In daily monitoring of structures instrumented with long‐term structural health monitoring (SHM) systems, the acquired data is often corrupted with gross outliers due to hardware imperfection and/or electromagnetic interference. These unexpected spikes in data are not unusual and their existence may greatly influence the results of structural health evaluation and lead to false alarms. Hence, there is a high demand for executing data cleaning and data recovery, especially in harsh monitoring environment. In this paper, we propose a robust gross outlier removal method, termed Hankel‐structured robust principal component analysis (HRPCA), to remove gross outliers in the monitoring data of structural dynamic responses. Different from the deep‐learning‐based approaches that possess only outlier identification or anomaly classification ability, HRPCA is a rapid and integrated methodology for data cleaning, which enables outlier detection, outlier identification, and recovery of fault data. It capitalizes on the fundamental duality between the sparsity of the signal and the rank of the structured matrix. Using annihilating filter‐based fundamental duality, structural responses could be modeled as lying in a low‐dimensional subspace with additional Hankel structure; thus, the gross outliers could be represented as a sparse component. Then the outlier removal issue turns into a matrix factorization problem, which could be successfully solved by robust principal component analysis (RPCA). To validate the denoising capability of HRPCA, a laboratory experiment is first conducted on a five‐story building model where the reference clean signal is aware. Then real‐world monitoring data with varying degrees of outliers (e.g., single outlier, multiple outliers, and periodic outliers) collected from a cable‐stayed bridge and a high‐rise structure is used to further illustrate the efficiency of the proposed approach.
{"title":"Gross outlier removal and fault data recovery for SHM data of dynamic responses by an annihilating filter‐based Hankel‐structured robust PCA method","authors":"Si Chen, You-Wu Wang, Y. Ni","doi":"10.1002/stc.3144","DOIUrl":"https://doi.org/10.1002/stc.3144","url":null,"abstract":"In daily monitoring of structures instrumented with long‐term structural health monitoring (SHM) systems, the acquired data is often corrupted with gross outliers due to hardware imperfection and/or electromagnetic interference. These unexpected spikes in data are not unusual and their existence may greatly influence the results of structural health evaluation and lead to false alarms. Hence, there is a high demand for executing data cleaning and data recovery, especially in harsh monitoring environment. In this paper, we propose a robust gross outlier removal method, termed Hankel‐structured robust principal component analysis (HRPCA), to remove gross outliers in the monitoring data of structural dynamic responses. Different from the deep‐learning‐based approaches that possess only outlier identification or anomaly classification ability, HRPCA is a rapid and integrated methodology for data cleaning, which enables outlier detection, outlier identification, and recovery of fault data. It capitalizes on the fundamental duality between the sparsity of the signal and the rank of the structured matrix. Using annihilating filter‐based fundamental duality, structural responses could be modeled as lying in a low‐dimensional subspace with additional Hankel structure; thus, the gross outliers could be represented as a sparse component. Then the outlier removal issue turns into a matrix factorization problem, which could be successfully solved by robust principal component analysis (RPCA). To validate the denoising capability of HRPCA, a laboratory experiment is first conducted on a five‐story building model where the reference clean signal is aware. Then real‐world monitoring data with varying degrees of outliers (e.g., single outlier, multiple outliers, and periodic outliers) collected from a cable‐stayed bridge and a high‐rise structure is used to further illustrate the efficiency of the proposed approach.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"85 7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77073795","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}
V. Matveenko, G. Serovaev, N. Kosheleva, A. Fedorov
The paper considers errors that occur during strain measurement by fiber‐optic sensors based on Bragg gratings, which are mounted on the surface of the controlled object with a connecting material. Errors due to the use of the assumption of a uniaxial stress state in the Bragg grating zone in the strains calculation based on the measured physical quantities are considered. The errors associated with the strain gradient along the Bragg grating and the strain gradient from the measurement zone to the measuring element are analyzed. To answer the question of what strain is measured, the change in the measured strain as a result of mounting the sensor on the material surface is estimated. Models and algorithms for numerical simulation of errors arising in the strain measurement are presented. Numerical results are given for estimating the considered types of errors when sensor is mounted with epoxy adhesives on the surface of isotropic and anisotropic (fiberglass, carbon fiber) materials. Variants of experiments are presented in which various options of nonuniform distribution of strains and a complex stress state are provided in the zones of strain measurement. The experimental results are compared with the results of numerical simulation based on the finite element method. The results of the analysis of the choice of the resonant wavelength from the reflected optical spectrum, which is used to calculate the strains under the assumption of a uniaxial stress state in the Bragg grating, are presented.
{"title":"Numerical and experimental analysis of the reliability of strain measured by surface‐mounted fiber‐optic sensors based on Bragg gratings","authors":"V. Matveenko, G. Serovaev, N. Kosheleva, A. Fedorov","doi":"10.1002/stc.3142","DOIUrl":"https://doi.org/10.1002/stc.3142","url":null,"abstract":"The paper considers errors that occur during strain measurement by fiber‐optic sensors based on Bragg gratings, which are mounted on the surface of the controlled object with a connecting material. Errors due to the use of the assumption of a uniaxial stress state in the Bragg grating zone in the strains calculation based on the measured physical quantities are considered. The errors associated with the strain gradient along the Bragg grating and the strain gradient from the measurement zone to the measuring element are analyzed. To answer the question of what strain is measured, the change in the measured strain as a result of mounting the sensor on the material surface is estimated. Models and algorithms for numerical simulation of errors arising in the strain measurement are presented. Numerical results are given for estimating the considered types of errors when sensor is mounted with epoxy adhesives on the surface of isotropic and anisotropic (fiberglass, carbon fiber) materials. Variants of experiments are presented in which various options of nonuniform distribution of strains and a complex stress state are provided in the zones of strain measurement. The experimental results are compared with the results of numerical simulation based on the finite element method. The results of the analysis of the choice of the resonant wavelength from the reflected optical spectrum, which is used to calculate the strains under the assumption of a uniaxial stress state in the Bragg grating, are presented.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"559 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77600206","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}
S. Cantero-Chinchilla, C. Papadimitriou, J. Chiachío, M. Chiachío, P. Koumoutsakos, A. Fabro, D. Chronopoulos
Sensing is the cornerstone of any functional structural health monitoring technology, with sensor number and placement being a key aspect for reliable monitoring. We introduce for the first time a robust methodology for optimal sensor configuration based on the value of information that accounts for (1) uncertainties from updatable and nonupdatable parameters, (2) variability of the objective function with respect to nonupdatable parameters, and (3) the spatial correlation between sensors. The optimal sensor configuration is obtained by maximizing the expected value of information, which leads to a cost‐benefit analysis that entails model parameter uncertainties. The proposed methodology is demonstrated on an application of structural health monitoring in plate‐like structures using ultrasonic guided waves. We show that accounting for uncertainties is critical for an accurate diagnosis of damage. Furthermore, we provide critical assessment of the role of both the effect of modeling and measurement uncertainties and the optimization algorithm on the resulting sensor placement. The results on the health monitoring of an aluminum plate indicate the effectiveness and efficiency of the proposed methodology in discovering optimal sensor configurations.
{"title":"Robust optimal sensor configuration using the value of information","authors":"S. Cantero-Chinchilla, C. Papadimitriou, J. Chiachío, M. Chiachío, P. Koumoutsakos, A. Fabro, D. Chronopoulos","doi":"10.1002/stc.3143","DOIUrl":"https://doi.org/10.1002/stc.3143","url":null,"abstract":"Sensing is the cornerstone of any functional structural health monitoring technology, with sensor number and placement being a key aspect for reliable monitoring. We introduce for the first time a robust methodology for optimal sensor configuration based on the value of information that accounts for (1) uncertainties from updatable and nonupdatable parameters, (2) variability of the objective function with respect to nonupdatable parameters, and (3) the spatial correlation between sensors. The optimal sensor configuration is obtained by maximizing the expected value of information, which leads to a cost‐benefit analysis that entails model parameter uncertainties. The proposed methodology is demonstrated on an application of structural health monitoring in plate‐like structures using ultrasonic guided waves. We show that accounting for uncertainties is critical for an accurate diagnosis of damage. Furthermore, we provide critical assessment of the role of both the effect of modeling and measurement uncertainties and the optimization algorithm on the resulting sensor placement. The results on the health monitoring of an aluminum plate indicate the effectiveness and efficiency of the proposed methodology in discovering optimal sensor configurations.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79935514","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}
{"title":"SCHM to publish open access from 2023","authors":"L. Faravelli","doi":"10.1002/stc.3145","DOIUrl":"https://doi.org/10.1002/stc.3145","url":null,"abstract":"","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88455819","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}
The contribution of masonry infill walls (MIW) to the dynamic behavior of reinforced concrete (RC) buildings is investigated in this study. An existing non‐symmetrical, six‐story reinforced concrete building has been used as a test specimen. Dynamic characteristics of the building have been determined by the ambient vibration survey (AVS) first. Then, the masonry infill walls on its ground floor were completely demolished, and the obtained new form of the building was studied by AVS. Later on, two forms of the building were modeled to visualize its behavior under ambient conditions, and the dynamic characteristics of the building have been determined numerically. The attained experimental and numerical results for both forms of the building were compared, and the constructed numerical models of the building were calibrated by an interactive tuning algorithm defined according to the specific dynamic features of the building. As the last numerical analysis, all MIW were removed from the verified numerical model of the building and the dynamic analysis was repeated. The main goal of the study was accomplished by comparing the experimentally and numerically obtained dynamic results on the basis of dominant frequencies, mode shapes, torsional behavior, and soft story mechanism.
{"title":"Full‐scaled experimental and numerical investigation on the contribution of masonry infill walls into dynamic behavior of RC buildings","authors":"F. Aras, Tolga Akbaş, S. Çeribaşı, F. Catbas","doi":"10.1002/stc.3141","DOIUrl":"https://doi.org/10.1002/stc.3141","url":null,"abstract":"The contribution of masonry infill walls (MIW) to the dynamic behavior of reinforced concrete (RC) buildings is investigated in this study. An existing non‐symmetrical, six‐story reinforced concrete building has been used as a test specimen. Dynamic characteristics of the building have been determined by the ambient vibration survey (AVS) first. Then, the masonry infill walls on its ground floor were completely demolished, and the obtained new form of the building was studied by AVS. Later on, two forms of the building were modeled to visualize its behavior under ambient conditions, and the dynamic characteristics of the building have been determined numerically. The attained experimental and numerical results for both forms of the building were compared, and the constructed numerical models of the building were calibrated by an interactive tuning algorithm defined according to the specific dynamic features of the building. As the last numerical analysis, all MIW were removed from the verified numerical model of the building and the dynamic analysis was repeated. The main goal of the study was accomplished by comparing the experimentally and numerically obtained dynamic results on the basis of dominant frequencies, mode shapes, torsional behavior, and soft story mechanism.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73249043","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}
Estimating modal parameters requires significant user interaction, especially when parametric system identification methods are used and the physical modes are selected in the stabilization diagram. In this paper, a fast density peaks clustering algorithm combined with the covariance‐driven stochastic subspace identification method is used to automatically identify modal parameters. Before the automatic identification process, the spurious modes from the stochastic subspace identification method were eliminated by a two‐stage method, including using the soft and hard verification criteria to remove spurious modes in the first stage and the removal of spurious modes based on the stability of physical modes in the second stage; thus, a better stabilization diagram was obtained for the subsequent automatic identification. Furthermore, fast density peaks clustering algorithm was applied to select the appropriate structure modes from the stabilization diagram. In the entire identification process, no user participation was required. The proposed method was demonstrated on a 4‐degree of freedom (DOF) numerical model and a benchmark frame structure, and the results indicated that the modal parameters can be identified accurately even with the noise effects using the default user‐defined parameters. This method showed higher efficiency and universality than the existing methods. Finally, the applicability and robustness of the proposed method in automated operational mode tracking were verified on a real cable‐stayed bridge.
{"title":"Automatic identification of structural modal parameters based on density peaks clustering algorithm","authors":"Xiulin Zhang, Wensong Zhou, Yong Huang, Hui Li","doi":"10.1002/stc.3138","DOIUrl":"https://doi.org/10.1002/stc.3138","url":null,"abstract":"Estimating modal parameters requires significant user interaction, especially when parametric system identification methods are used and the physical modes are selected in the stabilization diagram. In this paper, a fast density peaks clustering algorithm combined with the covariance‐driven stochastic subspace identification method is used to automatically identify modal parameters. Before the automatic identification process, the spurious modes from the stochastic subspace identification method were eliminated by a two‐stage method, including using the soft and hard verification criteria to remove spurious modes in the first stage and the removal of spurious modes based on the stability of physical modes in the second stage; thus, a better stabilization diagram was obtained for the subsequent automatic identification. Furthermore, fast density peaks clustering algorithm was applied to select the appropriate structure modes from the stabilization diagram. In the entire identification process, no user participation was required. The proposed method was demonstrated on a 4‐degree of freedom (DOF) numerical model and a benchmark frame structure, and the results indicated that the modal parameters can be identified accurately even with the noise effects using the default user‐defined parameters. This method showed higher efficiency and universality than the existing methods. Finally, the applicability and robustness of the proposed method in automated operational mode tracking were verified on a real cable‐stayed bridge.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87290909","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}
Slender footbridges are prone to excessive vibrations due to pedestrian effects, and comfort criteria often govern their design. In this sense, composite materials that combine high damping capacity with relatively high stiffness and low mass can provide functional benefits. This paper presents a study of the dynamic behaviour of an 11 m long hybrid footbridge made of two I‐shaped pultruded glass fibre reinforced polymer (GFRP) main girders and a thin steel fibre reinforced self‐compacting concrete (SFRSCC) deck, in operation since 2015. The main goals were (i) to improve the knowledge of the dynamic properties of composite footbridges and (ii) to assess the benefits of using a structure made of pultruded GFRP instead of a conventional material (steel), namely, considering its greater ability to dissipate energy. The resonant frequencies, damping ratios, and mode shapes of the footbridge were identified based on experimental testing. A finite element (FE) model of the footbridge was developed and calibrated with test data and used to simulate the effects of pedestrian loads. Simulations of the same type were conducted on an equivalent structural system made of steel profiles. The simulation results of the two short‐span footbridges with similar natural frequencies enhance the impact of high‐order harmonics of the pedestrian load in the dynamic response. It is also shown that polymer‐based components can contribute to limiting vibrations in footbridges or even act as self‐dampers.
{"title":"Modal identification and damping performance of a full‐scale GFRP‐SFRSCC hybrid footbridge","authors":"Vitor Dacol, E. Caetano, J. Correia","doi":"10.1002/stc.3137","DOIUrl":"https://doi.org/10.1002/stc.3137","url":null,"abstract":"Slender footbridges are prone to excessive vibrations due to pedestrian effects, and comfort criteria often govern their design. In this sense, composite materials that combine high damping capacity with relatively high stiffness and low mass can provide functional benefits. This paper presents a study of the dynamic behaviour of an 11 m long hybrid footbridge made of two I‐shaped pultruded glass fibre reinforced polymer (GFRP) main girders and a thin steel fibre reinforced self‐compacting concrete (SFRSCC) deck, in operation since 2015. The main goals were (i) to improve the knowledge of the dynamic properties of composite footbridges and (ii) to assess the benefits of using a structure made of pultruded GFRP instead of a conventional material (steel), namely, considering its greater ability to dissipate energy. The resonant frequencies, damping ratios, and mode shapes of the footbridge were identified based on experimental testing. A finite element (FE) model of the footbridge was developed and calibrated with test data and used to simulate the effects of pedestrian loads. Simulations of the same type were conducted on an equivalent structural system made of steel profiles. The simulation results of the two short‐span footbridges with similar natural frequencies enhance the impact of high‐order harmonics of the pedestrian load in the dynamic response. It is also shown that polymer‐based components can contribute to limiting vibrations in footbridges or even act as self‐dampers.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74122078","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}
Yixian Li, Limin Sun, Y. Xia, Lanxin Luo, Ao Wang, Xudong Jian
Estimating the load distribution of a bridge structure enables to evaluate the in‐service state and predict the structural responses. This paper develops an iterative strategy to inversely estimate the traffic load distribution of a bridge from limited measurements. The computer vision technologies, including the YOLO network‐based object detection and a pixel coordinate‐based positioning approach, are used to locate the vehicle positions on the bridge deck and form a prior information vector of the input positions. Then, a generalized Tikhonov regularization method is proposed to estimate the load distribution using the bridge response and prior information. The regularization parameter is determined by the L‐curve method. The fusion of computer vision and regularization can improve the load identification accuracy and reduce the overfitting effect. The developed approach is applied to numerical and experimental examples under various load conditions. The load can be accurately identified in all cases, and the full‐field responses of the structures can be reconstructed with minor errors.
{"title":"General Tikhonov regularization‐based load estimation of bridges considering the computer vision‐extracted prior information","authors":"Yixian Li, Limin Sun, Y. Xia, Lanxin Luo, Ao Wang, Xudong Jian","doi":"10.1002/stc.3135","DOIUrl":"https://doi.org/10.1002/stc.3135","url":null,"abstract":"Estimating the load distribution of a bridge structure enables to evaluate the in‐service state and predict the structural responses. This paper develops an iterative strategy to inversely estimate the traffic load distribution of a bridge from limited measurements. The computer vision technologies, including the YOLO network‐based object detection and a pixel coordinate‐based positioning approach, are used to locate the vehicle positions on the bridge deck and form a prior information vector of the input positions. Then, a generalized Tikhonov regularization method is proposed to estimate the load distribution using the bridge response and prior information. The regularization parameter is determined by the L‐curve method. The fusion of computer vision and regularization can improve the load identification accuracy and reduce the overfitting effect. The developed approach is applied to numerical and experimental examples under various load conditions. The load can be accurately identified in all cases, and the full‐field responses of the structures can be reconstructed with minor errors.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89013079","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}
It is necessary to investigate the identification of structural systems and unknown inputs under non‐Gaussian measurement noises. In recent years, a few scholars have proposed methods of particle filter (PF) with unknown input for such task. However, these PF with unknown input require that unknown inputs appear in structural measurement equations. Such requirement may not always met, which restrict their practical application. To overcome this limitation, a generalized extended Kalman particle filter with unknown input (GEKPF‐UI) is proposed for the simultaneous identification of structural systems and unknown inputs under non‐Gaussian measurement noises. The proposed method is more general than the existing methods of PF with unknown input as it is applicable whether measurement equations contain or do not contain unknown inputs. It is proposed to establish the importance density function of PF by the generalized extended Kalman filter with unknown input (GEKF‐UI) recently developed by the authors, in which GEKF‐UI is utilized to generate particles and allow particles to carry the latest observational information. The effectiveness of the proposed method is verified through two numerical identification examples of a nonlinear hysteretic structure under two types of unknown inputs, including unknown external excitation and unknown seismic inputs, respectively.
{"title":"A generalized extended Kalman particle filter with unknown input for nonlinear system‐input identification under non‐Gaussian measurement noises","authors":"Y. Lei, Junlong Lai, Jinshan Huang, Chengkai Qi","doi":"10.1002/stc.3139","DOIUrl":"https://doi.org/10.1002/stc.3139","url":null,"abstract":"It is necessary to investigate the identification of structural systems and unknown inputs under non‐Gaussian measurement noises. In recent years, a few scholars have proposed methods of particle filter (PF) with unknown input for such task. However, these PF with unknown input require that unknown inputs appear in structural measurement equations. Such requirement may not always met, which restrict their practical application. To overcome this limitation, a generalized extended Kalman particle filter with unknown input (GEKPF‐UI) is proposed for the simultaneous identification of structural systems and unknown inputs under non‐Gaussian measurement noises. The proposed method is more general than the existing methods of PF with unknown input as it is applicable whether measurement equations contain or do not contain unknown inputs. It is proposed to establish the importance density function of PF by the generalized extended Kalman filter with unknown input (GEKF‐UI) recently developed by the authors, in which GEKF‐UI is utilized to generate particles and allow particles to carry the latest observational information. The effectiveness of the proposed method is verified through two numerical identification examples of a nonlinear hysteretic structure under two types of unknown inputs, including unknown external excitation and unknown seismic inputs, respectively.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82198883","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}
Embedded strain sensors are the primary measurement device for strain in the tensile layer of asphalt pavement. The favorable deformation compatibility between embedded strain sensor and asphalt layer is the key to ensure the precise measurement of mechanical response. However, the good deformation coordination may be difficult to maintain under different environments due to the viscoelasticity of asphalt mixture. In this study, 4‐point bending beam tests were performed to investigate deformation compatibility between embedded strain sensor and asphalt mixture under different temperature. Then, a quasi‐static finite element model (FEM) was employed to simulate static mechanical response of asphalt pavement, and the design requirements for embedded strain sensor were proposed considering deformation coordination. In addition, the rationality of the design requirements of the sensor was further validated in the dynamic response monitoring. The results indicate that the deformation compatibility between embedded strain sensor and pavement material changes at different temperatures. In order to ensure favorable deformation compatibility, the reinforcement of the protective housing should be eliminated and the equivalent modulus (EM) of the sensitive element shall be the same as that of the asphalt mixture. Considering the viscoelasticity of asphalt mixture, the strain sensor with lower EM is recommended in the dynamic response monitoring of pavement structure. This study provides a basis for optimizing the embedded strain sensor of asphalt pavement from the perspective of deformation compatibility.
{"title":"Theoretical analysis on the measurement accuracy of embedded strain sensor in asphalt pavement dynamic response monitoring based on FEM","authors":"Dong-Kyu Han, Guoqiang Liu, Yinfei Xi, Yongli Zhao","doi":"10.1002/stc.3140","DOIUrl":"https://doi.org/10.1002/stc.3140","url":null,"abstract":"Embedded strain sensors are the primary measurement device for strain in the tensile layer of asphalt pavement. The favorable deformation compatibility between embedded strain sensor and asphalt layer is the key to ensure the precise measurement of mechanical response. However, the good deformation coordination may be difficult to maintain under different environments due to the viscoelasticity of asphalt mixture. In this study, 4‐point bending beam tests were performed to investigate deformation compatibility between embedded strain sensor and asphalt mixture under different temperature. Then, a quasi‐static finite element model (FEM) was employed to simulate static mechanical response of asphalt pavement, and the design requirements for embedded strain sensor were proposed considering deformation coordination. In addition, the rationality of the design requirements of the sensor was further validated in the dynamic response monitoring. The results indicate that the deformation compatibility between embedded strain sensor and pavement material changes at different temperatures. In order to ensure favorable deformation compatibility, the reinforcement of the protective housing should be eliminated and the equivalent modulus (EM) of the sensitive element shall be the same as that of the asphalt mixture. Considering the viscoelasticity of asphalt mixture, the strain sensor with lower EM is recommended in the dynamic response monitoring of pavement structure. This study provides a basis for optimizing the embedded strain sensor of asphalt pavement from the perspective of deformation compatibility.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"100 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74216887","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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