Pub Date : 2020-01-01DOI: 10.12989/SSS.2020.26.4.451
L. V. Ho, S. Khatir, G. Roeck, T. Bui-Tien, M. Wahab
{"title":"Finite element model updating of a cable-stayed bridge using metaheuristic algorithms combined with Morris method for sensitivity analysis","authors":"L. V. Ho, S. Khatir, G. Roeck, T. Bui-Tien, M. Wahab","doi":"10.12989/SSS.2020.26.4.451","DOIUrl":"https://doi.org/10.12989/SSS.2020.26.4.451","url":null,"abstract":"","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66185943","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 : 2020-01-01DOI: 10.12989/SSS.2020.25.1.097
A. Arbabi, R. Kolahchi, M. R. Bidgoli
Due to the superior properties of nanoparticles, using them has been increased in concrete production technology. In this study, the effect of zinc oxide (ZnO) nanoparticles on the mechanical and smart properties of concrete was studied. At the first, the ZnO nanoparticles are dispersed in water using shaker, magnetic stirrer and ultrasonic devices. The nanoparticles with 3.5, 0.25, 0.75, and 1.0 volume percent are added to the concrete mixture and replaced by the appropriate amount of cement to compare with the control sample without any additives. In order to study the mechanical and smart properties of the concrete, the cubic samples for determining the compressive strength and cylindrical samples for determining tensile strength with different amounts of ZnO nanoparticles are produced and tested. The most important finding of this paper is about the smartness of the concrete due to the piezoelectric properties of the ZnO nanoparticles. In other words, the concrete in this study can produce the voltage when subjected to mechanical load and vice versa it can induce the mechanical displacement when subjected to external voltage. The experimental results show that the best volume percent for ZnO nanoparticles in 28-day samples is 0.5%. In other words, adding 0.5% ZnO nanoparticles to the concrete instead of cement leads to increases of 18.70% and 3.77% in the compressive and tensile strengths, respectively. In addition, it shows the best direct and reverse piezoelectric properties. It is also worth to mention that adding 3.5% zinc oxide nanoparticles, the setting of cement is stopped in the concrete mixture.
{"title":"Experimental study for ZnO nanofibers effect on the smart and mechanical properties of concrete","authors":"A. Arbabi, R. Kolahchi, M. R. Bidgoli","doi":"10.12989/SSS.2020.25.1.097","DOIUrl":"https://doi.org/10.12989/SSS.2020.25.1.097","url":null,"abstract":"Due to the superior properties of nanoparticles, using them has been increased in concrete production technology. In this study, the effect of zinc oxide (ZnO) nanoparticles on the mechanical and smart properties of concrete was studied. At the first, the ZnO nanoparticles are dispersed in water using shaker, magnetic stirrer and ultrasonic devices. The nanoparticles with 3.5, 0.25, 0.75, and 1.0 volume percent are added to the concrete mixture and replaced by the appropriate amount of cement to compare with the control sample without any additives. In order to study the mechanical and smart properties of the concrete, the cubic samples for determining the compressive strength and cylindrical samples for determining tensile strength with different amounts of ZnO nanoparticles are produced and tested. The most important finding of this paper is about the smartness of the concrete due to the piezoelectric properties of the ZnO nanoparticles. In other words, the concrete in this study can produce the voltage when subjected to mechanical load and vice versa it can induce the mechanical displacement when subjected to external voltage. The experimental results show that the best volume percent for ZnO nanoparticles in 28-day samples is 0.5%. In other words, adding 0.5% ZnO nanoparticles to the concrete instead of cement leads to increases of 18.70% and 3.77% in the compressive and tensile strengths, respectively. In addition, it shows the best direct and reverse piezoelectric properties. It is also worth to mention that adding 3.5% zinc oxide nanoparticles, the setting of cement is stopped in the concrete mixture.","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66184516","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 : 2020-01-01DOI: 10.12989/SSS.2020.25.6.765
Xinyu Ye, Zongjie Lyu, L. K. Foong
{"title":"Hybridized dragonfly, whale and ant lion algorithms in enlarged pile's behavior","authors":"Xinyu Ye, Zongjie Lyu, L. K. Foong","doi":"10.12989/SSS.2020.25.6.765","DOIUrl":"https://doi.org/10.12989/SSS.2020.25.6.765","url":null,"abstract":"","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66185250","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 : 2020-01-01DOI: 10.12989/SSS.2020.26.4.533
M. Nili, B. Zahraie, H. Taghaddos
{"title":"BrDSS: A decision support system for bridge maintenance planning employing bridge information modeling","authors":"M. Nili, B. Zahraie, H. Taghaddos","doi":"10.12989/SSS.2020.26.4.533","DOIUrl":"https://doi.org/10.12989/SSS.2020.26.4.533","url":null,"abstract":"","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66186044","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 : 2020-01-01DOI: 10.12989/SSS.2020.25.1.023
Q. Xia, Li Senlin, Jian Zhang
It is important to take into account the thermal behavior in assessing the structural condition of bridges. An effective method of studying the temperature effect of long-span bridges is numerical simulation based on the solar radiation models. This study aims to develop a time-varying solar radiation model which can consider the real-time weather changes, such as a cloud cover. A statistical analysis of the long-term monitoring data is first performed, especially on the temperature data between the south and north anchors of the bridge, to confirm that temperature difference can be used to describe real-time weather changes. Second, a defect in the traditional solar radiation model is detected in the temperature field simulation, whereby the value of the turbidity coefficient tu is subjective and cannot be used to describe the weather changes in real-time. Therefore, a new solar radiation model with modified turbidity coefficient γ is first established on the temperature difference between the south and north anchors. Third, the temperature data of several days are selected for model validation, with the results showing that the simulated temperature distribution is in good agreement with the measured temperature, while the calculated results by the traditional model had minor errors because the turbidity coefficient tu is uncertainty. In addition, the vertical and transverse temperature gradient of a typical cross-section and the temperature distribution of the tower are also studied.
{"title":"Temperature analysis of a long-span suspension bridge based on a time-varying solar radiation model","authors":"Q. Xia, Li Senlin, Jian Zhang","doi":"10.12989/SSS.2020.25.1.023","DOIUrl":"https://doi.org/10.12989/SSS.2020.25.1.023","url":null,"abstract":"It is important to take into account the thermal behavior in assessing the structural condition of bridges. An effective method of studying the temperature effect of long-span bridges is numerical simulation based on the solar radiation models. This study aims to develop a time-varying solar radiation model which can consider the real-time weather changes, such as a cloud cover. A statistical analysis of the long-term monitoring data is first performed, especially on the temperature data between the south and north anchors of the bridge, to confirm that temperature difference can be used to describe real-time weather changes. Second, a defect in the traditional solar radiation model is detected in the temperature field simulation, whereby the value of the turbidity coefficient tu is subjective and cannot be used to describe the weather changes in real-time. Therefore, a new solar radiation model with modified turbidity coefficient γ is first established on the temperature difference between the south and north anchors. Third, the temperature data of several days are selected for model validation, with the results showing that the simulated temperature distribution is in good agreement with the measured temperature, while the calculated results by the traditional model had minor errors because the turbidity coefficient tu is uncertainty. In addition, the vertical and transverse temperature gradient of a typical cross-section and the temperature distribution of the tower are also studied.","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66184628","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 : 2020-01-01DOI: 10.12989/SSS.2020.25.3.345
Subhajit Das, N. Dhang
{"title":"Structural damage identification of truss structures using self-controlled multi-stage particle swarm optimization","authors":"Subhajit Das, N. Dhang","doi":"10.12989/SSS.2020.25.3.345","DOIUrl":"https://doi.org/10.12989/SSS.2020.25.3.345","url":null,"abstract":"","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66184811","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 : 2020-01-01DOI: 10.12989/SSS.2020.26.1.035
Duong H. Nguyen, H. Tran-Ngoc, T. Bui-Tien, G. Roeck, M. Wahab
This paper proposes the use of transmissibility functions combined with a machine learning algorithm, Artificial Neural Networks (ANNs), to assess damage in a truss bridge. A new approach method, which makes use of the input parameters calculated from the transmissibility function, is proposed. The network not only can predict the existence of damage, but also can classify the damage types and identity the location of the damage. Sensors are installed in the truss joints in order to measure the bridge vibration responses under train and ambient excitations. A finite element (FE) model is constructed for the bridge and updated using FE software and experimental data. Both single damage and multiple damage cases are simulated in the bridge model with different scenarios. In each scenario, the vibration responses at the considered nodes are recorded and then used to calculate the transmissibility functions. The transmissibility damage indicators are calculated and stored as ANNs inputs. The outputs of the ANNs are the damage type, location and severity. Two machine learning algorithms are used; one for classifying the type and location of damage, whereas the other for finding the severity of damage. The measurements of the Nam O bridge, a truss railway bridge in Vietnam, is used to illustrate the method. The proposed method not only can distinguish the damage type, but also it can accurately identify damage level.
{"title":"Damage detection in truss bridges using transmissibility and machine learning algorithm : application to Nam O bridge","authors":"Duong H. Nguyen, H. Tran-Ngoc, T. Bui-Tien, G. Roeck, M. Wahab","doi":"10.12989/SSS.2020.26.1.035","DOIUrl":"https://doi.org/10.12989/SSS.2020.26.1.035","url":null,"abstract":"This paper proposes the use of transmissibility functions combined with a machine learning algorithm, Artificial Neural Networks (ANNs), to assess damage in a truss bridge. A new approach method, which makes use of the input parameters calculated from the transmissibility function, is proposed. The network not only can predict the existence of damage, but also can classify the damage types and identity the location of the damage. Sensors are installed in the truss joints in order to measure the bridge vibration responses under train and ambient excitations. A finite element (FE) model is constructed for the bridge and updated using FE software and experimental data. Both single damage and multiple damage cases are simulated in the bridge model with different scenarios. In each scenario, the vibration responses at the considered nodes are recorded and then used to calculate the transmissibility functions. The transmissibility damage indicators are calculated and stored as ANNs inputs. The outputs of the ANNs are the damage type, location and severity. Two machine learning algorithms are used; one for classifying the type and location of damage, whereas the other for finding the severity of damage. The measurements of the Nam O bridge, a truss railway bridge in Vietnam, is used to illustrate the method. The proposed method not only can distinguish the damage type, but also it can accurately identify damage level.","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66185308","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 : 2020-01-01DOI: 10.12989/SSS.2020.26.4.495
F. Castaño, R. Haber, Wael M. Mohammed, M. Nejman, Alberto Villalonga, J. Lastra
{"title":"Quality monitoring of complex manufacturing systems on the basis of model driven approach","authors":"F. Castaño, R. Haber, Wael M. Mohammed, M. Nejman, Alberto Villalonga, J. Lastra","doi":"10.12989/SSS.2020.26.4.495","DOIUrl":"https://doi.org/10.12989/SSS.2020.26.4.495","url":null,"abstract":"","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66185767","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：