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Journal articles on the topic 'Structural analysis (Engineering) Structural health monitoring'

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Published: 4 June 2021
Last updated: 7 February 2022

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1

Wang, Xin, and Wei Bing Hu. "Structural Health Monitoring for Steel Structures." Applied Mechanics and Materials 351-352 (August 2013): 1088–91. http://dx.doi.org/10.4028/www.scientific.net/amm.351-352.1088.

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The process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring. Many different types and degrees accidents take place, especially some important collapse accidents, the significance of steel structural health monitoring has been recognized. The introduction begins with a brief research status of steel structural health monitoring in china and the world. The paper analyzes the projects and contents of steel structures monitoring from nine aspects and summarizes the diagnosis methods of steel structural damages which include power fingerprint analysis, the methods of model correction and system identification, neural network methods, genetic algorithm and wavelet analysis, it provides us theoretical guidence. In conclusion, structural health monitoring for steel structures could reduce the impact of such disasters immediately after natural hazards and man-made disasters both economically and socially, thus it is becoming increasingly important.
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Rajic, Nik, Steve C. Galea, and David Rowlands. "Thermoelastic Stress Analysis - Emerging Opportunities in Structural Health Monitoring." Key Engineering Materials 558 (June 2013): 501–9. http://dx.doi.org/10.4028/www.scientific.net/kem.558.501.

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The emergence recently of a thermoelastic stress analysis (TSA) capability exploiting low-cost, compact and rugged microbolometer detector technology provides a significant opportunity topromote a broader use of this powerful non-contact full-field stress analysis technique. An area whereit has considerable and hitherto unexplored potential is in in-situ structural health monitoring (SHM).The present paper outlines the case for a nexus between SHMand TSA in this new form. It is proposedthat the approach should yield diagnostic and prognostic capabilities surpassing those of some existingSHM modalities. An F/A-18 centre-fuselage full-scale structural fatigue test is employed as a casestudy to illustrate the practical feasibility of the approach and to underscore some of its potential.Although the case study focuses on an aircraft structure, the concept has potential application to awide variety of different engineering assets across the aerospace, civil and maritime sectors.
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Krüger, Markus, Christian U. Grosse, and Pedro José Marrón. "Wireless Structural Health Monitoring Using MEMS." Key Engineering Materials 293-294 (September 2005): 625–34. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.625.

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So far, the inspection of building structures and especially of bridges is mainly done visually. Therefore, the condition of the structure is examined from the surface and the interpretation and assessment is based on the experience of the expert. However, the main purpose of monitoring civil structures is not to substitute visual inspection. Continuous structural health monitoring should provide data from the inside of a structure to better understand its structural performance and to predict its durability and remaining life time. Monitoring should render objective data and observable alterations in the structure continuously, which cannot be done by visual inspection. More detailed information is needed with respect to different exposure due to dynamic and static loads and also temperature and moisture. Today mainly wired monitoring systems are used to monitor structures, which are relatively expensive and time consuming to install. In this paper the basic principle of a wireless monitoring system equipped with MEMS sensors is presented, which can be easily installed at different structures. Microelectromechanical systems (MEMS) are small integrated devices or systems that combine electrical and mechanical components. A wireless monitoring sensor network equipped with such MEMS could be produced with a very low budget and becomes very efficient. This permits a wide area of applications not only in civil engineering. With respect to different applications relevant properties of a wireless monitoring system are described. In detail network configuration, power consumption, data acquisition and data aggregation, signal analysis and data reduction as well as reliability and robustness are discussed.
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Li, Qiang, Chun Xiao, Wei Li, Li Qiao Li, and Hui Liu. "Research on Data Correlation in Structural Health Monitoring System." Advanced Materials Research 671-674 (March 2013): 2044–48. http://dx.doi.org/10.4028/www.scientific.net/amr.671-674.2044.

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In structural health monitoring system, data analysis is one of the most important parts. It is mainly for processing and analysis of the collected data, among of which, the correlation analysis of the collected data can be used to verify the feasibility of the system. This paper applies the method of wavelet de-noising analysis to reduce signal noise and utilizes MATLAB and LabVIEW to calculate the cross-correlation coefficient in simulation statistics independently. To verify the feasibility of correlation analysis method and the data processing, simulation study is finished based on sampled data, which are the information of measuring points of strain and temperature from the Baishazhou Bridge. The cross-correlation coefficients between various signals can provide the reference to the whole health status of the civil engineering structure, and then enhance the accuracy of structural health assessment.
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Zeng, Lei, Yiping Liu, Ge Zhang, Liqun Tang, Zhenyu Jiang, and Zejia Liu. "Analysis of structural responses of bridges based on long-term structural health monitoring." Mechanics of Advanced Materials and Structures 25, no. 1 (October 3, 2016): 79–86. http://dx.doi.org/10.1080/15376494.2016.1243283.

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6

Mykola Sysyn, Olga Nabochenko, Franziska Kluge, Vitalii Kovalchuk, and Andriy Pentsak. "Common Crossing Structural Health Analysis with Track-Side Monitoring." Communications - Scientific letters of the University of Zilina 21, no. 3 (August 15, 2019): 77–84. http://dx.doi.org/10.26552/com.c.2019.3.77-84.

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Track-side inertial measurements on common crossings are the object of the present study. The paper deals with the problem of measurement's interpretation for the estimation of the crossing structural health. The problem is manifested by the weak relation of measured acceleration components and impact lateral distribution to the lifecycle of common crossing rolling surface. The popular signal processing and machine learning methods are explored to solve the problem. The Hilbert-Huang Transform (HHT) method is used to extract the time-frequency features of acceleration components. The method is based on Ensemble Empirical Mode Decomposition (EEMD) that is advantageous to the conventional spectral analysis methods with higher frequency resolution and managing nonstationary nonlinear signals. Linear regression and Gaussian Process Regression are used to fuse the extracted features in one structural health (SH) indicator and study its relation to the crossing lifetime. The results have shown the significant relation of the derived with GPR indicator to the lifetime.
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Webb, G. T., P. J. Vardanega, P. R. A. Fidler, and C. R. Middleton. "Analysis of Structural Health Monitoring Data from Hammersmith Flyover." Journal of Bridge Engineering 19, no. 6 (June 2014): 05014003. http://dx.doi.org/10.1061/(asce)be.1943-5592.0000587.

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8

Lu, Ping, Brent M. Phares, Lowell Greimann, and Terry J. Wipf. "Bridge Structural Health–Monitoring System Using Statistical Control Chart Analysis." Transportation Research Record: Journal of the Transportation Research Board 2172, no. 1 (January 2010): 123–31. http://dx.doi.org/10.3141/2172-14.

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9

da Silva, Samuel, Milton Dias Júnior, and Vicente Lopes Junior. "Structural Health Monitoring in Smart Structures Through Time Series Analysis." Structural Health Monitoring: An International Journal 7, no. 3 (July 21, 2008): 231–44. http://dx.doi.org/10.1177/1475921708090561.

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10

Yang, Jian-Ping, Wei-Zhong Chen, Ming Li, Xian-Jun Tan, and Jian-xin Yu. "Structural health monitoring and analysis of an underwater TBM tunnel." Tunnelling and Underground Space Technology 82 (December 2018): 235–47. http://dx.doi.org/10.1016/j.tust.2018.08.053.

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11

Huang, Guojian, Dong Hui Wang, Xin Hua Wang, and Zhen Yu He. "Software System Development of Crane Structural Health Monitoring." Advanced Materials Research 774-776 (September 2013): 1599–603. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1599.

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In order to improve the level of safety technology and safeguard work efficiency, software is developed. The software is divided into several functional modules: User information Management Module, System Settings Module, Information Collection and Transmission Module, Database Management Module, Data Analysis Module, Site Display Module and User Help Module. This paper focuses on the development and application of the system software. Sensors data is collecting by a FBG System, and directly send into Crane Structure Health Monitoring and Security Alarm Expert System via data filtering and data correction procedures. Users can access the expert systems data network and expert system can send warning or alarm information to user via Wi-Fi. Its the main part of the software platform. After software functional use cases,the next thing to do is the dynamic behavior of software modeling. The dynamic behavior is related to information exchange between users and software, software and peripherals.
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12

Chaabane, Marwa, Majdi Mansouri, Kamaleldin Abodayeh, Ahmed Ben Hamida, Hazem Nounou, and Mohamed Nounou. "Effective fault detection in structural health monitoring systems." Advances in Mechanical Engineering 11, no. 9 (September 2019): 168781401987323. http://dx.doi.org/10.1177/1687814019873234.

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A new fault detection technique is considered in this article. It is based on kernel partial least squares, exponentially weighted moving average, and generalized likelihood ratio test. The developed approach aims to improve monitoring the structural systems. It consists of computing an optimal statistic that merges the current information and the previous one and gives more weight to the most recent information. To improve the performances of the developed kernel partial least squares model even further, multiscale representation of data will be used to develop a multiscale extension of this method. Multiscale representation is a powerful data analysis way that presents efficient separation of deterministic characteristics from random noise. Thus, multiscale kernel partial least squares method that combines the advantages of the kernel partial least squares method with those of multiscale representation will be developed to enhance the structural modeling performance. The effectiveness of the proposed approach is assessed using two examples: synthetic data and benchmark structure. The simulation study proves the efficiency of the developed technique over the classical detection approaches in terms of false alarm rate, missed detection rate, and detection speed.
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13

Karatas, Cansu, Boray Degerliyurt, Yavuz Yaman, and Melin Sahin. "Fibre Bragg grating sensor applications for structural health monitoring." Aircraft Engineering and Aerospace Technology 92, no. 3 (December 24, 2018): 355–67. http://dx.doi.org/10.1108/aeat-11-2017-0255.

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Purpose Structural health monitoring (SHM) has become an attractive subject in aerospace engineering field considering the opportunity to avoid catastrophic failures by detecting damage in advance and to reduce maintenance costs. Fibre Bragg Grating (FBG) sensors are denoted as one of the most promising sensors for SHM applications as they are lightweight, immune to electromagnetic effects and able to be embedded between the layers of composite structures. The purpose of this paper is to research on and demonstrate the feasibility of FBG sensors for SHM of composite structures. Design/methodology/approach Applications on thin composite beams intended for SHM studies are presented. The sensor system, which includes FBG sensors and related interrogator system, and manufacturing of the beams with embedded sensors, are detailed. Static tension and torsion tests are conducted to verify the effectiveness of the system. Strain analysis results obtained from the tests are compared with the ones obtained from the finite element analyses conducted using ABAQUS® software. In addition, the comparison between the data obtained from the FBG sensors and from the strain gauges is made by also considering the noise content. Finally, fatigue test under torsion load is conducted to observe the durability of FBG sensors. Findings The results demonstrated that FBG sensors are feasible for SHM of composite structures as the strain data are accurate and less noisy compared to that obtained from the strain gauges. Furthermore, the convenience of obtaining reliable data between the layers of a composite structure using embedded FBG sensors is observed. Practical implications Observing the advantages of the FBG sensors for strain measurement will promote using FBG sensors for damage detection related to the SHM applications. Originality/value This paper presents applications of FBG sensors on thin composite beams, which reveal the suitability of FBG sensors for SHM of lightweight composite structures.
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14

Mobaraki, Behnam, Haiying Ma, Jose Antonio Lozano Galant, and Jose Turmo. "Structural Health Monitoring of 2D Plane Structures." Applied Sciences 11, no. 5 (February 24, 2021): 2000. http://dx.doi.org/10.3390/app11052000.

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This paper presents the application of the observability technique for the structural system identification of 2D models. Unlike previous applications of this method, unknown variables appear both in the numerator and the denominator of the stiffness matrix system, making the problem non-linear and impossible to solve. To fill this gap, new changes in variables are proposed to linearize the system of equations. In addition, to illustrate the application of the proposed procedure into the observability method, a detailed mathematical analysis is presented. Finally, to validate the applicability of the method, the mechanical properties of a state-of-the-art plate are numerically determined.
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15

Emelianov, Mikhail. "Considering the information technology for structural health monitoring (SHM) systems." E3S Web of Conferences 97 (2019): 03011. http://dx.doi.org/10.1051/e3sconf/20199703011.

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The purpose of the research is to develop information technology for designing systems for monitoring load-bearing structures elements of buildings and structures of a high responsibility category based on the information about on the construction site and engineering survey results to obtain the automatic mode of results required for the development of these systems. The article describes the procedure for developing structural health monitoring (SHM) projects for the building and structures, based on the specific features of the development control and measuring system for building and structures and the engineering experience of designing these systems. The article presents the algorithms and macros that form the basis of the developed information technology, recommendations for determining the control parameters, the composition of the measuring equipment. The fundamentals of information technology development outlined in the article allow the development of algorithms and programs for the automated analysis of information about the construction and the construction site. The result of the analysis is the data necessary for the development of monitoring systems for buildings and structures of a higher category of responsibility.
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16

Ozer, Ekin, and Maria Q. Feng. "Structural Reliability Estimation with Participatory Sensing and Mobile Cyber-Physical Structural Health Monitoring Systems." Applied Sciences 9, no. 14 (July 16, 2019): 2840. http://dx.doi.org/10.3390/app9142840.

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With the help of community participants, smartphones can become useful wireless sensor network (WSN) components, form a self-governing structural health monitoring (SHM) system, and merge structural mechanics with participatory sensing and server computing. This paper presents a methodology and framework of such a cyber-physical system (CPS) that generates a bridge finite element model (FEM) integrated with vibration measurements from smartphone WSNs and centralized/distributed computational facilities, then assesses structural reliability based on updated FEMs. Structural vibration data obtained from smartphones are processed on a server to identify modal frequencies of an existing bridge. Without design drawings and supportive documentation but field measurements and observations, FEM of the bridge is drafted with uncertainties in the structural mass, stiffness, and boundary conditions (BCs). Then, 2700 FEMs are autonomously generated, and the baseline FEM is updated by minimizing the error between the crowdsourcing-based modal identification results and the FEM analysis. Furthermore, using 151 strong ground motion records from databases, the bridge response time history simulations are conducted to obtain displacement demand distribution. Finally, based on reference performance criteria, structural reliability of the bridge is estimated. Integrating the cyber (FEM analysis) and the physical (the bridge structure and measured vibration characteristics) worlds, this crowdsourcing-based CPS can provide a powerful tool for supporting rapid, remote, autonomous, and objective infrastructure-related decision-making. This study presents a new example of the emerging fourth industrial revolution from structural engineering and SHM perspective.
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17

Park, Gyuhae, Amanda C. Rutherford, Hoon Sohn, and Charles R. Farrar. "An outlier analysis framework for impedance-based structural health monitoring." Journal of Sound and Vibration 286, no. 1-2 (August 2005): 229–50. http://dx.doi.org/10.1016/j.jsv.2004.10.013.

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18

Rainieri, Carlo, Filipe Magalhaes, and Filippo Ubertini. "Automated Operational Modal Analysis and Its Applications in Structural Health Monitoring." Shock and Vibration 2019 (November 6, 2019): 1–3. http://dx.doi.org/10.1155/2019/5497065.

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19

Lee, Jong Won, Goutham R. Kirikera, Inpil Kang, Mark J. Schulz, and Vesselin N. Shanov. "Structural health monitoring using continuous sensors and neural network analysis." Smart Materials and Structures 15, no. 5 (August 24, 2006): 1266–74. http://dx.doi.org/10.1088/0964-1726/15/5/016.

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20

Zaurin, Ricardo, and F. Necati Catbas. "Structural health monitoring using video stream, influence lines, and statistical analysis." Structural Health Monitoring: An International Journal 10, no. 3 (June 28, 2010): 309–32. http://dx.doi.org/10.1177/1475921710373290.

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21

Rainieri, Carlo, Giovanni Fabbrocino, and E. Cosenza. "Automated Operational Modal Analysis as Structural Health Monitoring Tool: Theoretical and Applicative Aspects." Key Engineering Materials 347 (September 2007): 479–84. http://dx.doi.org/10.4028/www.scientific.net/kem.347.479.

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The aim of structural health monitoring for civil structures is not only detection of sudden or progressive damages but also monitoring their performance under operational conditions or under some particular environmental issues such as earthquakes. Seismic protection of buildings at risk can be reached increasing the knowledge of the structural behavior of existing constructions. This circumstance points out the opportunity of monitoring the performance of civil structures over their operational lives. The present paper deals with automated Structural Health Monitoring (SHM) technologies adopted for the School of Engineering Main Building at the University of Naples “Federico II”. In particular, the attention is focused on the development of an automated procedure based on the Operational Modal Analysis (OMA) that must ensure the continuous monitoring and extraction of the modal parameters of the building. Some numerical examples are then discussed in order to point out effectiveness of the algorithm and relevant issues that need to be improved.
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22

Ai, Demi, Chengxing Lin, Hui Luo, and Hongping Zhu. "Temperature effect on electromechanical admittance–based concrete structural health monitoring." Structural Health Monitoring 19, no. 3 (July 9, 2019): 661–92. http://dx.doi.org/10.1177/1475921719860397.

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Concrete structures in service are often subjected to environmental/operational temperature effects, which change their inherent properties and also inflict a challenge to their extrinsic monitoring systems. Recently, piezoelectric lead zirconate titanate (PZT)-based electromechanical admittance technique has been increasingly growing into an effective tool for concrete structural health monitoring; however, uncertainty in the changes of monitoring signals induced by temperature impact on concrete/PZT sensor would inevitably cause interference to structural damage detection, which adversely hinder its application from laboratory to engineering practice. This article, aiming at exploring the temperature effect on the electromechanical admittance–based concrete damage evaluation, primarily covered a series of theoretical/numerical analysis with rigorously experimental verifications. Three aspects of comparative studies were performed in theoretical/numerical analysis: (1) thermal-dependent parameters were inclusively evaluated in contribution to the electromechanical admittance characteristics via PZT-structure interaction models; (2) three-dimensional finite element analysis in multi-physics coupled field was employed to qualitatively assess the singular temperature effect on the electromechanical admittance behaviors of free-vibrated PZT, surface-bonded PZT/inside-embedded PZT coupled healthy concrete cubes; and (3) depending on the modeling of surface-bonded PZT-/inside-embedded PZT-cracked concrete cube, thermal effect on damage evaluation was addressed via quantification on the electromechanical admittance variations. In the experimental study, rigorous validation tests were carried out on a group of lab-scale concrete cubes, where surface-bonded PZT/inside-embedded PZT transducers were simultaneously employed for electromechanical admittance monitoring in view of thermal difference between concrete surface and its inner part. Correlation coefficient deviation value-based effective frequency shifts algorithm was also employed to compensate the temperature effect. Moreover, temperature effect was further testified on the monitoring of a full-scale shield-tunnel segment structure. Experimental results indicated that temperature triggered different behaviors of electromechanical admittance signatures for surface-bonded PZT/inside-embedded PZT transducers and contaminated the electromechanical admittance responses for damage detection. Structural damage severity level can be disadvantageously amplified by temperature increment even if under the same damage scenarios.
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23

Staszewski, Wiesław J., and Amy N. Robertson. "Time–frequency and time–scale analyses for structural health monitoring." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, no. 1851 (December 14, 2006): 449–77. http://dx.doi.org/10.1098/rsta.2006.1936.

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Signal processing is one of the most important elements of structural health monitoring. This paper documents applications of time-variant analysis for damage detection. Two main approaches, the time–frequency and the time–scale analyses are discussed. The discussion is illustrated by application examples relevant to damage detection.
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Peairs, Daniel M., Pablo A. Tarazaga, and Daniel J. Inman. "Frequency Range Selection for Impedance-Based Structural Health Monitoring." Journal of Vibration and Acoustics 129, no. 6 (July 26, 2007): 701–9. http://dx.doi.org/10.1115/1.2775506.

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Impedance-based structural health monitoring uses collocated piezoelectric transducers to locally excite a structure at high frequencies. The response of the structure is measured by the same transducer. Changes in this response indicate damage. Frequency range selection for monitoring with impedance-based structural health monitoring has, in the past, been done by trial and error methods or has been selected after analysis by engineers familiar with the method. This study aims to determine if, in future applications, it is possible to automatically select preferred frequency ranges based on sensor characteristics, perhaps even before installing the system. In addition, the paper demonstrates a method for determining preferable frequency ranges for monitoring. The study examines the analysis of the measurement change through a damage metric and relates the results of the analysis to characteristics of the measurement. Specifically, outlier detection concepts were used to statistically evaluate the damage detection ability of the transducers at various frequency ranges. The variation in undamaged measurements is compared to the amount of change in the measurement upon various levels of damage. Testing was performed with both solid piezoceramic transducers and macrofiber composite piezoelectric devices of different sizes bonded to aluminum and fiber reinforced composite structures. The results indicate that characteristics of the structure, not the sensor alone, determine the optimal monitoring frequency ranges.
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Wu, Qi, Yoji Okabe, and Fengming Yu. "Ultrasonic Structural Health Monitoring Using Fiber Bragg Grating." Sensors 18, no. 10 (October 11, 2018): 3395. http://dx.doi.org/10.3390/s18103395.

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The fiber Bragg grating (FBG) sensor, which was developed over recent decades, has been widely used to measure manifold static measurands in a variety of industrial sectors. Multiple experiments have demonstrated its ability in ultrasonic detection and its potential in ultrasonic structural health monitoring. Unlike static measurements, ultrasonic detection requires a higher sensitivity and broader bandwidth to ensure the fidelity of the ultrasonic Lamb wave that propagates in a plate-like structure for the subsequent waveform analysis. Thus, the FBG sensor head and its corresponding demodulation system need to be carefully designed, and other practical issues, such as the installation methods and data process methods, should also be properly addressed. In this review, the mature techniques of FBG-based ultrasonic sensors and their practical applications in ultrasonic structural health monitoring are discussed. In addition, state-of-the-art techniques are introduced to fully present the current developments.
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26

Emelianov, M. V. "THE DEVELOPMENT OF INFORMATION TECHNOLOGY OF STRUCTURAL HEALTH MONITORING SYSTEMS." Herald of Dagestan State Technical University. Technical Sciences 46, no. 1 (July 16, 2019): 123–31. http://dx.doi.org/10.21822/2073-6185-2019-46-1-123-131.

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Objectives. The aim of the study is to develop information technology design systems for monitoring load-bearing structures of buildings and structures of a higher category of responsibility based on the initial data on the construction object and the results of engineering surveys.Method. The article discusses the approach to the formalization of the description of the procedure for developing projects for monitoring buildings and structures, based on the design features of the SMC building structures, the engineering experience of designing these systems.Result. The article presents the conceptual foundations of the developed information technology, the algorithm for determining the monitoring mode based on information about the construction object, the algorithm for the macro to determine the most loaded nodes and elements in the complex finite element modeling. Recommendations are given for determining the control parameters, the composition of the measuring equipment.Conclusion. The conceptual foundations of information technology developed in the course of the research allow for the further development of algorithms and programs for the automated analysis of information about the object and the construction site in order to obtain the automatic mode of the results necessary for developing monitoring systems for buildings and structures of a higher category of responsibility.
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Zhao, Xiukuan, Ruolin Wang, Haichang Gu, Gangbing Song, and Y. L. Mo. "Innovative Data Fusion Enabled Structural Health Monitoring Approach." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/369540.

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Piezoceramic-based active sensing is a useful approach to structural health monitoring. This approach often involves a large number of distributed piezoceramic transducers. It may be confusing to incorporate each sensor data. It is desired to develop an automated health monitoring approach to obtain a comprehensive and accurate health monitoring result by simultaneously interpreting data from all sensors. In this paper, an innovative data fusion enabled structural health monitoring (SHM) approach based on the Dempster-Shafer (D-S) evidence theory is proposed to obtain comprehensive SHM results for a distributed sensor network in a civil infrastructure. Considering that evidence from multiple different information sources (sensor data) has different levels of significance, not all evidence is equivalently effective for the final decision. A weighted fusion damage index (WFDI) is proposed to perform damage identification based on the authors’ recently developed piezoceramic-based smart aggregates. Experimental data of a two-story concrete frame was used to study the effectiveness of the proposed weighted fusion damage index. Analyses show that the proposed weighted fusion damage index can reveal the damage status of different areas of the frame. The results are consistent with the visual inspection of the cracks on the concrete frame.
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Tondut, Jeanne, J. Geoffrey Chase, and Cong Zhou. "Automated structural dynamic modelling using model-free health monitoring results." Bulletin of the New Zealand Society for Earthquake Engineering 53, no. 4 (December 1, 2020): 189–202. http://dx.doi.org/10.5459/bnzsee.53.4.189-202.

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Structural health monitoring (SHM) methods provide damage metrics and localisation, but not a means of answering subsequent questions concerning immediate or long-term damage mitigation, risk, or safety in re-occupancy. Models based on the SHM results would provide a means to test these issues, but typically require extensive human input, which is not available immediately after an event to enhance and optimise immediate decision-making. This work presents a simple, readily automated modelling approach to translate SHM results from the proven hysteresis loop analysis (HLA) method into foundation models for immediate use. Experimental data from a 3-storey structure tested at the E-Defense facility in Japan are used to assess model performance. The model’s ability to capture the essential dynamics is assessed by comparing peak dynamic displacement and cross correlation coefficient (Rcoeff). For all 6 events, 3 storeys, and 2 directions, median (5-95% Range) of peak displacement error was 0.82 (0.17, 4.09) mm, and average Rcoeff = 0.82, all of which were significantly improved if the worst event was excluded. Overall, accurate nonlinear, time-varying baseline models were created using data from SHM damage identification and localisation methods using relatively quite simple model structures. The method is readily automated via algorithm, and the models were suitable for initial investigation and analysis on safety, damage mitigation, and thus re-occupancy. Such models could take SHM from being a tool for damage identification and extend it into further decision-making, creating far greater utility for engineers and owners, which could further spur impetus for investment in monitoring.
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Devriendt, Christof, Filipe Magalhães, Wout Weijtjens, Gert De Sitter, Álvaro Cunha, and Patrick Guillaume. "Structural health monitoring of offshore wind turbines using automated operational modal analysis." Structural Health Monitoring 13, no. 6 (November 2014): 644–59. http://dx.doi.org/10.1177/1475921714556568.

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This article will present and discuss the approach and the first results of a long-term dynamic monitoring campaign on an offshore wind turbine in the Belgian North Sea. It focuses on the vibration levels and modal parameters of the fundamental modes of the support structure. These parameters are crucial to minimize the operation and maintenance costs and to extend the lifetime of offshore wind turbine structure and mechanical systems. In order to perform a proper continuous monitoring during operation, a fast and reliable solution, applicable on an industrial scale, has been developed. It will be shown that the use of appropriate vibration measurement equipment together with state-of-the art operational modal analysis techniques can provide accurate estimates of natural frequencies, damping ratios, and mode shapes of offshore wind turbines. The identification methods have been automated and their reliability has been improved, so that the system can track small changes in the dynamic behavior of offshore wind turbines. The advanced modal analysis tools used in this application include the poly-reference least squares complex frequency-domain estimator, commercially known as PolyMAX, and the covariance-driven stochastic subspace identification method. The implemented processing strategy will be demonstrated on data continuously collected during 2 weeks, while the wind turbine was idling or parked.
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Singh, Priyanka, Umaid Faraz Ahmad, and Siddharth Yadav. "Structural Health Monitoring and Damage Detection through Machine Learning approaches." E3S Web of Conferences 220 (2020): 01096. http://dx.doi.org/10.1051/e3sconf/202022001096.

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Data-driven approaches are gaining popularity in structural health monitoring (SHM) due to recent technological advances in sensors, high-speed Internet and cloud computing. Since Machine learning (ML), particularly in SHM, was introduced in civil engineering, this modern and promising method has drawn significant research attention. SHM’s main goal is to develop different data processing methodologies and generate results related to the different levels of damage recognition process. SHM implements a technique for damage detection and classification, including data from a system collected under different structural states using a piezoelectric sensor network using guided waves, hierarchical non-linear primary component analysis and machine learning. The primary objective of this paper is to analyse the current SHM literature using evolving ML-based methods and to provide readers with an overview of various SHM applications. The technique and implementation of vibration-based, vision-based surveillance, along with some recent SHM developments are discussed.
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Sivasuriyan, Arvindan, Dhanasingh Sivalinga Vijayan, Wojciech Górski, Łukasz Wodzyński, Magdalena Daria Vaverková, and Eugeniusz Koda. "Practical Implementation of Structural Health Monitoring in Multi-Story Buildings." Buildings 11, no. 6 (June 20, 2021): 263. http://dx.doi.org/10.3390/buildings11060263.

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This study investigated operational and structural health monitoring (SHM) as well as damage evaluations for building structures. The study involved damage detection and the assessment of buildings by placing sensors and by assuming weak areas, and considered situations of assessment and self-monitoring. From this perspective, advanced sensor technology and data acquisition techniques can systematically monitor a building in real time. Furthermore, the structure’s response and behavior were observed and recorded to predict the damage to the building. In this paper, we discuss the real-time monitoring and response of buildings, which includes both static and dynamic analyses along with numerical simulation studies such as finite element analysis (FEA), and recommendations for the future research and development of SHM are made.
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Kot, Patryk, Magomed Muradov, Michaela Gkantou, George S. Kamaris, Khalid Hashim, and David Yeboah. "Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring." Applied Sciences 11, no. 6 (March 18, 2021): 2750. http://dx.doi.org/10.3390/app11062750.

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Structural health monitoring (SHM) is an important aspect of the assessment of various structures and infrastructure, which involves inspection, monitoring, and maintenance to support economics, quality of life and sustainability in civil engineering. Currently, research has been conducted in order to develop non-destructive techniques for SHM to extend the lifespan of monitored structures. This paper will review and summarize the recent advancements in non-destructive testing techniques, namely, sweep frequency approach, ground penetrating radar, infrared technique, fiber optics sensors, camera-based methods, laser scanner techniques, acoustic emission and ultrasonic techniques. Although some of the techniques are widely and successfully utilized in civil engineering, there are still challenges that researchers are addressing. One of the common challenges within the techniques is interpretation, analysis and automation of obtained data, which requires highly skilled and specialized experts. Therefore, researchers are investigating and applying artificial intelligence, namely machine learning algorithms to address the challenges. In addition, researchers have combined multiple techniques in order to improve accuracy and acquire additional parameters to enhance the measurement processes. This study mainly focuses on the scope and recent advancements of the Non-destructive Testing (NDT) application for SHM of concrete, masonry, timber and steel structures.
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Aung, Thein Lin, Ninshu Ma, Kinzo Kishida, and Artur Guzik. "Advanced Structural Health Monitoring Method by Integrated Isogeometric Analysis and Distributed Fiber Optic Sensing." Sensors 21, no. 17 (August 28, 2021): 5794. http://dx.doi.org/10.3390/s21175794.

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Attempts in digital management of structures are among the most popular topics in the trend of Information of Things (IoT). However, the implementation lags behind. This work recognized that Computer Aided Design (CAD) comprises the core of modern engineering; thus, most digital information can be available if CAD is used not only in design but also for life cycle structural health monitoring (SHM). Based on this concept, the newly designed method utilizes the isogeometric analysis (IGA) tool to include the Distributed Fiber Optic Sensing (DFOS) information by proposing a fiber mesh model. The IGA model can be obtained directly from CAD, and the boundary conditions can be provided directly or indirectly from DFOS in real time and remotely. Hence a practical method of SHM is able to achieve highly efficient and accurate numerical model creation, which can even accommodate non-linear constitutive property of materials. The proposed method was applied to a pipe deformation model as an example. The inverse analysis method is also shown to determine the contact force for loading on the pipe, which shows the potential for many engineering applications.
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Wang, Hao, Aiqun Li, Tong Guo, and Tianyou Tao. "Establishment and Application of the Wind and Structural Health Monitoring System for the Runyang Yangtze River Bridge." Shock and Vibration 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/421038.

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Structural health monitoring can provide a practical platform for detecting the evolution of structural damage or performance deterioration of engineering structures. The final objective is to provide reasonable suggestions for structural maintenance and management and therefore ensure the structural safety according to the real-time recorded data. In this paper, the establishment of the wind and structural health monitoring system (WSHMS) implemented on the Runyang Yangtze River Bridge (RYRB) in China is introduced. The composition and functions of the WSHMS are presented. Thereinto, the sensory subsystem utilized to measure the input actions and structural output responses is introduced. And the core functions of the data management and analysis subsystem (DMAS) including model updating, structural condition identification, and structural condition assessment are illustrated in detail. A three-stage strategy is applied into the FE model updating of RYRB, and a two-phase strategy is proposed to adapt to structural health diagnosis and damage identification. Considering the structural integral security and the fatigue characteristic of steel material, the condition assessment of RYRB is divided into structural reliability assessment and structural fatigue assessment, which are equipped with specific and elaborate module for effective operation. This research can provide references for the establishment of the similar structural health monitoring systems on other cable-supported bridges.
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35

Worden, Keith, and Graeme Manson. "The application of machine learning to structural health monitoring." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, no. 1851 (December 12, 2006): 515–37. http://dx.doi.org/10.1098/rsta.2006.1938.

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In broad terms, there are two approaches to damage identification. Model-driven methods establish a high-fidelity physical model of the structure, usually by finite element analysis, and then establish a comparison metric between the model and the measured data from the real structure. If the model is for a system or structure in normal (i.e. undamaged) condition, any departures indicate that the structure has deviated from normal condition and damage is inferred. Data-driven approaches also establish a model, but this is usually a statistical representation of the system, e.g. a probability density function of the normal condition. Departures from normality are then signalled by measured data appearing in regions of very low density. The algorithms that have been developed over the years for data-driven approaches are mainly drawn from the discipline of pattern recognition, or more broadly, machine learning. The object of this paper is to illustrate the utility of the data-driven approach to damage identification by means of a number of case studies.
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Xiao, Chun, Xue Ping Hao, Li Qiao Li, Wei Li, and Xun Gang Liu. "Research on Data Prediction Methods of Structural Health Monitoring System." Applied Mechanics and Materials 256-259 (December 2012): 1022–28. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.1022.

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Trend prediction is virtually modeling process for dynamic data. The key to prediction is to establish a model in accordance with actual status, then use the model to predict the trend of object, and infer its behavior in future. Two prediction methods are researched to predict the trend on the observed points of the structure in this paper, which are regression prediction method and grey prediction method. The continuous time strain value of a measured point on Tianxingzhou Yangtze River Bridge is used as data sample for researching. The method of regression analysis is applied for predicting the trend of short-term data, and the method of grey model prediction for predicting long-term data. Regression prediction can assess the health status of the structure and obtain the alarm information effectively by comparing the actual monitoring data with the range of forecast interval. Grey prediction method has great advantages when dealing with poor information. By engineering example this study shows the pros and cons of these two methods, and proves that the method of grey model prediction is more suitable of predicting the trend of object in the structural health monitoring system.
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Taha, M. M. Reda, A. Noureldin, A. Osman, and N. El-Sheimy. "Introduction to the use of wavelet multiresolution analysis for intelligent structural health monitoring." Canadian Journal of Civil Engineering 31, no. 5 (October 1, 2004): 719–31. http://dx.doi.org/10.1139/l04-022.

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This paper suggests the use of wavelet multiresolution analysis (WMRA) as a reliable tool for digital signal processing in structural health monitoring (SHM) systems. A damage occurrence detection algorithm using WMRA augmented with artificial neural networks (ANN) is described. The suggested algorithm allows intelligent monitoring of structures in real time. The probability of damage occurrence is determined by evaluating the wavelet norm index (WNI) representing the energy of a signal describing the change in the system dynamics due to damage. An example application of the proposed algorithm is presented using a finite element simulated structural dynamics of a prestressed concrete bridge. The new algorithm showed very promising results.Key words: structural health monitoring, neural networks, wavelet analysis, signal processing, damage detection.
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Li, Chuang, Li Sun, Ziqiang Xu, Xiangxiang Wu, Tianqi Liang, and Wenlong Shi. "Experimental Investigation and Error Analysis of High Precision FBG Displacement Sensor for Structural Health Monitoring." International Journal of Structural Stability and Dynamics 20, no. 06 (April 24, 2020): 2040011. http://dx.doi.org/10.1142/s0219455420400118.

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High precision structural displacement monitoring is challenging, but an effective method for structural health monitoring and particularly damage evaluation. In this paper, a high precision fiber Bragg grating (FBG) displacement sensor with embedded spring is developed to monitor structural displacement variation even at very small ranges. The principle of such monitoring is based on the central wavelength shifts in accordance with the displacement between measuring points. Calibration experiments are conducted to examine the performance of the innovative displacement sensor. The result indicates that the sensor has excellent linearity and repeatability, with the sensitivity coefficient being 23.96[Formula: see text]pm/mm, and the static relative error is 4.94% after three loading and unloading cycles. The displacement sensor therefore shows an excellent sensitivity and high precision for application. Moreover, it has been verified that this sensor is suitable and applicable for displacement monitoring in quasi-static experiment of structures.
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Huang, Hai-Bin, Ting-Hua Yi, and Hong-Nan Li. "Anomaly Identification of Structural Health Monitoring Data Using Dynamic Independent Component Analysis." Journal of Computing in Civil Engineering 34, no. 5 (September 2020): 04020025. http://dx.doi.org/10.1061/(asce)cp.1943-5487.0000905.

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Liu, Xiangming, and Valéri L. Markine. "MBS Vehicle–Crossing Model for Crossing Structural Health Monitoring." Sensors 20, no. 10 (May 19, 2020): 2880. http://dx.doi.org/10.3390/s20102880.

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This paper presents the development of a multi-body system (MBS) vehicle–crossing model and its application in the structural health monitoring (SHM) of railway crossings. The vehicle and track configurations in the model were adjusted to best match the real-life situation. By using the measurement results obtained from an instrumented crossing and the simulation results from a finite element (FE) model, the MBS model was validated and verified. The results showed that the main outputs of the MBS model correlated reasonably well with those from both the measurements and the FE model. The MBS and FE models formed the basis of an integrated analysis tool, which can be applied to thoroughly study the performance of railway crossings. As part of the SHM system for railway crossings developed at Delft University of Technology, the MBS model was applied to identify the condition stage of a monitored railway crossing. The numerical results confirmed the highly degraded crossing condition. By using the measured degradation as the input in the MBS model, the primary damage sources were further verified. Through identifying the crossing condition stage and verifying the damage source, necessary and timely maintenance can be planned. These actions will help to avoid crossing failure and unexpected traffic interruptions, which will ultimately lead to sustainable railway infrastructure.
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41

Daliri, Ali, Amir Galehdar, Wayne ST Rowe, Kamran Ghorbani, and Sabu John. "Utilising microstrip patch antenna strain sensors for structural health monitoring." Journal of Intelligent Material Systems and Structures 23, no. 2 (December 28, 2011): 169–82. http://dx.doi.org/10.1177/1045389x11432655.

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In this article, the feasibility of using a circular microstrip patch antenna to measure strain and the effects of different materials on sensitivity of the patch antenna are investigated. Also, the effect of strain direction on the frequency shift is studied. The theoretical model shows a linear relationship between strain and the shift in the resonant frequency of the antenna in any material. Both finite element analysis (FEA) and experimental tests have been undertaken to corroborate the relationship between strain and frequency shift. In addition, a new antenna sensor based on a meandered microstrip patch antennas has been designed and tested to overcome the shortcomings of the circular patch. The meandered circular microstrip patch antenna exhibited a threefold increase in sensitivity and a fivefold reduction in its physical size, when compared to the simple circular patch. The ultimate intention of this work is to configure antennas for the detection of relatively small damage zones in structures and to do so wirelessly.
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42

Todd, Michael D., Jonathan M. Nichols, Stephen T. Trickey, Mark Seaver, Christy J. Nichols, and Lawrence N. Virgin. "Bragg grating-based fibre optic sensors in structural health monitoring." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, no. 1851 (December 12, 2006): 317–43. http://dx.doi.org/10.1098/rsta.2006.1937.

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This work first considers a review of the dominant current methods for fibre Bragg grating wavelength interrogation. These methods include WDM interferometry, tunable filter (both Fabry–Perot and acousto-optic) demultiplexing, CCD/prism technique and a newer hybrid method utilizing Fabry–Perot and interferometric techniques. Two applications using these techniques are described: hull loads monitoring on an all-composite fast patrol boat and bolt pre-load loss monitoring in a composite beam in conjunction with a state-space modelling data analysis technique.
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43

Overbey, L. A., and M. D. Todd. "Analysis of Local State Space Models for Feature Extraction in Structural Health Monitoring." Structural Health Monitoring: An International Journal 6, no. 2 (June 2007): 145–72. http://dx.doi.org/10.1177/1475921706072079.

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44

Sepehry, Naserodin, Mahnaz Shamshirsaz, and Ali Bastani. "Experimental and theoretical analysis in impedance-based structural health monitoring with varying temperature." Structural Health Monitoring 10, no. 6 (December 21, 2010): 573–85. http://dx.doi.org/10.1177/1475921710388338.

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In the recent years, the piezoelectric wafer active sensors (PWASs) are increasing as a measurement tool in structural health monitoring techniques. In impedance-based structural health monitoring (ISHM) method, the electrical impedance of a PWAS bonded to the structure is measured and served as a defect detection index of the structure. The principle of this method is based on the electromechanical coupling effect of PWAS materials. As any change in the structure will lead to a change in mechanical impedance of structure, the electrical impedance of PWAS could sense this change by the electromechanical coupling effect of PWAS. Since the physical and mechanical properties of PWAS materials are temperature-dependent, so the electrical impedance of PWAS will change with varying temperature. Consequently, the changes in environmental or service temperatures could be detected in ISHM method as a defect. In this article, in order to consider the temperature dependency of PWAS material properties, a temperature-dependent model is developed for a PWAS bonded to an Euler Bernoulli cantilever beam. An aluminum (alloy 2024) beam was examined experimentally by ISHM method in order to validate the proposed model. The comparison of theoretical and experimental results demonstrates a good improvement in ISHM modeling where temperature variation is present.
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45

Sharma, Ambuj, Sandeep Kumar, and Amit Tyagi. "Noise filtering techniques for Lamb waves in structural health monitoring." Multidiscipline Modeling in Materials and Structures 14, no. 4 (December 3, 2018): 676–94. http://dx.doi.org/10.1108/mmms-08-2017-0089.

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Purpose The real challenges in online crack detection testing based on guided waves are random noise as well as narrow-band coherent noise; and to achieve efficient structural health assessment methodology, magnificent extraction of noise and analysis of the signals are essential. The purpose of this paper is to provide optimal noise filtering technique for Lamb waves in the diagnosis of structural singularities. Design/methodology/approach Filtration of time-frequency information of guided elastic waves through the noisy signal is investigated in the present analysis using matched filtering technique which “sniffs” the signal buried in noise and most favorable mother wavelet based denoising methods. The optimal wavelet function is selected using Shannon’s entropy criterion and verified by the analysis of root mean square error of the filtered signal. Findings Wavelet matched filter method, a newly developed filtering technique in this work and which is a combination of the wavelet transform and matched filtering method, significantly improves the accuracy of the filtered signal and identifies relatively small damage, especially in enormously noisy data. A comparative study is also performed using the statistical tool to know acceptability and practicability of filtered signals for guided wave application. Practical implications The proposed filtering techniques can be utilized in online monitoring of civil and mechanical structures. The algorithm of the method is easy to implement and found to be successful in accurately detecting damage. Originality/value Although many techniques have been developed over the past several years to suppress random noise in Lamb wave signal but filtration of interferences of wave modes and boundary reflection is not in a much matured stage and thus needs further investigation. The present study contains detailed information about various noise filtering methods, newly developed filtration technique and their efficacy in handling the above mentioned issues.
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Buff, H., A. Friedmann, M. Koch, T. Bartel, and M. Kauba. "Design of a Random Decrement Method Based Structural Health Monitoring System." Shock and Vibration 19, no. 5 (2012): 787–94. http://dx.doi.org/10.1155/2012/718985.

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Structural Health Monitoring (SHM) has reached a high importance in numerous fields of civil and mechanical engineering. Promising damage detection approaches like the Damage Index Method, Gapped Smoothing Technique and Modal Strain Energy Method require the structure's mode shapes [1].Long term modal data acquisition on real life structures requires a computational efficient system based on a measuring method that can easily be installed. Systems using the Random Decrement Method (RDM) are composed of a decentralized network of smart acceleration sensors applied for both, triggering and pure measuring. They allow the reduction of cabling effort and computational costs to a minimum.In order to design a RDM measuring network efficiently, an approved procedure for defining hardware as well as measuring settings is required. In addition, optimal sensor positions have to be defined. However, today those decisions are mostly based on expert's knowledge. In this paper a systematic and analytical procedure for defining the hardware requirements and measuring settings as well as optimal sensor positions is presented. The proposed routine uses the outcome of an Experimental Modal Analysis (EMA).Due to different requirements for triggering and non-triggering sensors in the RDM network a combination of two approaches for sensor placement has to be used in order to find the best distribution of measurement points over the structure. A controllability based technique is used for placing triggering sensors, whereas the Effective Independence (EI) is utilized for the placement of non-triggering sensors.The combination of these two techniques selects the best set of measuring points for a given number of sensors out of all possible sensor positions.Damage detection itself is not considered within the scope of this paper.
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Bolandi, Hamed, Nizar Lajnef, Pengcheng Jiao, Kaveh Barri, Hassene Hasni, and Amir H. Alavi. "A Novel Data Reduction Approach for Structural Health Monitoring Systems." Sensors 19, no. 22 (November 6, 2019): 4823. http://dx.doi.org/10.3390/s19224823.

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The massive amount of data generated by structural health monitoring (SHM) systems usually affects the system’s capacity for data transmission and analysis. This paper proposes a novel concept based on the probability theory for data reduction in SHM systems. The beauty salient feature of the proposed method is that it alleviates the burden of collecting and analysis of the entire strain data via a relative damage approach. In this methodology, the rate of variation of strain distributions is related to the rate of damage. In order to verify the accuracy of the approach, experimental and numerical studies were conducted on a thin steel plate subjected to cyclic in-plane tension loading. Circular holes with various sizes were made on the plate to define damage states. Rather than measuring the entire strain response, the cumulative durations of strain events at different predefined strain levels were obtained for each damage scenario. Then, the distribution of the calculated cumulative times was used to detect the damage progression. The results show that the presented technique can efficiently detect the damage progression. The damage detection accuracy can be improved by increasing the predefined strain levels. The proposed concept can lead to over 2500% reduction in data storage requirement, which can be particularly important for data generation and data handling in on-line SHM systems.
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Maleki Tabrizi, Mohamad Hossein. "Repair and Health Monitoring for Historical Structures." Advanced Materials Research 133-134 (October 2010): 205–9. http://dx.doi.org/10.4028/www.scientific.net/amr.133-134.205.

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Historical structures, like to masonry towers and another structures are among the structures subjected to the higher risk, due to their age, elevation and low base area on height ratio. In this paper introduce a technique of monitoring the structural integrity of historical buildings by a noncontact and non-destructive analysis is presented and discussed. The damage and destroy of a structure and building, in terms of cracks and overall structural degradation, is detected throughout the measurement of its dynamic characteristics by various ways, for example by a laser Doppler vibrometer(LDV). These techniques can show the frequency spectrum of the structure with high accuracy and reliability and reaction of structure against this technique. Also we will introduce that timber components have no influence on the system behaviour, which is mainly determined by the properties of connections and reactions.
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Zhang, Hai Yan, Jian Bo Yu, and Xian Hua Chen. "An Analysis for Effect Factors of Ultrasonic Guided Wave Tomography in Structural Health Monitoring." Advanced Materials Research 282-283 (July 2011): 574–78. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.574.

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Localized flaws such as corrosions in petroleum pipelines often cause fragility, impairing integrity and shortening service lifetime of the structures. There has been much interest recently in monitoring the integrity of the pipe structures. Ultrasonic guided waves provide a highly efficient technique for rapid pipe inspection because they can be made to propagate significant distances in pitch-catch configurations. Crosshole tomographic geometry is formed in such pitch-catch configurations when transmits and receivers are respectively laid along two parallel circumferential belts around the pipe. Considering the pipe as an unwrapped plate, we investigate the adapation of the tomographic reconstruction in seismology to the guided wave inspection of a pipe. Various effects such as transducer arrangement, mesh precision, sampling interval and iterative algorithm on tomographic reconstruction are analyzed. The results provide a theoretical basis for quantitative detection of pipeline flaw using guided wave tomography.
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Scuro, Carmelo, Domenico Luca Carnì, Francesco Lamonaca, Renato Sante Olivito, and Gabriele Milani. "Preliminary study of an ancient earthquake-proof construction technique monitoring by an innovative structural health monitoring system." ACTA IMEKO 10, no. 1 (March 31, 2021): 47. http://dx.doi.org/10.21014/acta_imeko.v10i1.819.

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<p class="Abstract">The historical and cultural heritage analysis of the Italian territory is of primary importance, because this territory is one of the richest in the world and can enrich our knowledge in different field. In fact, in the field of structural engineering, a new discovery was made in Calabria, in the south of Italy. By investigating various architectural treatises related to earthquake-proof constructions, new knowledge was ganed by analyzing buildings made with fictile tubules bricks. One of them is an unprecedented anti-seismic construction widespread in southern Calabria and patented by Pasquale Frezza.</p><p class="Abstract">In order to avoid the collapses of these structures, in this work, an innovative method to monitor and obtain the mechanical properties of these structures in real time, minimizing measurement uncertainty is proposed.</p>
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