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Journal articles on the topic 'Model Updating, Structural Health Monitoring'

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Author: Grafiati
Published: 14 March 2023

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1

Haidarpour, Amirabbas, and Kong Fah Tee. "Finite Element Model Updating for Structural Health Monitoring." Structural Durability & Health Monitoring 14, no. 1 (2020): 1–17. http://dx.doi.org/10.32604/sdhm.2020.08792.

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2

Rocchetta, Roberto, Matteo Broggi, Quentin Huchet, and Edoardo Patelli. "On-line Bayesian model updating for structural health monitoring." Mechanical Systems and Signal Processing 103 (March 2018): 174–95. http://dx.doi.org/10.1016/j.ymssp.2017.10.015.

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3

Dan, Danhui, Tong Yang, and Jiongxin Gong. "Intelligent Platform for Model Updating in a Structural Health Monitoring System." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/628619.

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The main aim of this study is to develop an automated smart software platform to improve the time-consuming and laborious process of model updating. We investigate the key techniques of model updating based on intelligent optimization algorithms, that is, accuracy judgment methods for basic finite element model, parameter choice theory based on sensitivity analysis, commonly used objective functions and their construction methods, particle swarm optimization, and other intelligent optimization algorithms. An intelligent model updating prototype software framework is developed using the commercial software systems ANSYS and MATLAB. A parameterized finite element modeling technique is proposed to suit different bridge types and different model updating requirements. An objective function library is built to fit different updating targets. Finally, two case studies are conducted to verify the feasibility of the techniques used by the proposed software platform.
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4

Schommer, Sebastian, Viet Ha Nguyen, Stefan Maas, and Arno Zürbes. "Model updating for structural health monitoring using static and dynamic measurements." Procedia Engineering 199 (2017): 2146–53. http://dx.doi.org/10.1016/j.proeng.2017.09.156.

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5

Dey, Palash, V. Akhil, and A. I. Laskar. "Application of Smartphone and Model Updating Technique in Structural Health Monitoring." Arabian Journal for Science and Engineering 44, no. 5 (September 28, 2018): 4819–28. http://dx.doi.org/10.1007/s13369-018-3565-8.

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6

Ching, Jianye, Matthew Muto, and James L. Beck. "Structural Model Updating and Health Monitoring with Incomplete Modal Data Using Gibbs Sampler." Computer-Aided Civil and Infrastructure Engineering 21, no. 4 (May 2006): 242–57. http://dx.doi.org/10.1111/j.1467-8667.2006.00432.x.

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7

Betti, Michele, Salvatore Giacomo Morano, Gianni Bartoli, Giacomo Zini, and Paolo Spinelli. "Structural health monitoring of a masonry arch bridge: modal identification and model updating." International Journal of Masonry Research and Innovation 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijmri.2022.10052514.

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8

Yang, J., H. F. Lam, and J. Hu. "Ambient Vibration Test, Modal Identification and Structural Model Updating Following Bayesian Framework." International Journal of Structural Stability and Dynamics 15, no. 07 (August 31, 2015): 1540024. http://dx.doi.org/10.1142/s0219455415400246.

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Structural health monitoring (SHM) of civil engineering structures based on vibration data includes three main components: ambient vibration test, modal identification and model updating. This paper discussed these three components in detail and proposes a general framework of SHM for practical application. First, a fast Bayesian modal identification method based on Fast Fourier Transform (FFT) is introduced for efficiently extracting modal parameters together with the corresponding uncertainties from ambient vibration data. A recently developed Bayesian model updating method using Markov chain Monte Carlo simulation (MCMCS) is then discussed. To illustrate the performance of the proposed modal identification and model updating methods, a scale-down transmission tower is investigated. Ambient vibration test is conducted on the target structure to obtain modal parameters. By using the measured modal parameters, model updating is carried out. The MCMC-based Bayesian model updating method can efficiently evaluate the posterior marginal PDFs of the uncertain parameters without calculating high-dimension numerical integration, which provides posterior uncertainties for the target systems.
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9

Mordini, Andrea, Konstantin Savov, and Helmut Wenzel. "The Finite Element Model Updating: A Powerful Tool for Structural Health Monitoring." Structural Engineering International 17, no. 4 (November 2007): 352–58. http://dx.doi.org/10.2749/101686607782359010.

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10

Ching, Jianye, and James L. Beck. "New Bayesian Model Updating Algorithm Applied to a Structural Health Monitoring Benchmark." Structural Health Monitoring: An International Journal 3, no. 4 (December 2004): 313–32. http://dx.doi.org/10.1177/1475921704047499.

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11

Dai, Li, Mi-Da Cui, and Xiao-Xiang Cheng. "Structural-Health-Monitoring-Oriented Finite Element Model for a Specially Shaped Steel Arch Bridge and Its Application." Mathematical and Computational Applications 28, no. 2 (February 28, 2023): 33. http://dx.doi.org/10.3390/mca28020033.

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To rigorously evaluate the health of a steel bridge subjected to vehicle-induced fatigue, both a detailed numerical model and effective fatigue analysis methods are needed. In this paper, the process for establishing the structural health monitoring (SHM)-oriented finite element (FE) model and assessing the vehicle-induced fatigue damage is presented for a large, specially shaped steel arch bridge. First, the bridge is meticulously modeled using multiple FEs to facilitate the exploration of the local structural behavior. Second, manual tuning and model updating are conducted according to the modal parameters measured at the bridge’s location. Since the numerical model comprises a large number of FEs, two surrogate-model-based methods are employed to update the model. Third, the established models are validated by using them to predict the structure’s mode shapes and the actual structural behavior for the case in which the whole bridge is subjected to static vehicle loads. Fourth, using the numerical model, a new fatigue analysis method based on the high-circle fatigue damage accumulation theory is employed to further analyze the vehicle-induced fatigue damage to the bridge. The results indicate that manual tuning and model updating are indispensable for SHM-oriented FE models with erroneous configurations, and one surrogate-model-based model updating method is effective. In addition, it is shown that the fatigue analysis method based on the high-circle fatigue damage accumulation theory is applicable to real-world engineering cases.
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12

Nguyen, Andy, KA Tharindu Lakshitha Kodikara, Tommy HT Chan, and David P. Thambiratnam. "Deterioration assessment of buildings using an improved hybrid model updating approach and long-term health monitoring data." Structural Health Monitoring 18, no. 1 (September 19, 2018): 5–19. http://dx.doi.org/10.1177/1475921718799984.

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In recent years, it has become increasingly important to develop methodologies for reliable deterioration assessment of civil structures over their life cycle to facilitate maintenance and/or rehabilitation planning processes. Several approaches have been established to address this issue mainly using Bayesian probabilistic model updating techniques with some capability to incorporate uncertainties in the updating process. However, Bayesian model updating techniques are often found to be complex and computationally inefficient as opposed to their deterministic counterparts such as conventional or hybrid techniques of sensitivity-based model updating. Nevertheless, the deterministic model updating techniques have not been well developed for sophisticated assessment applications such as deterioration evaluation. To address these issues, this article presents a novel methodology for deterioration assessment of building structures under serviceability loading conditions, based upon an improved hybrid model updating approach incorporating the use of long-term monitoring data. This is first realized by a simple but effective scheme to simulate the deterioration mechanism in serviceability loading conditions before enhanced with innovative solutions to classify structural elements as well as to handle measurement and updating uncertainties in a meaningful way. The effectiveness of the established methodology is illustrated through a benchmark 10-story reinforced concrete building which is equipped with a long-term structural health monitoring system.
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13

FEI, QING GUO, YOU LIN XU, CHI LUN NG, K. Y. WONG, W. Y. CHAN, and K. L. MAN. "STRUCTURAL HEALTH MONITORING ORIENTED FINITE ELEMENT MODEL OF TSING MA BRIDGE TOWER." International Journal of Structural Stability and Dynamics 07, no. 04 (December 2007): 647–68. http://dx.doi.org/10.1142/s0219455407002502.

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The modeling, updating and validation of a structural health monitoring oriented finite element model (FEM) of the Tsing Ma suspension bridge towers are presented in this paper. The portal-type bridge tower is composed of two hollow reinforced concrete legs and four deep pre-stressed cross-beams with a steel truss cast in the concrete of each cross-beam to form a narrow corridor for access between two legs. Except that steel trusses are modeled by beam elements, all structural components are modeled by solid elements to facilitate local damage detection, in particular at member joints. The established tower model is then updated using sensitivity-based model updating method taking the natural frequencies identified from field measurement data as reference. Furthermore, a two-level validation criterion is proposed and implemented to examine the replication performance of the updated finite element model of the bridge tower in terms of (1) natural frequencies in higher modes of vibration and (2) dynamic characteristics of the tower-cable system. The validation results show that a good replication of dynamic characteristics is achieved by the updated tower model when compared to the field measurement results. Finally, stress distribution and concentration of the bridge tower are investigated through nonlinear static analysis of the tower-cable system.
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14

He, Xu-hui, Zhi-wu Yu, and Zheng-qing Chen. "Finite element model updating of existing steel bridge based on structural health monitoring." Journal of Central South University of Technology 15, no. 3 (June 2008): 399–403. http://dx.doi.org/10.1007/s11771-008-0075-y.

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15

Yang, Hao, Xiangyang Xu, and Ingo Neumann. "Laser Scanning-Based Updating of a Finite-Element Model for Structural Health Monitoring." IEEE Sensors Journal 16, no. 7 (April 2016): 2100–2104. http://dx.doi.org/10.1109/jsen.2015.2508965.

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16

Wang, De Jun, and Yang Liu. "A Meta-Modeling Procedure for Updating the Finite Element Model of an Arch Bridge Model." Key Engineering Materials 540 (January 2013): 79–86. http://dx.doi.org/10.4028/www.scientific.net/kem.540.79.

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Finite element (FE) model updating of structures using vibration test data has received considerable attentions in recent years due to its crucial role in fields ranging from establishing a reality-consistent structural model for dynamic analysis and control, to providing baseline model for damage identification in structural health monitoring. Model updating is to correct the analytical finite element model using test data to produce a refined one that better predict the dynamic behavior of structure. However, for real complex structures, conventional updating methods is difficult to be utilized to update the FE model of structures due to the heavy computational burden for the dynamic analysis. Meta-model is an effective surrogate model for dynamic analysis of large-scale structures. An updating method based on the combination between meta-model and component mode synthesis (CMS) is proposed to improve the efficiency of model updating of large-scale structures. The effectiveness of the proposed method is then validated by updating a scaled suspender arch bridge model using the simulated data.
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17

Lam, Heung Fai, Jun Hu, and Mujib Olamide Adeagbo. "Bayesian model updating of a 20-story office building utilizing operational modal analysis results." Advances in Structural Engineering 22, no. 16 (January 23, 2019): 3385–94. http://dx.doi.org/10.1177/1369433218825043.

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Most existing buildings are not equipped with long-term monitoring system. For the structural model updating and damage detection of this type of structures, ambient vibration test is popular as artificial excitation is not required. This article presents in detail the full-scale ambient vibration test, operational modal analysis, and model updating of a tall building. To capture the dynamic properties of the target 20-story building with limited number of sensors, a 15-setup ambient vibration test was designed to cover at least three measurement points (each consists of a vertical and two orthogonal horizontal measured degrees of freedom) for each selected floor. The modal parameters of each setup were extracted from the measured acceleration signals using a frequency domain decomposition method and were combined to form the global mode shape through the least-squares method. Due to the regularity of the building, a simple class of shear building models was employed to capture the dynamic characteristics of the building under lateral vibration. The identified modal parameters of the building were employed for the model updating of the shear building model to identify the distribution of inter-story stiffness. Since the “amount” of the measured information is small when compared to the “amount” of required information for identifying the uncertain parameters, the model updating problem is unidentifiable. To handle this problem, the Markov chain Monte Carlo–based Bayesian model updating method is employed in this study. The identified modal parameters revealed interesting features about the dynamic properties of the building. The well-matched results between model-predicted and identified modal parameters show the validity of the shear building model in this case study. This study provides valuable experience in the area of structural model updating and structural health monitoring.
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18

He, Leqia, Edwin Reynders, Jaime H. García-Palacios, Giuseppe Carlo Marano, Bruno Briseghella, and Guido De Roeck. "Wireless-Based Identification and Model Updating of a Skewed Highway Bridge for Structural Health Monitoring." Applied Sciences 10, no. 7 (March 29, 2020): 2347. http://dx.doi.org/10.3390/app10072347.

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Vibration-based monitoring was performed on a short-span skewed highway bridge on the basis of wireless measurements. By means of operational modal analysis, highly accurate modal results (frequencies and mode shapes) were extracted by using a self-developed wireless acquisition system, for which the performance was verified in the field. In order to reproduce the experimental modal characteristics, a refined finite element model was manually tuned to reduce the idealization errors and then updated with the sensitivity method to reduce the parametric errors. It was found that to build a reliable Finite element (FE) model for application in structural health monitoring, the effects of superelevation and boundary conditions of a skewed bridge should be taken into account carefully.
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19

Cui, Yan, Wei Lu, and Jun Teng. "Updating of structural multi-scale monitoring model based on multi-objective optimisation." Advances in Structural Engineering 22, no. 5 (October 12, 2018): 1073–88. http://dx.doi.org/10.1177/1369433218805235.

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Structural safety assessments are implemented based on measured data, but the limited number of sensors restricts the comprehensive acquisition of response information in large complex structures. A concurrent multi-scale model utilises global and local simulation characteristics to expand the insufficient measured data. Thus, good global and local simulation capability is necessary for structural health monitoring-oriented multi-scale model, and the updating of this monitoring model needs to consider the multi-type responses that are obtained from different structural scales. However, the existing methods usually integrate multi-type responses into a single-objective function, which cannot ensure the acquisition of the optimal parameters. Moreover, in common parameter screening method, the perturbation and threshold are set artificially, which causes a strong subjectivity, and the common polynomial response surface fits poorly for highly non-linear problem. Therefore, an updating method of the structural multi-scale monitoring model based on multi-objective optimisation is proposed. For the proposed method, a variance analysis based on the orthogonal experimental design is used to screen the unique significant influence parameters. The Kriging spatial interpolation technique is used to establish the approximate surrogate model between the response and its corresponding influence parameters. Simultaneously, the responses obtained from the global and local structural scales are used to define the sub-objectives of the multi-objective function vector in order to avoid the introduction of weight coefficients. Then, the multi-objective optimisation algorithm NSGA-II is used to obtain the optimal parameter values and realise the comprehensive updating of the initial multi-scale monitoring model. Finally, based on the health monitoring system of the large shell structure of the Zhuhai Opera House, the initial multi-scale monitoring model is updated using the proposed method. The structural dynamic characteristics and local stress obtained from the initial model, updated model and the real structure are compared to validate the effectiveness of the proposed method.
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20

Teșu, Lăzărică, Gabriela Maria Atanasiu, and Cristian Claudiu Comisu. "Parameter Estimation of FE Model of a Bridge Using Identification System Assisted by Paris Software." Advanced Engineering Forum 21 (March 2017): 122–28. http://dx.doi.org/10.4028/www.scientific.net/aef.21.122.

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Recent studies in the field of Structural Health Monitoring SHM using nondestructive test NDT have shown promising applications for parameter estimation methods based on finite element FE models of bridge structures. Using measurements data, carried out during the experiment on real bridge structures, in situ, one can estimate the structural parameters of the bridge. These parameters can be used further in the process of model updating, model verification, structural evaluation and damage assessment. This paper aims to determine the structural parameters using the PARIS approach from a simulated FE model of a real bridge structure subjected to a typical bridge damage scenario. The MATLAB computer based program, entitled PARIS PARmeter Identification System used for the study case illustrated in this paper is applied for the Finite Element FE model updating of a reinforced concrete bridge, located in Iași municipality. PARIS software takes advantage of the Application Programming Interface API of a standalone structural computing software allowing to perform the necessary computational steps for the model updating process. In the same time the application of this software, recently developed by Prof. Masoud Sanayei permits the use of static and modal measurements, as input data, for the estimation of stiffness and mass parameters at the element level of the bridge. The obtained results of the updated model are useful in the process of further validation of a simulated damage test data. Finally, the results based on FE model parameter estimation can be useful for the structural health monitoring and condition assessment process of the bridge structure.
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21

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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22

Kamariotis, Antonios, Eleni Chatzi, and Daniel Straub. "Value of information from vibration-based structural health monitoring extracted via Bayesian model updating." Mechanical Systems and Signal Processing 166 (March 2022): 108465. http://dx.doi.org/10.1016/j.ymssp.2021.108465.

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23

Sharry, Thomas, Hong Guan, Andy Nguyen, Erwin Oh, and Nam Hoang. "Latest Advances in Finite Element Modelling and Model Updating of Cable-Stayed Bridges." Infrastructures 7, no. 1 (January 5, 2022): 8. http://dx.doi.org/10.3390/infrastructures7010008.

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As important links in the transport infrastructure system, cable-stayed bridges are among the most popular candidates for implementing structural health monitoring (SHM) technology. The primary aim of SHM for these bridges is to ensure their structural integrity and satisfactory performance by monitoring their behaviour over time. Finite element (FE) model updating is a well-recognised approach for SHM purposes, as an accurate model serves as a baseline reference for damage detection and long-term monitoring efforts. One of the many challenges is the development of the initial FE model that can accurately reflect the dynamic characteristics and the overall behaviour of a bridge. Given the size, slenderness, use of long cables, and high levels of structural redundancy, precise initial models of long-span cable-stayed bridges are desirable to better facilitate the model updating process and to improve the accuracy of the final updated model. To date, very few studies offer in-depth discussions on the modelling approaches for cable-stayed bridges and the methods used for model updating. As such, this article presents the latest advances in finite element modelling and model updating methods that have been widely adopted for cable-stayed bridges, through a critical literature review of existing research work. An overview of current SHM research is presented first, followed by a comprehensive review of finite element modelling of cable-stayed bridges, including modelling approaches of the deck girder and cables. A general overview of model updating methods is then given before reviewing the model updating applications to cable-stayed bridges. Finally, an evaluation of all available methods and assessment for future research outlook are presented to summarise the research achievements and current limitations in this field.
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24

Velazquez, Antonio, and R. Andrew Swartz. "Operational model updating of low-order horizontal axis wind turbine models for structural health monitoring applications." Journal of Intelligent Material Systems and Structures 26, no. 13 (December 19, 2014): 1739–52. http://dx.doi.org/10.1177/1045389x14563864.

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25

de Stefano, Alessandro. "Structural Identification and Health Monitoring on the Historical Architectural Heritage." Key Engineering Materials 347 (September 2007): 37–54. http://dx.doi.org/10.4028/www.scientific.net/kem.347.37.

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Architectural heritage is a resource and a fundamental part of the cultural European background; it also causes concern, due to the huge investment needed to maintain it, or even to repair it after environmental injuries. The Author is general coordinator of an Italian National research project aimed to formulate new guidelines for structural health monitoring and survey of historical and monumental structures. The Project programme traces a general strategy to build maintenance related monitoring procedures, to face the problems of data uncertainties, to propose robust approaches. This paper shows the general path traced for that project, just now at its start-up phase, but the main attention is addressed to the robust approaches to dynamic testing and model updating.
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26

Ettefagh, Mir M., Hossein Akbari, Keivan Asadi, and Farshid Abbasi. "New structural damage-identification method using modal updating and model reduction." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 229, no. 6 (July 8, 2014): 1041–59. http://dx.doi.org/10.1177/0954406214542966.

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Early prediction of damages using vibration signal is essential in avoiding the failure in structures. Among different damage-detection approaches, the finite-element model updating and modal analysis-based methods are of most importance due to their applicability and feasibility. Owing to some restrictions in nodal measurements in experimental cases, finite-element model reduction is an indispensable part of fault-detection methods. Even though model reduction of dynamic systems leads to the less complicated models, an improved convergence rate and acceptable accuracy are highly required for a successful structural health monitoring of the real complex systems. In this paper, the aim is to design a damage-detection algorithm based on a new model updating method, which has a faster rate of convergence and higher accuracy. Then the proposed method is applied on a simulated damaged beam considering different noise levels to see how capable the method is in dealing with noise-corrupted data. Finally, the experimentally extracted data from a cracked beam in a real noisy condition are used to evaluate the efficiency of the proposed method in identifying the damages in a beam-like structure. It is concluded that the identification of the damages by the proposed method is encouraging and robust to the noise compared with the traditional method. Also, the proposed method converges faster and is more accurate in identifying damage than the traditional method.
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27

Xue, Gang, Wei Yu Bai, and Xian Wang. "Finite Element Model Updating of a Prestressed Concrete Continuous Bridge Based on Dynamic Monitoring." Applied Mechanics and Materials 405-408 (September 2013): 1645–50. http://dx.doi.org/10.4028/www.scientific.net/amm.405-408.1645.

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The finite element model modified technique based on optimization principle is benefit for the model updating of the large bridge structures. By the ambient vibration test information provided by health monitoring system of huanghe bridge II in baotou , this article adopted corrected parameter based on structural eigenvalue sensitivity analysis to update the bridge dynamic model.The fundamental vibrational frequency of updated finite element model is more closer to the measured result under ambient excitation, which indicates that the optimization algorithm provided by large-scale general software can carry out the model updating effectively.
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28

Feng, Zhouquan, Wenzan Wang, and Jiren Zhang. "Probabilistic Structural Model Updating with Modal Flexibility Using a Modified Firefly Algorithm." Materials 15, no. 23 (December 3, 2022): 8630. http://dx.doi.org/10.3390/ma15238630.

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Structural model updating is one of the most important steps in structural health monitoring, which can achieve high-precision matching between finite element models and actual engineering structures. In this study, a Bayesian model updating method with modal flexibility was presented, where a modified heuristic optimization algorithm named modified Nelder–Mead firefly algorithm (m-NMFA) was proposed to find the most probable values (MPV) of model parameters for the maximum a posteriori probability (MAP) estimate. The proposed m-NMFA was compared to the original firefly algorithm (FA), the genetic algorithm (GA), and the particle swarm algorithm (PSO) through the numerical illustrative examples of 18 benchmark functions and a twelve-story shear frame model. Then, a six-story shear frame model test was performed to identify the inter-story stiffness of the structure in the original and the damage states, respectively. By comparing the two, the position and extent of damage were accurately found and quantified in a probabilistic manner. In terms of optimization, the proposed m-NMFA was powerful to find the MPVs much faster and more accurately. In the incomplete measurement case, only the m-NMFA achieved target damage identification results. The proposed Bayesian model updating method has the advantages of high precision, fast convergence, and strong robustness in MPV finding and the ability of parameter uncertainty quantification.
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29

Erdogan, Yildirim Serhat, Mustafa Gul, F. Necati Catbas, and Pelin Gundes Bakir. "Investigation of Uncertainty Changes in Model Outputs for Finite-Element Model Updating Using Structural Health Monitoring Data." Journal of Structural Engineering 140, no. 11 (November 2014): 04014078. http://dx.doi.org/10.1061/(asce)st.1943-541x.0001002.

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30

Eidukynas, D., A. Adumitroaie, P. Griškevičius, V. Grigaliunas, and T. Vaitkūnas. "Finite Element Model Updating Approach for Structural Health Monitoring of Lightweight Structures Using Response Surface Optimization." IOP Conference Series: Materials Science and Engineering 1239, no. 1 (June 1, 2022): 012002. http://dx.doi.org/10.1088/1757-899x/1239/1/012002.

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Abstract Mechanical defects in the structure changes it’s vibration response. There is a wide variety of methods that examine changes in measured vibration response to detect, locate, and characterize damage in structural and mechanical systems. One method to evaluate the structural changes and to analyse their causes is the Finite Element Model Updating (FEMU). The objective of this research is to investigate the FEMU procedure for mechanical damage identification and to propose an experimental-computational SHM method for lightweight structures. The structural dynamic response to impact excitation of a structure with and without defects are collected from transient and modal analysis using Ansys FE software. Afterwards, FEMU algorithm using Ansys Surface Response Optimization is investigated for its applicability to damage identification. Obtained results revealed the possibility to use this algorithm with having minimum discrepancy between parameters obtained from experiments and finite element modelling.
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31

Lai, Yutao, Jianye Chen, Qi Hong, Zhekai Li, Haitian Liu, Benhao Lu, Ruihao Ma, et al. "Framework for long-term structural health monitoring by computer vision and vibration-based model updating." Case Studies in Construction Materials 16 (June 2022): e01020. http://dx.doi.org/10.1016/j.cscm.2022.e01020.

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32

Giagopoulos, Dimitrios, Alexandros Arailopoulos, Vasilis Dertimanis, Costas Papadimitriou, Eleni Chatzi, and Konstantinos Grompanopoulos. "Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating." Structural Health Monitoring 18, no. 4 (August 7, 2018): 1189–206. http://dx.doi.org/10.1177/1475921718790188.

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33

Xia, Yong, Yi Qing Ni, Jan Ming Ko, and Hua Bin Chen. "Development of a Structural Health Monitoring Benchmark Problem for High-Rise Slender Structures." Advances in Science and Technology 56 (September 2008): 489–94. http://dx.doi.org/10.4028/www.scientific.net/ast.56.489.

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Under the auspices of the Asian-Pacific Network of Centers for Research in Smart Structures Technology (ANCRiSST) and the International Society for Structural Health Monitoring of Intelligent Infrastructure (ISHMII), a structural health monitoring benchmark problem for highrise slender structures is being developed by taking the instrumented Guangzhou New Television Tower as a test bed. The benchmark problem consists of the following four tasks: (i) output-only modal identification and finite element model updating, (ii) damage detection using simulated data, (iii) optimal sensor placement for structural health monitoring, and (iv) damage detection using field measurement data. This paper will address some key issues related to the development of this first benchmark problem for high-rise structures. More details of the study can be found in the website: http://www.cse.polyu.edu.hk/benchmark/index.htm
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34

Hu, Jun, and Jia-Hua Yang. "Operational Modal Analysis and Bayesian Model Updating of a Coupled Building." International Journal of Structural Stability and Dynamics 19, no. 01 (December 20, 2018): 1940012. http://dx.doi.org/10.1142/s0219455419400121.

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This paper reports the step-by-step procedures of a full-scale ambient vibration test and the corresponding modal identification and Bayesian structural model updating of a coupled building. The building is characterized as a combination of a main part and a complementary part connected together by corridors in between. Compared with the main part, the volume of the complementary part is much smaller. Therefore, the influence on the dynamic properties of the complementary part from its counterpart is expected. To capture the dynamic properties of the coupled building, a 21-setup ambient vibration test was designed to cover all the degrees of freedom (DOFs) of interest. The modal parameters of each setup were identified following the frequency domain decomposition (FDD) method and the partial mode shapes from different setups were assembled following a least-squares method. To determine the stiffness of the linkage between the two parts, the coupled building was simulated with two linked shear buildings and updated utilizing the Markov chain Monte Carlo (MCMC)-based Bayesian model updating method. The identified modal parameters revealed interesting features about the coupled effects between the main part and complementary part and were discussed in detail. The good match between the model-predicted and identified modal parameters verified the validity of proposed shear building model. This study provides valuable experience in the area of structural model updating and structural health monitoring.
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Geara, Christelle, Rafic Faddoul, Alaa Chateauneuf, and Wassim Raphaël. "A predator-prey optimization for structural health monitoring problems." MATEC Web of Conferences 281 (2019): 01004. http://dx.doi.org/10.1051/matecconf/201928101004.

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Monitoring a structure using permanent sensors has been one of the most interesting topics, especially with the increase of the number of aging structures. Such a technique requires the implementation of sensors on a structure to predict the condition states of the structural elements. However, due to the costs of sensors, one must judiciously install few sensors at some defined locations in order to maximize the probability of detecting potential damages. In this paper, we propose a methodology based on a genetic algorithm of type predator-prey with a Bayesian updating of the structural parameters, to optimize the number and location of the sensors to be placed. This methodology takes into consideration all uncertainties related to the degradation of the elements, the mechanical model and the accuracy of sensors. Starting with two initial populations representing the damages (prey) and the sensors (predator), the genetic algorithm evolves both populations in order to converge towards the optimal configuration of sensors, in terms of number and location. The proposed methodology is illustrated by a two-story concrete frame structure.
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Heo, Gwanghee, and Joonryong Jeon. "Structural Identification for Structural Health Monitoring of Long-span Bridge - Focusing on Optimal Sensing and FE Model Updating -." Transactions of the Korean Society for Noise and Vibration Engineering 25, no. 12 (December 20, 2015): 830–42. http://dx.doi.org/10.5050/ksnve.2015.25.12.830.

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Chen, Xing Hua, and Piotr Omenzetter. "A Framework for Reliability Assessment of an In-Service Bridge Using Structural Health Monitoring Data." Key Engineering Materials 558 (June 2013): 39–51. http://dx.doi.org/10.4028/www.scientific.net/kem.558.39.

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Because of the critical role that bridges play in land transport networks and broader economy, the assessment of existing bridges is gradually becoming a global concern. Structural health monitoring (SHM) systems have been installed on many bridges to provide data for the evaluation of bridge performance and safety. The challenge for bridge engineers is now to make use of the data and convert them into usable information and knowledge. Integrating SHM data with reliability analysis procedures offers a useful and practical methodology for bridge assessment since reliability is an important performance index and reliability-based procedures have the capability of accommodating uncertainties in structural models, responses, loads and monitoring data. In this paper, an approach for integrating SHM data in a reliability assessment framework is proposed. The reliability of the bridge is quantified by incorporating SHM information in the resistance, load and structural models. Advanced modeling tools and techniques, such as finite element analysis, finite model updating and Bayesian updating, are used for the reliability computations. Data from the SHM system installed on the Newmarket Viaduct, a newly constructed, 12-span, post-tensioned box girder bridge erected by the balanced cantilever method in Auckland, New Zealand, are also presented in this paper and used to explain the proposed framework.
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Eiras, Jesús N., Cédric Payan, Sandrine Rakotonarivo, and Vincent Garnier. "Experimental modal analysis and finite element model updating for structural health monitoring of reinforced concrete radioactive waste packages." Construction and Building Materials 180 (August 2018): 531–43. http://dx.doi.org/10.1016/j.conbuildmat.2018.06.004.

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Malekghaini, Niloofar, Farid Ghahari, Hamed Ebrahimian, Matthew Bowers, Eric Ahlberg, and Ertugrul Taciroglu. "A Two-Step FE Model Updating Approach for System and Damage Identification of Prestressed Bridge Girders." Buildings 13, no. 2 (February 2, 2023): 420. http://dx.doi.org/10.3390/buildings13020420.

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This study presents a two-step FE model updating approach for health monitoring and damage identification of prestressed concrete girder bridges. To reduce the effects of modeling error in the model updating process, in the first step, modal-based model updating is used to estimate linear model parameters mainly related to the stiffness of boundary conditions and material properties. In the second step, a time-domain model updating is carried out using acceleration data to refine parameters accounting for the nonlinear response behavior of the bridge. In this step, boundary conditions are fixed at their final estimates using modal-based model updating. To prevent the convergence of updating algorithm to local solutions, the initial estimates for nonlinear material properties are selected based on the first-step model updating results. To validate the applicability of the two-step FE model updating approach, a series of forced-vibration experiments are designed and carried out on a pair of full-scale decommissioned and deteriorated prestressed bridge I-girders. In the first step, parameters related to boundary conditions, including stiffness of supports and coupling beams, as well as material properties, including initial stiffness of concrete material, are estimated. In the second step, concrete compressive strength and damping properties are updated. The final estimates of the concrete compressive strength are used to infer the extent of damage in the girders. The obtained results agree with the literature regarding the extent of reduction in concrete compressive strength in deteriorated concrete structures.
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Cheng, Xiao-Xiang, Yang Peng, Zargham Ahmad Khan, and Jun Dong. "Beam-end stiffness identification for a structural health monitoring-oriented finite-element transmission tower model using effective optimization techniques." Advances in Structural Engineering 22, no. 2 (August 3, 2018): 364–83. http://dx.doi.org/10.1177/1369433218788584.

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As an important issue for establishing structural health monitoring–oriented finite element models for large steel pylons, identification of beam-end stiffness draws the attention of the engineering circle. Since the methods adopted by other researchers for parameter identification are impracticable, a beam-end stiffness identification method which combines in situ measurements for the structure’s global dynamic properties with effective multi-variable optimization methods is utilized to improve the accuracy of established finite element models. A 131-m-high large transmission tower is employed as a case study to validate the method. In situ measurements for the tower’s global dynamic characteristics are performed, and identifications of Young’s modulus for 20 semi-rigid connections distributed along each of the tower’s four main chords are undertaken utilizing three multi-variable optimization methods, that is, the first-order method, the subproblem approximation method, and the response surface method. Static numerical simulations on two detailed connection models prove that multiple uncertain parameters can be correctly and simultaneously identified when appropriate optimization techniques are chosen. Finally, the influence of beam-end stiffness identification on the structural health monitoring–oriented structural safety assessment is revealed by calculating the wind-induced dynamic structural responses for the single tower and the transmission tower-line system, which indicates that identification of the correct beam-end stiffness and updating the structural health monitoring–oriented finite element model are indispensable procedures for reliable structural health monitoring.
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Ebrahimian, Hamed, Abdelrahman Taha, Farid Ghahari, Domniki Asimaki, and Ertugrul Taciroglu. "Estimation of Soil–Structure Model Parameters for the Millikan Library Building Using a Sequential Bayesian Finite Element Model Updating Technique." Buildings 13, no. 1 (December 22, 2022): 28. http://dx.doi.org/10.3390/buildings13010028.

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We present a finite element model updating technique for soil–structure system identification of the Millikan Library building using the seismic data recorded during the 2002 Yorba Linda earthquake. A detailed finite element (FE) model of the Millikan Library building is developed in OpenSees and updated using a sequential Bayesian estimation approach for joint parameter and input identification. A two-step system identification approach is devised. First, the fixed-base structural model is updated to estimate the structural model parameters (including effective elastic modulus of structural components, distributed floor mass, and Rayleigh damping parameters) and some uncertain components of the foundation-level motion. Then, the identified structural model is used for soil–structure model updating wherein the Rayleigh damping parameters, the stiffness and viscosity of the soil subsystem (modeled using a substructure approach), and the foundation input motions (FIMs) are estimated. The identified model parameters are compared with state-of-practice recommendations. While a specific application is made for the Millikan Library, the present work offers a framework for integrating large-scale FE models with measurement data for model inversion. By utilizing this framework for different civil structures and earthquake records, key structural model parameters can be estimated from the real-world recorded data, which can subsequently be used for assessing and improving, as necessary, state-of-the-art seismic analysis and structural modeling techniques. This paper presents an effort towards using real-world measurements for large-scale FE model updating in the soil and structure, uniform soil time domain for joint parameter and input estimation, and thus paves the way for future applications in system identification, health monitoring, and diagnosis of civil structures.
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Moravej, Hans, Tommy H. T. Chan, Andre Jesus, and Khac-Duy Nguyen. "Computation-Effective Structural Performance Assessment Using Gaussian Process-Based Finite Element Model Updating and Reliability Analysis." International Journal of Structural Stability and Dynamics 20, no. 10 (September 2020): 2042003. http://dx.doi.org/10.1142/s0219455420420031.

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Structural health monitoring data has been widely acknowledged as a significant source for evaluating the performance and health conditions of structures. However, a holistic framework that efficiently incorporates monitored data into structural identification and, in turn, provides a realistic life-cycle performance assessment of structures is yet to be established. There are different sources of uncertainty, such as structural parameters, computer model bias and measurement errors. Neglecting to account for these factors results in unreliable structural identifications, consequent financial losses, and a threat to the safety of structures and human lives. This paper proposes a new framework for structural performance assessment that integrates a comprehensive probabilistic finite element model updating approach, which deals with various structural identification uncertainties and structural reliability analysis. In this framework, Gaussian process surrogate models are replaced with a finite element model and its associate discrepancy function to provide a computationally efficient and all-round uncertainty quantification. Herein, the structural parameters that are most sensitive to measured structural dynamic characteristics are investigated and used to update the numerical model. Sequentially, the updated model is applied to compute the structural capacity with respect to loading demand to evaluate its as-is performance. The proposed framework’s feasibility is investigated and validated on a large lab-scale box girder bridge in two different health states, undamaged and damaged, with the latter state representing changes in structural parameters resulted from overloading actions. The results from the box girder bridge indicate a reduced structural performance evidenced by a significant drop in the structural reliability index and an increased probability of failure in the damaged state. The results also demonstrate that the proposed methodology contributes to more reliable judgment about structural safety, which in turn enables more informed maintenance decisions to be made.
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Rafiq, M. Imran. "Performance Updating of Concrete Structures Using Proactive Health Monitoring: A Systems Approach." ISRN Civil Engineering 2012 (December 10, 2012): 1–10. http://dx.doi.org/10.5402/2012/926412.

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Uncertainties in predictive models for concrete structures performance can influence adversely the timing of management activities. A methodology has been developed that uses data obtained through proactive health monitoring to increase the confidence in predicted performance by reducing the associated uncertainties. Due to temporal and spatial variations associated with climatic changes, exposure conditions, workmanship, and concrete quality, the actual performance could vary at different locations of the member. In this respect, the use of multiple sensors may be beneficial, notwithstanding cost and other constraints. Two distinct cases are identified for which an updating methodology based on data from multiple sensors needs to be developed. In the first case the interest lies in improving the performance prediction for an entire member (or a structure) incorporating spatial and temporal effects. For this purpose, the member is divided into small zones with the assumption that a sensor can be located in each zone. In the second case, the objective is to minimise uncertainties in performance prediction, or to increase the redundancy of health monitoring systems, at critical locations. The development of updating methodologies for the above-mentioned scenarios is described in this paper. Its implications on the management activities, for example, establishing the timing of principal inspections, are evaluated and discussed.
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Waeytens, Julien, Veronique le Corvec, Philippe Lévèque, Dominique Siegert, and Frederic Bourquin. "Elastodynamics Model Updating for the Monitoring of Reinforced Concrete Beam: Methodology and Numerical Implementation." Applied Mechanics and Materials 513-517 (February 2014): 3401–6. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3401.

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Reinforced concrete beams are widely employed in civil engineering structures. To reduce the maintenance financial cost, structure damages have to be detected early. To this end, one needs robust monitoring techniques. The paper deals with the identification of mechanical parameters, useful for Structural Health Monitoring, in a 2D beam using inverse modeling technique. The optimal control theory is employed. As an example, we aim to identify a reduction of the steel bar cross-section and a decrease of the concrete Young modulus in damaged areas. In our strategy, the beam is instrumented with strain sensors, and a known dynamic load is applied. In the inverse technique, two space discretizations are considered: a fine dicretization (h) to solve the structural dynamic problem and a coarse discretization (H) for the beam parameter identification. To get the beam parameters, we minimize a classical data misfit functional using a gradient-like algorithm. A low-cost computation of the functional gradient is performed using the adjoint equation. The inverse problem is solved in a general way using engineer numerical tools: Python scripts and the free finite element software Code_Aster. First results show that a local reduction of the steel bar cross-section and a local decrease of concrete Young modulus can be detected using this inverse technique.
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Singh, Tarunpreet, Shankar Sehgal, Chander Prakash, and Saurav Dixit. "Real-Time Structural Health Monitoring and Damage Identification Using Frequency Response Functions along with Finite Element Model Updating Technique." Sensors 22, no. 12 (June 16, 2022): 4546. http://dx.doi.org/10.3390/s22124546.

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Throughout service, damage can arise in the structure of buildings; hence, their dynamic testing becomes essential to verify that such buildings possess sufficient strength to withstand disturbances, particularly in the event of an earthquake. Dynamic testing, being uneconomical, requires proof of concept; for this, a model of a structure can be dynamically tested, and the results are used to update its finite element model. This can be used for damage detection in the prototype and aids in predicting its behavior during an earthquake. In this instance, a wireless MEMS accelerometer was used, which can measure the vibration signals emanating from the building and transfer these signals to a remote workstation. The base of the structure is excited using a shaking table to induce an earthquake-like situation. Four natural frequencies have been considered and six different types of damage conditions have been identified in this work. For each damage condition, the experimental responses are measured and the finite element model is updated using the Berman and Nagy method. It is seen that the updated models can predict the dynamic responses of the building accurately. Thus, depending on these responses, the damage condition can be identified by using the updated finite element models.
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46

Zong, Zhouhong, Xiaosong Lin, and Jie Niu. "Finite element model validation of bridge based on structural health monitoring—Part I: Response surface-based finite element model updating." Journal of Traffic and Transportation Engineering (English Edition) 2, no. 4 (August 2015): 258–78. http://dx.doi.org/10.1016/j.jtte.2015.06.001.

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47

Ghiasi, Ramin, Mohammad Noori, Sin-Chi Kuok, Ahmed Silik, Tianyu Wang, Francesc Pozo, and Wael A. Altabey. "Structural Assessment under Uncertain Parameters via the Interval Optimization Method Using the Slime Mold Algorithm." Applied Sciences 12, no. 4 (February 11, 2022): 1876. http://dx.doi.org/10.3390/app12041876.

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Damage detection of civil and mechanical structures based on measured modal parameters using model updating schemes has received increasing attention in recent years. In this study, for uncertainty-oriented damage identification, a non-probabilistic structural damage identification (NSDI) technique based on an optimization algorithm and interval mathematics is proposed. In order to take into account the uncertainty quantification, the elastic modulus is described as unknown-but-bounded interval values and the proposed new scheme determines the upper and lower bounds of the damage index. In this method, the interval bounds can provide supports for structural health diagnosis under uncertain conditions by considering the uncertainties in the variables of optimization algorithm. The model updating scheme is subsequently used to predict the interval-bound of the Elemental Stiffness Parameter (ESP). The slime mold algorithm (SMA) is used as the main algorithm for model updating. In addition, in this study, an enhanced variant of SMA (ESMA) is developed, which removes unchanged variables after a defined number of iterations. The method is implemented on three well-known numerical examples in the domain of structural health monitoring under single damage and multi-damage scenarios with different degrees of uncertainty. The results show that the proposed NSDI methodology has reduced computation time, by at least 30%, in comparison with the probabilistic methods. Furthermore, ESMA has the capability to detect damaged elements with higher certainty and lower computation cost in comparison with the original SMA.
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A. Mordini and H. Wenzel. "Damage detection on beam structures by means of VCUPDATE." Electronic Journal of Structural Engineering 10 (January 1, 2010): 11–21. http://dx.doi.org/10.56748/ejse.10121.

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One of the most promising computational methods in the field of Structural Health Monitoring is the Finite Element Model Updating. The VCUPDATE solution presented in this work is a Scilab code performing the iterative updating algorithm interfaced with a Finite Element code (OpenSees or ANSYS) executing the numerical analysis. The code is applied to the damage detection on beam structures. At first, a simple case numerically generated by OpenSees is investigated. Subsequently, using experimental test data, the code is applied to a real structure by using OpenSees as well as ANSYS.
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Zhao, Rui, Yuhang Wu, and Zehua Feng. "Research for Pedestrian Steel Bridge Design of Neural Network in Structural Model Updating." Shock and Vibration 2022 (May 2, 2022): 1–11. http://dx.doi.org/10.1155/2022/1057422.

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The application of the neural network method in health monitoring and structural system identification has received extensive attention. A reasonable neural network structure is very important for its performance. This paper takes the pedestrian bridge of the Xingfu intersection in Urumqi, China, as the research object and uses MIDAS/Civil to establish a finite element analysis model. Taking the natural vibration frequency obtained from the dynamic test of the actual bridge as the target, two kinds of neural networks are used to predict the structural material parameters. An appropriate bridge model correction method is selected by comparing the prediction results of the BP neural network and the GRNN. The test results show that the pedestrian bridge model based on MIDAS/Civil has a high accuracy, but it still does not meet the actual needs. The modified model based on the BP neural network is close to the actual measured results, and a more accurate finite element analysis model can be established by this method, which makes the modified model closer to the real stress state of the structure.
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Sokol, Milan, Rudolf Aroch, Michal Venglar, Michal Fabry, and Tomáš Živner. "Experience with Structural Damage Identification of an Experimental Bridge Model." Applied Mechanics and Materials 769 (June 2015): 192–99. http://dx.doi.org/10.4028/www.scientific.net/amm.769.192.

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The paper is devoted to structural health monitoring using a non-destructive method based on the method of direct stiffness determination combined with the model updating method. The primary aim of the paper is to determine the change in bending and torsional stiffnesses. In the first part of the work it was necessary to prepare a simple experimental scale model of the bridge which was made from two materials – wood and plaster boards. The same bridge model was created in the finite element commercial codes RFEM5 - Dlubal (a more detailed 3D analysis) and ANSYS (a 2D analysis). The last numerical model consists of beam elements with lumped mass elements. The modal analysis was made and these results were used as a comparative base for measurements. In either case data for the original and the damaged models were acquired. Analysis of the measurement data led to the identification of vertical and torsional mode shapes. Last part of the paper is devoted to identification of the damage by application of the direct stiffness calculation method.
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