Yuzhu Guo, Xudong Chen, Jing Wu, Tao Ji, Yingjie Ning
To investigate the possibility of quantitative monitoring of the fracture process zone (FPZ) at the shotcrete-rock interface, the acoustic emission (AE) and digital image correlation (DIC) are used to monitor the three-point bending test of shotcrete-rock specimens. Firstly, the AE intensity signal characteristics during damage to the shotcrete-rock interface are analyzed. Then, the spatial b-value of AE is used to visually characterize the shotcrete-rock interface damage, and the interface damage characteristics of two specimens, shotcrete-granite and shotcrete-sandstone, are analyzed using this analysis method. The analysis reveals that not only the AE spatial b-value can determine the location of microdamage within the interface but it can also characterize the degree of damage. Finally, a new parameter, Tb-value, is constructed based on the AE spatial b-value to quantitatively characterize the FPZ, and the newly established characterization method is validated with the FPZ monitored by DIC. The results show that the Tb-value not only locates and visually characterizes the location of the FPZ within the specimen but also enables the quantitative determination of the FPZ. This provides a new idea for localizing and quantitatively monitoring cracks and FPZs inside structures using AE techniques.
{"title":"Quantitative Visualization Monitoring of Cracks at Shotcrete-Rock Interface Based on Acoustic Emission","authors":"Yuzhu Guo, Xudong Chen, Jing Wu, Tao Ji, Yingjie Ning","doi":"10.1155/2023/9958905","DOIUrl":"https://doi.org/10.1155/2023/9958905","url":null,"abstract":"To investigate the possibility of quantitative monitoring of the fracture process zone (FPZ) at the shotcrete-rock interface, the acoustic emission (AE) and digital image correlation (DIC) are used to monitor the three-point bending test of shotcrete-rock specimens. Firstly, the AE intensity signal characteristics during damage to the shotcrete-rock interface are analyzed. Then, the spatial b-value of AE is used to visually characterize the shotcrete-rock interface damage, and the interface damage characteristics of two specimens, shotcrete-granite and shotcrete-sandstone, are analyzed using this analysis method. The analysis reveals that not only the AE spatial b-value can determine the location of microdamage within the interface but it can also characterize the degree of damage. Finally, a new parameter, Tb-value, is constructed based on the AE spatial b-value to quantitatively characterize the FPZ, and the newly established characterization method is validated with the FPZ monitored by DIC. The results show that the Tb-value not only locates and visually characterizes the location of the FPZ within the specimen but also enables the quantitative determination of the FPZ. This provides a new idea for localizing and quantitatively monitoring cracks and FPZs inside structures using AE techniques.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82333775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jingkai Wang, L. Huo, Chunguang Liu, Gangbing Song
The acoustic emission (AE) technique has been widely investigated for its ability to locate damage in structures. However, the selection of the arrival point of AE signals and the existence of nonhomologous AE signals can significantly affect the location accuracy of damages. The synchrosqueezed wavelet transform (SWT) was used in our previous research to pick the accurate arrival point, but the existence of the nonhomologous signals was neglected in the picking process. To address this limitation, the synchrosqueezed wavelet transform coherence (SWTC) method is proposed to improve the accuracy by recognizing homologous signals and suppressing the spectral leakage in this paper. Compared with the wavelet transform coherence (WTC) method previously used, the SWTC method using the squeezing wavelet coefficients obtained by the SWT can constitute a more explicit coherence graph of AE signals. This clear coherence graph can help reduce the effect of subjective factors in observing the coherence and improve the recognition accuracy of homologous signals. The effectiveness of the proposed method is experimentally verified on a steel pipe and a concrete beam. The results demonstrate that the SWTC accurately identifies homologous AE signals and effectively improves the localization accuracy across different signal densities, localization distances, and materials.
{"title":"Theoretical and Experimental Study on Homologous Acoustic Emission Signal Recognition Based on Synchrosqueezed Wavelet Transform Coherence","authors":"Jingkai Wang, L. Huo, Chunguang Liu, Gangbing Song","doi":"10.1155/2023/6968338","DOIUrl":"https://doi.org/10.1155/2023/6968338","url":null,"abstract":"The acoustic emission (AE) technique has been widely investigated for its ability to locate damage in structures. However, the selection of the arrival point of AE signals and the existence of nonhomologous AE signals can significantly affect the location accuracy of damages. The synchrosqueezed wavelet transform (SWT) was used in our previous research to pick the accurate arrival point, but the existence of the nonhomologous signals was neglected in the picking process. To address this limitation, the synchrosqueezed wavelet transform coherence (SWTC) method is proposed to improve the accuracy by recognizing homologous signals and suppressing the spectral leakage in this paper. Compared with the wavelet transform coherence (WTC) method previously used, the SWTC method using the squeezing wavelet coefficients obtained by the SWT can constitute a more explicit coherence graph of AE signals. This clear coherence graph can help reduce the effect of subjective factors in observing the coherence and improve the recognition accuracy of homologous signals. The effectiveness of the proposed method is experimentally verified on a steel pipe and a concrete beam. The results demonstrate that the SWTC accurately identifies homologous AE signals and effectively improves the localization accuracy across different signal densities, localization distances, and materials.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"204 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80341927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores a change detection method in modal properties to automate and generalize in-service damage detection for vibration-based structural health monitoring of bridges. The noisy conditions caused by ambient loading pose difficulty for in-service damage detection because the load-induced noise often masks the difference in the modal properties. The proposed method directly converts measured time series into a simplified anomaly indicator robust against load-induced noise. This study adopts a vector autoregressive model to represent the vibration of bridges. Bayesian inference produces a posterior probability distribution function of the model parameters. Principal component analysis extracts a subspace comparable to the modal properties in the model parameters. Bayesian hypothesis testing quantifies anomalies in the extracted subspace. The feasibility of the proposed method is assessed with vibration data from field experiments conducted on an actual steel truss bridge. The field experiment includes damage severing the truss members. The modal frequencies and mode shapes estimated from the principal component analysis correspond well to earlier reported results. The proposed damage detection method successfully indicated all damage considered in the experiment.
{"title":"Bridge Damage Detection Using Ambient Loads by Bayesian Hypothesis Testing for a Parametric Subspace of an Autoregressive Model","authors":"Y. Goi, Chul‐Woo Kim","doi":"10.1155/2023/7986061","DOIUrl":"https://doi.org/10.1155/2023/7986061","url":null,"abstract":"This study explores a change detection method in modal properties to automate and generalize in-service damage detection for vibration-based structural health monitoring of bridges. The noisy conditions caused by ambient loading pose difficulty for in-service damage detection because the load-induced noise often masks the difference in the modal properties. The proposed method directly converts measured time series into a simplified anomaly indicator robust against load-induced noise. This study adopts a vector autoregressive model to represent the vibration of bridges. Bayesian inference produces a posterior probability distribution function of the model parameters. Principal component analysis extracts a subspace comparable to the modal properties in the model parameters. Bayesian hypothesis testing quantifies anomalies in the extracted subspace. The feasibility of the proposed method is assessed with vibration data from field experiments conducted on an actual steel truss bridge. The field experiment includes damage severing the truss members. The modal frequencies and mode shapes estimated from the principal component analysis correspond well to earlier reported results. The proposed damage detection method successfully indicated all damage considered in the experiment.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"229 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74958368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To devise an optimum and robust fuzzy logic controller for MR damper-based structures subjected to earthquake ground motions, the multiobjective reliability-based design optimization (RBDO) using the adaptive Kriging model is performed to determine the parameters of the fuzzy logic controller. The optimization problem is formulated with two objective functions, namely, the minimization of interstory drift and average control force of the concerned structure, and subjected to a probability constraint on structural dynamic responses under the effects of random structural stiffness and stochastic earthquake loadings. To reduce the computational cost of reliability assessment, a global Kriging model is constructed in an augmented space as a surrogate for computational evaluations. Subsequently, the trained metamodel combined with the nondominated sorting genetic algorithm (NSGA-II) is integrated into the framework of RBDO for solving the fuzzy logic control (FLC) optimization problem. The feasibility and effectiveness of the multiobjective RBDO in the FLC design are finally validated by conducting numerical simulations on both linear and nonlinear structures. As demonstrated in the linear case, the fuzzy logic controllers obtained from the multiobjective RBDO show more robustness than those derived from the multiobjective deterministic design optimization (DDO). In the nonlinear case, using the multiobjective DDO to prelocate a coarse safety domain can significantly reduce the number of samples for training the metamodel and facilitate the implementation of the multiobjective RBDO; in addition, the controlled structural performance with a specified fuzzy logic controller can be further improved by considering MR damper distribution optimization.
{"title":"Multiobjective Reliability-Based Design Optimization of the Fuzzy Logic Controller for MR Damper-Based Structures","authors":"Pei Pei, S. Quek, Yongbo Peng","doi":"10.1155/2023/4009397","DOIUrl":"https://doi.org/10.1155/2023/4009397","url":null,"abstract":"To devise an optimum and robust fuzzy logic controller for MR damper-based structures subjected to earthquake ground motions, the multiobjective reliability-based design optimization (RBDO) using the adaptive Kriging model is performed to determine the parameters of the fuzzy logic controller. The optimization problem is formulated with two objective functions, namely, the minimization of interstory drift and average control force of the concerned structure, and subjected to a probability constraint on structural dynamic responses under the effects of random structural stiffness and stochastic earthquake loadings. To reduce the computational cost of reliability assessment, a global Kriging model is constructed in an augmented space as a surrogate for computational evaluations. Subsequently, the trained metamodel combined with the nondominated sorting genetic algorithm (NSGA-II) is integrated into the framework of RBDO for solving the fuzzy logic control (FLC) optimization problem. The feasibility and effectiveness of the multiobjective RBDO in the FLC design are finally validated by conducting numerical simulations on both linear and nonlinear structures. As demonstrated in the linear case, the fuzzy logic controllers obtained from the multiobjective RBDO show more robustness than those derived from the multiobjective deterministic design optimization (DDO). In the nonlinear case, using the multiobjective DDO to prelocate a coarse safety domain can significantly reduce the number of samples for training the metamodel and facilitate the implementation of the multiobjective RBDO; in addition, the controlled structural performance with a specified fuzzy logic controller can be further improved by considering MR damper distribution optimization.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74415785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Takehiko Asai, Shota Tsukamoto, Y. Nemoto, Kenji Yoshimizu, Urara Watanabe, Y. Taniyama
Offshore wind turbines (OWTs) are considered vital to the promotion of the development of renewable energy. Especially, floating OWTs can be deployed over a larger area than bottom-fixed OWTs. The floating OWTs, however, are vulnerable to vibration induced by disturbances and require a backup power supply in the case of power outage. On the one hand, various kinds of inerter-based devices have been proposed especially for vibration suppression of civil structures subjected to earthquake loadings. Recently, combined with electromagnetic devices, the inerter technologies have also been applied in the field of vibration energy harvesting such as point absorber wave energy converters. Thus, this paper proposes a novel floating OWT consisting of two bodies combined with inerter-based power take-off (PTO) devices which accomplishes vibration suppression and wave energy conversion at the same time. To investigate the vibration suppression and energy conversion capabilities of the proposed floating OWT with a variety of inerter-based PTO devices for ocean waves, numerical simulation studies employing WEC-Sim are conducted, and the performance of each system is compared for regular and irregular waves. Results show that the proposed floating OWT with the appropriately designed inerter-based PTO devices for the incident wave period has great potential for both vibration suppression and wave energy conversion in a specific frequency range.
{"title":"Numerical Simulation of a Floating Offshore Wind Turbine Incorporating an Electromagnetic Inerter-Based Device for Vibration Suppression and Wave Energy Conversion","authors":"Takehiko Asai, Shota Tsukamoto, Y. Nemoto, Kenji Yoshimizu, Urara Watanabe, Y. Taniyama","doi":"10.1155/2023/5513733","DOIUrl":"https://doi.org/10.1155/2023/5513733","url":null,"abstract":"Offshore wind turbines (OWTs) are considered vital to the promotion of the development of renewable energy. Especially, floating OWTs can be deployed over a larger area than bottom-fixed OWTs. The floating OWTs, however, are vulnerable to vibration induced by disturbances and require a backup power supply in the case of power outage. On the one hand, various kinds of inerter-based devices have been proposed especially for vibration suppression of civil structures subjected to earthquake loadings. Recently, combined with electromagnetic devices, the inerter technologies have also been applied in the field of vibration energy harvesting such as point absorber wave energy converters. Thus, this paper proposes a novel floating OWT consisting of two bodies combined with inerter-based power take-off (PTO) devices which accomplishes vibration suppression and wave energy conversion at the same time. To investigate the vibration suppression and energy conversion capabilities of the proposed floating OWT with a variety of inerter-based PTO devices for ocean waves, numerical simulation studies employing WEC-Sim are conducted, and the performance of each system is compared for regular and irregular waves. Results show that the proposed floating OWT with the appropriately designed inerter-based PTO devices for the incident wave period has great potential for both vibration suppression and wave energy conversion in a specific frequency range.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"223 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85937904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jian Yu, Yaming Xu, C. Xing, Jianguo Zhou, Pai Pan
Crack detection based on deep learning is an advanced technology, and many scholars have proposed many methods for the segmentation of pavement cracks. However, due to the difference of image specifications and crack characteristics, some existing methods are not effective in detecting cracks of containment. To quickly detect cracks and accurately extract crack quantitative information, this paper proposes a crack detection model, called MA_CrackNet, based on deep learning and a crack quantitative analysis algorithm. MA_CrackNet is an end-to-end model based on multiscale fusions that achieve pixel-level segmentation of cracks. Experimental results show that the proposed MA_CrackNet has excellent performance in the crack detection task of nuclear containment, achieving a precision, recall, F1, and mean intersection-over-union (mIoU) of 86.07%, 89.96%, 87.97%, and 89.19%, respectively, outperforming other advanced semantic segmentation models. The quantification algorithm automatically measures the four characteristic indicators of the crack, namely, the length of the crack, the area, the maximum width, and the mean width and obtains reliable results.
{"title":"Pixel-Level Crack Detection and Quantification of Nuclear Containment with Deep Learning","authors":"Jian Yu, Yaming Xu, C. Xing, Jianguo Zhou, Pai Pan","doi":"10.1155/2023/9982080","DOIUrl":"https://doi.org/10.1155/2023/9982080","url":null,"abstract":"Crack detection based on deep learning is an advanced technology, and many scholars have proposed many methods for the segmentation of pavement cracks. However, due to the difference of image specifications and crack characteristics, some existing methods are not effective in detecting cracks of containment. To quickly detect cracks and accurately extract crack quantitative information, this paper proposes a crack detection model, called MA_CrackNet, based on deep learning and a crack quantitative analysis algorithm. MA_CrackNet is an end-to-end model based on multiscale fusions that achieve pixel-level segmentation of cracks. Experimental results show that the proposed MA_CrackNet has excellent performance in the crack detection task of nuclear containment, achieving a precision, recall, F1, and mean intersection-over-union (mIoU) of 86.07%, 89.96%, 87.97%, and 89.19%, respectively, outperforming other advanced semantic segmentation models. The quantification algorithm automatically measures the four characteristic indicators of the crack, namely, the length of the crack, the area, the maximum width, and the mean width and obtains reliable results.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73903313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dynamic-vibration-based structural damage identification (SDI) represents the main target for structural health monitoring (SHM). It is significant to consider the unavoidable uncertainties arising from both the structure and measuring noise. On the other hand, nonuniform measurement conditions often appear in actual SHM applications, which consist of two parts, i.e., spatial nonuniform characteristics for noises are induced by various intensities of input noise in every single sampling channel and multisensor stays in a damaged state. This paper proposes a new method for the SDI considering uncertainties in nonuniform measurement conditions integrating convolutional neural network (CNN). Herein, the great ability of feature extraction from the measurement associated with the convolutional network is used to handle the input data, and the mapping connection between the selected features and damage states is established. Time histories of structural responses, such as acceleration, are applied for damage identification. The application and accuracy of the CNN, which is trained with input uncertain parameters contaminated by stochastic noises, are verified by the finite element numerical and experimental results. Both uncertain parameters and measurement conditions are considered in the verification. The responses obtained from the numerical and experimental approach show that the proposed neural network model can identify the structural damage with high accuracy. The great robustness of the proposed method is examined by studying the influence of uncertainties, even considering the nonuniform measurement condition.
{"title":"Structural Damage Identification considering Uncertainties in Nonuniform Measurement Conditions Based on Convolution Neural Networks","authors":"Siyu Zhu, T. Xiang","doi":"10.1155/2023/8325686","DOIUrl":"https://doi.org/10.1155/2023/8325686","url":null,"abstract":"Dynamic-vibration-based structural damage identification (SDI) represents the main target for structural health monitoring (SHM). It is significant to consider the unavoidable uncertainties arising from both the structure and measuring noise. On the other hand, nonuniform measurement conditions often appear in actual SHM applications, which consist of two parts, i.e., spatial nonuniform characteristics for noises are induced by various intensities of input noise in every single sampling channel and multisensor stays in a damaged state. This paper proposes a new method for the SDI considering uncertainties in nonuniform measurement conditions integrating convolutional neural network (CNN). Herein, the great ability of feature extraction from the measurement associated with the convolutional network is used to handle the input data, and the mapping connection between the selected features and damage states is established. Time histories of structural responses, such as acceleration, are applied for damage identification. The application and accuracy of the CNN, which is trained with input uncertain parameters contaminated by stochastic noises, are verified by the finite element numerical and experimental results. Both uncertain parameters and measurement conditions are considered in the verification. The responses obtained from the numerical and experimental approach show that the proposed neural network model can identify the structural damage with high accuracy. The great robustness of the proposed method is examined by studying the influence of uncertainties, even considering the nonuniform measurement condition.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85235359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Structural health monitoring (SHM) system has been operating for a long time in a harsh environment, resulting in various abnormalities in the collected structural vibration monitoring data. Detecting these abnormal data not only requires user interaction but also is quite time-consuming. Inspired by the manual recognition process, a vibration data anomaly detection method based on the combined model of convolutional neural network (CNN) and long short-term memory (LSTM) network is proposed in this paper. This method simulates intelligent human decision making in two steps. First, the original data are reconstructed by two feature sequences with higher universality and smaller size. In the time domain, the residual signal is extracted from the upper and lower peak envelopes of the original data to characterize the symmetry of the data. In the frequency domain, the power spectral density sequence of the original data is extracted to characterize the interpretability of the data. Second, a CNN-LSTM model is constructed and trained which utilizes CNN to extract local high-level features of input sequence and inputs new continuous high-level feature representations into LSTM to learn global long-term dependencies of abnormal data features. For verification, the method was applied to the automatic classification of continuous monitoring data for 42 days of long-span bridge, and the average accuracy of the classification results exceeded 94% and the detection time was 78 minutes. Compared with existing methods, this method can detect abnormal data more accurately and efficiently and has a stronger generalization ability.
{"title":"Structural Vibration Data Anomaly Detection Based on Multiple Feature Information Using CNN-LSTM Model","authors":"Xiulin Zhang, Wensong Zhou","doi":"10.1155/2023/3906180","DOIUrl":"https://doi.org/10.1155/2023/3906180","url":null,"abstract":"Structural health monitoring (SHM) system has been operating for a long time in a harsh environment, resulting in various abnormalities in the collected structural vibration monitoring data. Detecting these abnormal data not only requires user interaction but also is quite time-consuming. Inspired by the manual recognition process, a vibration data anomaly detection method based on the combined model of convolutional neural network (CNN) and long short-term memory (LSTM) network is proposed in this paper. This method simulates intelligent human decision making in two steps. First, the original data are reconstructed by two feature sequences with higher universality and smaller size. In the time domain, the residual signal is extracted from the upper and lower peak envelopes of the original data to characterize the symmetry of the data. In the frequency domain, the power spectral density sequence of the original data is extracted to characterize the interpretability of the data. Second, a CNN-LSTM model is constructed and trained which utilizes CNN to extract local high-level features of input sequence and inputs new continuous high-level feature representations into LSTM to learn global long-term dependencies of abnormal data features. For verification, the method was applied to the automatic classification of continuous monitoring data for 42 days of long-span bridge, and the average accuracy of the classification results exceeded 94% and the detection time was 78 minutes. Compared with existing methods, this method can detect abnormal data more accurately and efficiently and has a stronger generalization ability.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87778944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Renan Rocha Ribeiro, Rafael de Almeida Sobral, I. B. Cavalcante, Luís Augusto Conte Mendes Veloso, Rodrigo de Melo Lameiras
Structural health monitoring (SHM) has gained importance because many structures are approaching the end of their design life and demanding maintenance and monitoring. Low-cost solutions may push forward a widespread implementation of SHM on infrastructures but further investigation is still required to assess the performance of technically accessible, simple, and scalable low-cost systems. This work presents the development and validation of a low-cost vibration-based SHM multinode wireless system, based on the Arduino platform, for identification of modal parameters in civil infrastructures. Full details about the hardware and source code of the system are disclosed in an open repository, allowing its reproduction even by non-specialists in electronics. The sampling frequency stability of the system is experimentally characterized, and interpolation postprocessing algorithms are proposed to solve inherent limitations. The system is validated, and its performance is investigated in impulse and ambient vibration tests performed in a real-scale slab and a high-grade system. The data obtained from the proposed system in impulse tests allowed estimation of natural frequencies within 2%, and MAC values around 0.3 to 0.9, in relation to those estimated with the high-grade system. However, the low-cost system was unable to produce usable data in ambient vibration tests.
{"title":"A Low-Cost Wireless Multinode Vibration Monitoring System for Civil Structures","authors":"Renan Rocha Ribeiro, Rafael de Almeida Sobral, I. B. Cavalcante, Luís Augusto Conte Mendes Veloso, Rodrigo de Melo Lameiras","doi":"10.1155/2023/5240059","DOIUrl":"https://doi.org/10.1155/2023/5240059","url":null,"abstract":"Structural health monitoring (SHM) has gained importance because many structures are approaching the end of their design life and demanding maintenance and monitoring. Low-cost solutions may push forward a widespread implementation of SHM on infrastructures but further investigation is still required to assess the performance of technically accessible, simple, and scalable low-cost systems. This work presents the development and validation of a low-cost vibration-based SHM multinode wireless system, based on the Arduino platform, for identification of modal parameters in civil infrastructures. Full details about the hardware and source code of the system are disclosed in an open repository, allowing its reproduction even by non-specialists in electronics. The sampling frequency stability of the system is experimentally characterized, and interpolation postprocessing algorithms are proposed to solve inherent limitations. The system is validated, and its performance is investigated in impulse and ambient vibration tests performed in a real-scale slab and a high-grade system. The data obtained from the proposed system in impulse tests allowed estimation of natural frequencies within 2%, and MAC values around 0.3 to 0.9, in relation to those estimated with the high-grade system. However, the low-cost system was unable to produce usable data in ambient vibration tests.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82028940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Niloofar Malekghaini, F. Ghahari, Hamed Ebrahimian, M. Bowers, Hoda Azari, E. Taciroglu
The well-known limitations of modal system identification methods have led to a broad exploration of alternative solutions for operational monitoring and damage diagnosis of structures. This study presents a time-domain Bayesian finite element model updating approach to jointly identify the vehicular loads and finite element modeling parameters of bridges using the vibration data and the location of vehicles traversing the bridge as input. A Bayesian model updating is devised and verified through a series of case studies based on numerically simulated data from a prestressed reinforced concrete box-girder bridge model. Damage states are defined for concrete degradation and delamination, steel corrosion, and loss of prestressing force. Ten different damage scenarios, encompassing the range from minor localized to major distributed damage, are examined. The responses of the damaged bridge are simulated under random traffic scenarios. The acceleration responses, along with the location of the vehicles on the bridge, are used for jointly estimating the model parameters and vehicular loads. The estimated model parameters are then used to infer the location and extent of damage within the bridge. The results show the successful performance of the proposed approach in a numerically simulated environment.
{"title":"Time-Domain Finite Element Model Updating for Operational Monitoring and Damage Identification of Bridges","authors":"Niloofar Malekghaini, F. Ghahari, Hamed Ebrahimian, M. Bowers, Hoda Azari, E. Taciroglu","doi":"10.1155/2023/4170149","DOIUrl":"https://doi.org/10.1155/2023/4170149","url":null,"abstract":"The well-known limitations of modal system identification methods have led to a broad exploration of alternative solutions for operational monitoring and damage diagnosis of structures. This study presents a time-domain Bayesian finite element model updating approach to jointly identify the vehicular loads and finite element modeling parameters of bridges using the vibration data and the location of vehicles traversing the bridge as input. A Bayesian model updating is devised and verified through a series of case studies based on numerically simulated data from a prestressed reinforced concrete box-girder bridge model. Damage states are defined for concrete degradation and delamination, steel corrosion, and loss of prestressing force. Ten different damage scenarios, encompassing the range from minor localized to major distributed damage, are examined. The responses of the damaged bridge are simulated under random traffic scenarios. The acceleration responses, along with the location of the vehicles on the bridge, are used for jointly estimating the model parameters and vehicular loads. The estimated model parameters are then used to infer the location and extent of damage within the bridge. The results show the successful performance of the proposed approach in a numerically simulated environment.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"161 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77341725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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