers specifically designed for capacitive loads. Moreover, with the increase in number of applications of wireless SHM systems, energy efficiency and cost reduction for this type of amplifiers is becoming a requirement. General lab grade amplifiers are big and costly, and not built for outdoor environments. Although some piezoceramic power amplifiers are available in the market, none of them are specifically targeting the wireless constraints and low power requirements. In this paper, a piezoceramic transducer amplifier for wireless active SHM systems has been designed. Power requirements are met by two digital On/Off switches that set the amplifier in a standby state when not in use. It provides a stable ${\pm}180$ Volts output with a bandwidth of 7k Hz using a single 12 V battery. Additionally, both voltage and current outputs are provided for feedback control, impedance check, or actuator damage verification. Vibration control tests of an aluminum beam were conducted in the University of Houston lab, while wireless active SHM tests of a wind turbine blade were performed in the Harbin Institute of Technology wind tunnel. The results showed that the developed amplifier provided equivalent results to commercial solutions in suppressing structural vibrations, and that it allows researchers to perform active wireless SHM on moving objects with no power wires from the grid.

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.1-8
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• 2009

The proposed real-time structural health monitoring(SHM) system in past transferred and received data, central server gathered data from sensors, through coaxial cable. an immense sum of money is required to structure sensor network using coaxial cable. This paper proposes USN-based structural health monitoring(SHM). AIso, this paper designs and realizes prototypes according to proposed SHM. The value of sensing data obtained through HSDPA transfer to the BMS(Bridge Monitoring Server) passing through the TCP / IP socket by building two-way communication system, We have implemented a complete graph converting full system.

• 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.

• International Journal of High-Rise Buildings
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• v.11 no.3
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• pp.221-226
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• 2022

Installation of Structural Health Monitoring (SHM) system is a legal obligation for high-rise buildings over 200 m or 50-floor high in South Korea. CNP Dongyang has developed key technologies for SHM system design, installation, and data analyzing. Also, CNP Dongyang has applied SHM technology to a plenty of South Korea's representative high-rise buildings. The SHM technology, also, could be used in safety management of construction phase, evaluation of structural performance, etc. In this paper, state of the art SHM technologies and their application examples are introduced to give insight for future research and practical use of SHM.

• Geophysics and Geophysical Exploration
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• v.27 no.2
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• pp.144-153
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• 2024

Recent research is increasingly focused on utilizing seismic waves for structure health monitoring (SHM). Specifically, seismic interferometry, a technique applied in geophysical surveys using ambient noise, is widely applied in SHM. This method involves analyzing the response of buildings to propagating seismic waves. This enables the estimation of changes in structural stiffness and the evaluation of the location and presence of damage. Analysis of seismic interferometry applied to SHM, along with case studies, indicates its highly effective application for assessing structural stability and monitoring building conditions. Seismic interferometry is thus recognized as an efficient approach for evaluating building integrity and damage detection in SHM and monitoring applications.

• 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.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.751-760
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• 2017

In recent years, multifractal-based analysis methods have been widely applied in engineering. Among these methods, multifractal detrended cross-correlation analysis (MFDXA), a branch of fractal analysis, has been successfully applied in the fields of finance and biomedicine. For its great potential in reflecting the subtle characteristic among signals, a structural health monitoring (SHM) system based on MFDXA is proposed. In this system, damage assessment is conducted by exploiting the concept of multifractal theory to quantify the complexity of the vibration signal measured from a structure. According to the proposed algorithm, the damage condition is first distinguished by multifractal detrended fluctuation analysis. Subsequently, the relationship between the q-order, q-order detrended covariance, and length of segment is further explored. The dissimilarity between damaged and undamaged cases is visualized on contour diagrams, and the damage location can thus be detected using signals measured from different floors. Moreover, a damage index is proposed to efficiently enhance the SHM process. A seven-story benchmark structure, located at the National Center for Research on Earthquake Engineering (NCREE), was employed for an experimental verification to demonstrate the performance of the proposed SHM algorithm. According to the results, the damage condition and orientation could be correctly identified using the MFDXA algorithm and the proposed damage index. Since only the ambient vibration signal is required along with a set of initial reference measurements, the proposed SHM system can provide a lower cost, efficient, and reliable monitoring process.