Pub Date : 2021-09-01DOI: 10.12989/SSS.2021.28.3.355
Chunhee Cho, LeThanh Long, JeeWoong Park, Sung-Hwan Jang
A passive wireless strain sensing method using antenna sensors has significantly advanced structural health monitoring systems. Since the dimensions of antenna sensors are sensitive to their strain sensing performance and operating frequency, an iterative tuning process is required to achieve a final optimized design. Although multi-physics finite element simulation enables accurate estimation of antenna performance for each turning iteration, the simulation process requires high computational resources. Therefore, antenna tuning processes are recognized as obstacles to delay the final design process. In this study, we explore the potential of multi-physics informed models as an alternative approach for analyzing antenna sensors. Through deep neural networks, as a branch of the machine-learning algorithms, we formulate multi-physics informed models with six input parameters (antenna dimensions) and two outputs (resonance frequency and strain sensitivity). Twenty-two hundred high fidelity data sets are prepared by simulating multi-physics models: 1,600, 400, and 200 data sets are applied to deep neural network regression (DNNR) training, validating, and testing, respectively. From extensive data investigation, an optimized DNNR architecture is obtained to be two layers, with 16 neurons in each layer. Its training, validating, and testing values of mean square errors are 13.01, 44.22, 37.27, respectively. Finally, the proposed multi-physics informed model predicts the resonance frequency and strain sensitivity with errors of 0.1% and 0.07%, respectively. In addition, since the average computation speed for each tuning process is 0.007 seconds, the practical usefulness of the proposed method is also proven.
{"title":"A multi-physics informed antenna sensor model through the deep neural network regression","authors":"Chunhee Cho, LeThanh Long, JeeWoong Park, Sung-Hwan Jang","doi":"10.12989/SSS.2021.28.3.355","DOIUrl":"https://doi.org/10.12989/SSS.2021.28.3.355","url":null,"abstract":"A passive wireless strain sensing method using antenna sensors has significantly advanced structural health monitoring systems. Since the dimensions of antenna sensors are sensitive to their strain sensing performance and operating frequency, an iterative tuning process is required to achieve a final optimized design. Although multi-physics finite element simulation enables accurate estimation of antenna performance for each turning iteration, the simulation process requires high computational resources. Therefore, antenna tuning processes are recognized as obstacles to delay the final design process. In this study, we explore the potential of multi-physics informed models as an alternative approach for analyzing antenna sensors. Through deep neural networks, as a branch of the machine-learning algorithms, we formulate multi-physics informed models with six input parameters (antenna dimensions) and two outputs (resonance frequency and strain sensitivity). Twenty-two hundred high fidelity data sets are prepared by simulating multi-physics models: 1,600, 400, and 200 data sets are applied to deep neural network regression (DNNR) training, validating, and testing, respectively. From extensive data investigation, an optimized DNNR architecture is obtained to be two layers, with 16 neurons in each layer. Its training, validating, and testing values of mean square errors are 13.01, 44.22, 37.27, respectively. Finally, the proposed multi-physics informed model predicts the resonance frequency and strain sensitivity with errors of 0.1% and 0.07%, respectively. In addition, since the average computation speed for each tuning process is 0.007 seconds, the practical usefulness of the proposed method is also proven.","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":"28 1","pages":"355-362"},"PeriodicalIF":3.5,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41581548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The enclosed civil structures pose a challenging environment for wireless communication between sensor nodes. Wireless electromagnetic (EM) signal attenuates significantly when transmitting through reinforced concrete structures. This paper simulates the signal attenuation for plain concrete, pure steel rebar lattice and reinforced concrete using finite element method (FEM) in Ansoft High Frequency Structure Simulator (HFSS). Jonscher model is found to be a better concrete dielectric model than Debye model from the attenuation test results. FEM simulation for signal attenuation of reinforced concrete (RC) slab is validated by finite difference time domain (FDTD) simulation and test results from literature. Optimal frequency to minimize the signal attenuation through RC structure is in the range of 0.35 GHz ~ 0.5 GHz. Resonance occurs at t / (λc/4) = 2n and t / (λc/4) = 2n + 1, n = 1, 2, 3, 4, ... for low concrete volumetric water content (VWC). Signal attenuation is highly linear with slab thickness t for high concrete VWC. 433 MHz is suggested for real application of wireless sensor network considering the antenna size and optimization results. FEM simulation is validated by the experiment using intact wireless sensor nodes.
{"title":"Finite element simulation and frequency optimization for wireless signal transmission through RC structures","authors":"Jingkang Shi, Feiyang Wang, Dongming Zhang, Hong-wei Huang","doi":"10.12989/SSS.2021.28.3.319","DOIUrl":"https://doi.org/10.12989/SSS.2021.28.3.319","url":null,"abstract":"The enclosed civil structures pose a challenging environment for wireless communication between sensor nodes. Wireless electromagnetic (EM) signal attenuates significantly when transmitting through reinforced concrete structures. This paper simulates the signal attenuation for plain concrete, pure steel rebar lattice and reinforced concrete using finite element method (FEM) in Ansoft High Frequency Structure Simulator (HFSS). Jonscher model is found to be a better concrete dielectric model than Debye model from the attenuation test results. FEM simulation for signal attenuation of reinforced concrete (RC) slab is validated by finite difference time domain (FDTD) simulation and test results from literature. Optimal frequency to minimize the signal attenuation through RC structure is in the range of 0.35 GHz ~ 0.5 GHz. Resonance occurs at t / (λc/4) = 2n and t / (λc/4) = 2n + 1, n = 1, 2, 3, 4, ... for low concrete volumetric water content (VWC). Signal attenuation is highly linear with slab thickness t for high concrete VWC. 433 MHz is suggested for real application of wireless sensor network considering the antenna size and optimization results. FEM simulation is validated by the experiment using intact wireless sensor nodes.","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":"28 1","pages":"319"},"PeriodicalIF":3.5,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43282247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.12989/SSS.2021.28.3.343
L. Hadji, A. Tounsi
In this paper a higher-order shear deformation plate theory is presented to investigate the stress distribution and static deflections of functionally graded sandwich plates with porosity effects. The displacement field of the present theory is chosen based on nonlinear variations in the in-plane displacements through the thickness of the plate. By dividing the transverse displacement into the bending and shear parts and making further assumptions, the number of unknowns and equations of motion of the present theory is reduced a and hence makes them simple to use. The functionally graded materials (FGM) used in plates contain probably a porosity volume fraction which needs taking into account this aspect of imperfection in the mechanical bahavior of such structures. The present work aims to study the effect of the distribution forms of porosity on the bending of simply supported FG sandwich plate. The governing equations of the problem are derived by using the principle of virtual work. In the solution of the governing equations, the Navier procedure is used for the simply supported plate. In the porosity effect, four different porosity types are used for functionally graded sandwich plates. In the numerical results, the effects of the porosity parameters, porosity types and aspect ratio of plates on the normal stress, shear stress and static deflections of the functionally graded sandwich plates are presented and discussed. Also, some comparison studies are performed in order to validate the present formulations.
{"title":"Static deflections and stress distribution of functionally graded sandwich plates with porosity","authors":"L. Hadji, A. Tounsi","doi":"10.12989/SSS.2021.28.3.343","DOIUrl":"https://doi.org/10.12989/SSS.2021.28.3.343","url":null,"abstract":"In this paper a higher-order shear deformation plate theory is presented to investigate the stress distribution and static deflections of functionally graded sandwich plates with porosity effects. The displacement field of the present theory is chosen based on nonlinear variations in the in-plane displacements through the thickness of the plate. By dividing the transverse displacement into the bending and shear parts and making further assumptions, the number of unknowns and equations of motion of the present theory is reduced a and hence makes them simple to use. The functionally graded materials (FGM) used in plates contain probably a porosity volume fraction which needs taking into account this aspect of imperfection in the mechanical bahavior of such structures. The present work aims to study the effect of the distribution forms of porosity on the bending of simply supported FG sandwich plate. The governing equations of the problem are derived by using the principle of virtual work. In the solution of the governing equations, the Navier procedure is used for the simply supported plate. In the porosity effect, four different porosity types are used for functionally graded sandwich plates. In the numerical results, the effects of the porosity parameters, porosity types and aspect ratio of plates on the normal stress, shear stress and static deflections of the functionally graded sandwich plates are presented and discussed. Also, some comparison studies are performed in order to validate the present formulations.","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":"28 1","pages":"343"},"PeriodicalIF":3.5,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44383150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.12989/SSS.2021.28.3.425
Chunxiang Li, Hai Li, Xu Chen
While recognized as most rigorous procedure leading to 'exact' structural seismic responses, nonlinear time history analysis is usually time consuming and computational demanding, especially when numerous structures remain to be analyzed. This paper proposes a framework to improve the time efficiency in evaluating the structural seismic demands, using ensemble machine learning models based on 'classification-regression' philosophy. Typical tall pier bridges widely located in southwest China are employed as illustrative examples to validate the efficiency and performance of this proposed framework. The results and discussion show that with properly selected input variables, the proposed ensemble model (ORF-ANN herein) performs better in predicting seismic demands than other single learning algorithms (i.e., ANN and ORF), while the time efficiency is improved over 90%. This proposed model could drastically improve the efficiency for determining structural parameters in preliminary design process, and thus reduce the iterations of trail analysis. Additionally, the model constructed from proposed framework is believed especially favored for evaluating the post-earthquake states/resilience of a region and/or highway network, where thousands of structures might be contained, and conducting nonlinear time history analysis for each one would be prohibitively time consuming and delay the rescue operations.
{"title":"A framework for fast estimation of structural seismic responses using ensemble machine learning model","authors":"Chunxiang Li, Hai Li, Xu Chen","doi":"10.12989/SSS.2021.28.3.425","DOIUrl":"https://doi.org/10.12989/SSS.2021.28.3.425","url":null,"abstract":"While recognized as most rigorous procedure leading to 'exact' structural seismic responses, nonlinear time history analysis is usually time consuming and computational demanding, especially when numerous structures remain to be analyzed. This paper proposes a framework to improve the time efficiency in evaluating the structural seismic demands, using ensemble machine learning models based on 'classification-regression' philosophy. Typical tall pier bridges widely located in southwest China are employed as illustrative examples to validate the efficiency and performance of this proposed framework. The results and discussion show that with properly selected input variables, the proposed ensemble model (ORF-ANN herein) performs better in predicting seismic demands than other single learning algorithms (i.e., ANN and ORF), while the time efficiency is improved over 90%. This proposed model could drastically improve the efficiency for determining structural parameters in preliminary design process, and thus reduce the iterations of trail analysis. Additionally, the model constructed from proposed framework is believed especially favored for evaluating the post-earthquake states/resilience of a region and/or highway network, where thousands of structures might be contained, and conducting nonlinear time history analysis for each one would be prohibitively time consuming and delay the rescue operations.","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":"28 1","pages":"425"},"PeriodicalIF":3.5,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46497856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.12989/SSS.2021.28.3.407
Z. Ying, Y. W. Wang, Y. Ni, C. Xu
{"title":"Model-free identification of multiple periodic excitations and detection of structural anomaly using noisy response measurements","authors":"Z. Ying, Y. W. Wang, Y. Ni, C. Xu","doi":"10.12989/SSS.2021.28.3.407","DOIUrl":"https://doi.org/10.12989/SSS.2021.28.3.407","url":null,"abstract":"","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":"28 1","pages":"407-423"},"PeriodicalIF":3.5,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48373666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.12989/SSS.2021.28.3.363
Wen-Yu He, Xiucai Ding, W. Ren, Yue-Ling Jing
The modal parameters identified under operation conditions are normally employed for bridge damage detection. However, the moving vehicles are usually deemed as part of the operation conditions without considering their mass property. Thus, the identified modal parameters belong to the vehicle-bridge system rather than the bridge itself, which would affect the effectiveness of subsequent damage detection. In this paper, the effects of moving vehicles on the identified frequencies and mode shapes under operation conditions are investigated via finite element model. The necessary of considering the moving vehicle effects is demonstrated by comparing the modal parameters variations induced by the moving vehicle and bridge damage. Then the empirical formulas to eliminate the moving vehicle effects considering the vehicle mass, velocity, bridge span and relative position are established by using the orthogonal test and least square method. Finally, examples are conducted to verify of the effectiveness of the proposed empirical formulas.
{"title":"Elimination of moving vehicles effects on modal identification of beam type bridges","authors":"Wen-Yu He, Xiucai Ding, W. Ren, Yue-Ling Jing","doi":"10.12989/SSS.2021.28.3.363","DOIUrl":"https://doi.org/10.12989/SSS.2021.28.3.363","url":null,"abstract":"The modal parameters identified under operation conditions are normally employed for bridge damage detection. However, the moving vehicles are usually deemed as part of the operation conditions without considering their mass property. Thus, the identified modal parameters belong to the vehicle-bridge system rather than the bridge itself, which would affect the effectiveness of subsequent damage detection. In this paper, the effects of moving vehicles on the identified frequencies and mode shapes under operation conditions are investigated via finite element model. The necessary of considering the moving vehicle effects is demonstrated by comparing the modal parameters variations induced by the moving vehicle and bridge damage. Then the empirical formulas to eliminate the moving vehicle effects considering the vehicle mass, velocity, bridge span and relative position are established by using the orthogonal test and least square method. Finally, examples are conducted to verify of the effectiveness of the proposed empirical formulas.","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":"28 1","pages":"363"},"PeriodicalIF":3.5,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47767292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.12989/SSS.2021.28.3.391
Jiayue Xue, Ge Ou
Wind-induced dynamic response of the nonlinear structure is critical for the structural safety and reliability. The traditional approaches for this response including observation or simulation focus on the structural health monitoring, the experiment, or finite element model development. However, all these approaches require high cost or computational investment. This paper proposes to predict the wind-induced dynamic response of the nonlinear structure with a novel deep learning approach, LSTM, and applies this in a structural lifeline system, the transmission tower-line system. By constructing the optimized LSTM architectures, the proposed method applies to both the linear structure, the single transmission tower and the nonlinear structure, the transmission tower-line system, with promising results for the dynamic and extreme response prediction. It can conclude that the layers and the hidden units have a strong impact on the LSTM prediction performance, and with proper training data set, the computational time can significantly decrease. A comparison surrogate model developed by CNN is also utilized to demonstrate the robustness of the LSTM-based surrogate model with limited data scale.
{"title":"Predicting wind-induced structural response with LSTM in transmission tower-line system","authors":"Jiayue Xue, Ge Ou","doi":"10.12989/SSS.2021.28.3.391","DOIUrl":"https://doi.org/10.12989/SSS.2021.28.3.391","url":null,"abstract":"Wind-induced dynamic response of the nonlinear structure is critical for the structural safety and reliability. The traditional approaches for this response including observation or simulation focus on the structural health monitoring, the experiment, or finite element model development. However, all these approaches require high cost or computational investment. This paper proposes to predict the wind-induced dynamic response of the nonlinear structure with a novel deep learning approach, LSTM, and applies this in a structural lifeline system, the transmission tower-line system. By constructing the optimized LSTM architectures, the proposed method applies to both the linear structure, the single transmission tower and the nonlinear structure, the transmission tower-line system, with promising results for the dynamic and extreme response prediction. It can conclude that the layers and the hidden units have a strong impact on the LSTM prediction performance, and with proper training data set, the computational time can significantly decrease. A comparison surrogate model developed by CNN is also utilized to demonstrate the robustness of the LSTM-based surrogate model with limited data scale.","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":"28 1","pages":"391-405"},"PeriodicalIF":3.5,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44965810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.12989/SSS.2021.28.3.305
Yang Yang, Keyong Shen, Gholamreza Ghasemian Talkhunche, M. Arefi
This paper uses higher-order shear deformation theory and modified couple stress theory (MCST) to the electroelastic results of FG micro-shell integrated with piezoelectric thin sheets subjected to electrical and mechanical loads rested on Pasternak's foundation. Third-order shear deformation theory (TSDT) is used for the description of the displacement field. Effect of micro-size is applied using MCST with the introduction of one micro-length scale parameter. Governing equations are derived based on the principle of virtual work. Micro-shell is composed of a FG micro core and two piezoelectric hollow shells. The numerical results are obtained for the simply-supported boundary conditions. Longitudinal and radial displacements are presented in terms of important parameters such as applied electric potentials, micro length scale parameter, dimensionless geometric parameters and two parameters of Pasternak's foundation.
{"title":"Effect of applied electric potential and micro length scale parameters on the electroelastic analysis of three-layered shear deformable micro-shell","authors":"Yang Yang, Keyong Shen, Gholamreza Ghasemian Talkhunche, M. Arefi","doi":"10.12989/SSS.2021.28.3.305","DOIUrl":"https://doi.org/10.12989/SSS.2021.28.3.305","url":null,"abstract":"This paper uses higher-order shear deformation theory and modified couple stress theory (MCST) to the electroelastic results of FG micro-shell integrated with piezoelectric thin sheets subjected to electrical and mechanical loads rested on Pasternak's foundation. Third-order shear deformation theory (TSDT) is used for the description of the displacement field. Effect of micro-size is applied using MCST with the introduction of one micro-length scale parameter. Governing equations are derived based on the principle of virtual work. Micro-shell is composed of a FG micro core and two piezoelectric hollow shells. The numerical results are obtained for the simply-supported boundary conditions. Longitudinal and radial displacements are presented in terms of important parameters such as applied electric potentials, micro length scale parameter, dimensionless geometric parameters and two parameters of Pasternak's foundation.","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":"28 1","pages":"305-318"},"PeriodicalIF":3.5,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47126299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.12989/SSS.2021.28.2.181
Rajanikant Rao, B. Sindu, S. Sasmal
There is a growing need to develop sensors which can be embedded into the structures during the construction stage itself for developing smart structures. It is preferred to develop these kinds of sensors with the material same as that of material used in construction for the sake of compatibility and better capturing the actual state of distress in the structure. Towards this, in this study cement based piezo-resistive sensors are developed with the help of conductive nano-fillers (Carbon Nanotubes (CNTs)). Since the sensors are cement based, and porous in nature, the characteristics of the sensor will vary due to water penetration into the sensor. As the structures with such embedded sensors have to perform for years, understanding the variations in the characteristics of the sensor due to pore structure is very important. In this regard, the conductivity of the sensor is assessed where the effect of dosage of CNTs, functionalization of CNTs, type of electrical conductivity measurement (both DC and AC) and pore water are the parameters. The strain sensitivity of the sensors under cyclic stress is also investigated and reported in the present study. The findings of this study will help in developing continuous health monitoring strategies using highly sensitive embeddable cement-based nanocomposites.
{"title":"Performance of cement composite embeddable sensors for strain-based health monitoring of in-service structures","authors":"Rajanikant Rao, B. Sindu, S. Sasmal","doi":"10.12989/SSS.2021.28.2.181","DOIUrl":"https://doi.org/10.12989/SSS.2021.28.2.181","url":null,"abstract":"There is a growing need to develop sensors which can be embedded into the structures during the construction stage itself for developing smart structures. It is preferred to develop these kinds of sensors with the material same as that of material used in construction for the sake of compatibility and better capturing the actual state of distress in the structure. Towards this, in this study cement based piezo-resistive sensors are developed with the help of conductive nano-fillers (Carbon Nanotubes (CNTs)). Since the sensors are cement based, and porous in nature, the characteristics of the sensor will vary due to water penetration into the sensor. As the structures with such embedded sensors have to perform for years, understanding the variations in the characteristics of the sensor due to pore structure is very important. In this regard, the conductivity of the sensor is assessed where the effect of dosage of CNTs, functionalization of CNTs, type of electrical conductivity measurement (both DC and AC) and pore water are the parameters. The strain sensitivity of the sensors under cyclic stress is also investigated and reported in the present study. The findings of this study will help in developing continuous health monitoring strategies using highly sensitive embeddable cement-based nanocomposites.","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":"28 1","pages":"181-193"},"PeriodicalIF":3.5,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43309813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.12989/SSS.2021.28.2.261
Yan Wang, Jing Lv, Yan Feng, Bowen Dai, Cheng Wang, Jing Wu, Zitao Chen
Substructure pseudo-dynamic hybrid simulation (SPDHS) is an advanced structural seismic testing method which combines physical experiment and numerical simulation. Generally, the key components which display nonlinearity first are taken as experimental substructures for actual test, and the remaining parts are modeled in simulation. Model updating techniques can be effectively applied to enhance the model precision of nonlinear numerical elements. Specifically, the constitutive model of the experimental substructure is identified online by the instantaneously-measured data, and the corresponding numerical elements with similar hysteretic behaviors are updated synchronously. Artificial neural network (ANN) can recognize the system which cannot be represented by definite numerical model, and thus avoids the structural response distortion caused by the inherent numerical model defects. In this study, a framework for online model updating in SPDHS with ANN method is expanded to implement actual test validation. Moreover, the effectiveness of ANN method is demonstrated by practical tests of a two-story frame model with bending dampers. Additionally, the unscented Kalman filter technique and offline ANN identification approach are both examined in the test validation. The experimental results show that, under the identical loading history, the online ANN method can significantly reduce the model errors and improve the accuracy of SPDHS.
{"title":"Implementation of online model updating with ANN method in substructure pseudo-dynamic hybrid simulation","authors":"Yan Wang, Jing Lv, Yan Feng, Bowen Dai, Cheng Wang, Jing Wu, Zitao Chen","doi":"10.12989/SSS.2021.28.2.261","DOIUrl":"https://doi.org/10.12989/SSS.2021.28.2.261","url":null,"abstract":"Substructure pseudo-dynamic hybrid simulation (SPDHS) is an advanced structural seismic testing method which combines physical experiment and numerical simulation. Generally, the key components which display nonlinearity first are taken as experimental substructures for actual test, and the remaining parts are modeled in simulation. Model updating techniques can be effectively applied to enhance the model precision of nonlinear numerical elements. Specifically, the constitutive model of the experimental substructure is identified online by the instantaneously-measured data, and the corresponding numerical elements with similar hysteretic behaviors are updated synchronously. Artificial neural network (ANN) can recognize the system which cannot be represented by definite numerical model, and thus avoids the structural response distortion caused by the inherent numerical model defects. In this study, a framework for online model updating in SPDHS with ANN method is expanded to implement actual test validation. Moreover, the effectiveness of ANN method is demonstrated by practical tests of a two-story frame model with bending dampers. Additionally, the unscented Kalman filter technique and offline ANN identification approach are both examined in the test validation. The experimental results show that, under the identical loading history, the online ANN method can significantly reduce the model errors and improve the accuracy of SPDHS.","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":"28 1","pages":"261"},"PeriodicalIF":3.5,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47561882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：