In damage‐level classification, deep learning. models are more likely to focus on regions unrelated to classification targets because of the complexities inherent in real data, such as the diversity of damages (e.g., crack, efflorescence, and corrosion). This causes performance degradation. To solve this problem, it is necessary to handle data complexity and uncertainty. This study proposes a multimodal deep learning model that can focus on damaged regions using text data related to damage in images, such as materials and components. Furthermore, by adjusting the effect of attention maps on damage‐level classification performance based on the confidence calculated when estimating these maps, the proposed method realizes an accurate damage‐level classification. Our contribution is the development of a model with an end‐to‐end multimodal attention mechanism that can simultaneously consider both text and image data and the confidence of the attention map. Finally, experiments using real images validate the effectiveness of the proposed method.
{"title":"Damage‐level classification considering both correlation between image and text data and confidence of attention map","authors":"Keisuke Maeda, Naoki Ogawa, Takahiro Ogawa, Miki Haseyama","doi":"10.1111/mice.13366","DOIUrl":"https://doi.org/10.1111/mice.13366","url":null,"abstract":"In damage‐level classification, deep learning. models are more likely to focus on regions unrelated to classification targets because of the complexities inherent in real data, such as the diversity of damages (e.g., crack, efflorescence, and corrosion). This causes performance degradation. To solve this problem, it is necessary to handle data complexity and uncertainty. This study proposes a multimodal deep learning model that can focus on damaged regions using text data related to damage in images, such as materials and components. Furthermore, by adjusting the effect of attention maps on damage‐level classification performance based on the confidence calculated when estimating these maps, the proposed method realizes an accurate damage‐level classification. Our contribution is the development of a model with an end‐to‐end multimodal attention mechanism that can simultaneously consider both text and image data and the confidence of the attention map. Finally, experiments using real images validate the effectiveness of the proposed method.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"13 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Structural response estimation based on deep learning can suffer from reduced estimation performance owing to discrepancies between the training and test data as the noise level in the test data increases. This study proposes a short‐time Fourier transform‐based long short‐term memory (STFT‐LSTM) model to improve estimation performance in the presence of noise and ensure estimation robustness. This model enables robust estimations in the presence of noise by positioning an STFT layer before feeding the data into the LSTM layer. The output transformed into the time‐frequency domain by the STFT layer is learned by the LSTM model. The robustness of the proposed model was validated using a numerical model with three degrees of freedom at various signal‐to‐noise ratio levels, and its robustness against impulse and periodic noise was verified. Experimental validation assessed the estimation robustness under impact load and verified the robustness against environmental noise in the acquired acceleration response.
{"title":"Noise‐robust structural response estimation method using short‐time Fourier transform and long short‐term memory","authors":"Da Yo Yun, Hyo Seon Park","doi":"10.1111/mice.13370","DOIUrl":"https://doi.org/10.1111/mice.13370","url":null,"abstract":"Structural response estimation based on deep learning can suffer from reduced estimation performance owing to discrepancies between the training and test data as the noise level in the test data increases. This study proposes a short‐time Fourier transform‐based long short‐term memory (STFT‐LSTM) model to improve estimation performance in the presence of noise and ensure estimation robustness. This model enables robust estimations in the presence of noise by positioning an STFT layer before feeding the data into the LSTM layer. The output transformed into the time‐frequency domain by the STFT layer is learned by the LSTM model. The robustness of the proposed model was validated using a numerical model with three degrees of freedom at various signal‐to‐noise ratio levels, and its robustness against impulse and periodic noise was verified. Experimental validation assessed the estimation robustness under impact load and verified the robustness against environmental noise in the acquired acceleration response.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"35 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The cover image is based on the Article A generative adversarial network approach for removing motion blur in the automatic detection of pavement cracks by Yu Zhang and Lin Zhang, https://doi.org/10.1111/mice.13231. Image Credit: Lin Zhang.
封面图像基于文章《路面裂缝自动检测中去除运动模糊的生成式对抗网络方法》，作者：Yu Zhang 和 Lin Zhang，https://doi.org/10.1111/mice.13231。图片来源：Lin Zhang。
{"title":"Cover Image, Volume 39, Issue 22","authors":"","doi":"10.1111/mice.13368","DOIUrl":"10.1111/mice.13368","url":null,"abstract":"<p><b>The cover image</b> is based on the Article <i>A generative adversarial network approach for removing motion blur in the automatic detection of pavement cracks</i> by Yu Zhang and Lin Zhang, https://doi.org/10.1111/mice.13231. Image Credit: Lin Zhang.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 22","pages":""},"PeriodicalIF":8.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13368","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142574492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To address the inaccurate classification of coarse aggregate particle size due to insufficient height information in single-view, a multi-view and graph convolutional network (GCN) based method for coarse aggregate particle size classification was proposed in this study. First, the viewpoint selection and projection strategies were designed to build the aggregate multi-view datasets. Then, the surface texture of the aggregate was reconstructed by using 3D point cloud information. Finally, self-attention mechanism and three-layer GCN were introduced to aggregate global shape feature descriptors. The experimental results show that the proposed interleaved self-attention and view GCN model achieves a coarse aggregate particle size classification accuracy of 94.11%, outperforming other multi-view classification algorithms. This method provides a new possibility for the accurate detection of aggregate particle size and provides significant support for the production and automatic detection of aggregate raw materials.
{"title":"A coarse aggregate particle size classification method by fusing 3D multi-view and graph convolutional networks","authors":"Aojia Tian, Wei Li, Ming Yang, Jiangang Ding, Lili Pei, Yuhan Weng","doi":"10.1111/mice.13369","DOIUrl":"https://doi.org/10.1111/mice.13369","url":null,"abstract":"To address the inaccurate classification of coarse aggregate particle size due to insufficient height information in single-view, a multi-view and graph convolutional network (GCN) based method for coarse aggregate particle size classification was proposed in this study. First, the viewpoint selection and projection strategies were designed to build the aggregate multi-view datasets. Then, the surface texture of the aggregate was reconstructed by using 3D point cloud information. Finally, self-attention mechanism and three-layer GCN were introduced to aggregate global shape feature descriptors. The experimental results show that the proposed interleaved self-attention and view GCN model achieves a coarse aggregate particle size classification accuracy of 94.11%, outperforming other multi-view classification algorithms. This method provides a new possibility for the accurate detection of aggregate particle size and provides significant support for the production and automatic detection of aggregate raw materials.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"213 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In railway transportation, the evaluation of track irregularities is an indispensable requirement to ensure the safety and comfort of railway vehicles. A promising approach is to directly use vehicle dynamic responses to assess the impact of track irregularities. However, the computational cost of obtaining the dynamic response of the vehicle body using dynamics simulation methods is large. To this end, this study proposes a physics‐informed neural operator framework for vehicle–track spatially coupled dynamics (PINO‐VTSCD) calculation, which can effectively acquire the vehicle dynamic response. The backbone structure of PINO‐VTSCD is established by the branched Fourier neural operator, which features one branch for outputting car body responses and the other branch for estimating the responses of bogie frames, wheelsets, and rails. The relative L2 loss (rLSE) of PINO‐VTSCD under the optimal hyperparameter combination is 4.96%, which is 57% lower than the convolutional neural network‐gated recurrent unit model. Evaluation cases from large‐scale simulations and real‐world track irregularities show that the proposed framework can achieve fast solution in scenarios such as different wavelength‐depth combinations and different wavelength ranges. Compared with the traditional vehicle–track coupled model, the speedup of the PINO‐VTSCD model is 32x. The improved computational efficiency of the proposed model can support many railway engineering tasks that require repetitive calculations.
{"title":"A branched Fourier neural operator for efficient calculation of vehicle–track spatially coupled dynamics","authors":"Qingjing Wang, Huakun Sun, Qing He, Peihai Li, Yu Sun, Weijun Wu, Guanren Lyu, Ping Wang","doi":"10.1111/mice.13367","DOIUrl":"https://doi.org/10.1111/mice.13367","url":null,"abstract":"In railway transportation, the evaluation of track irregularities is an indispensable requirement to ensure the safety and comfort of railway vehicles. A promising approach is to directly use vehicle dynamic responses to assess the impact of track irregularities. However, the computational cost of obtaining the dynamic response of the vehicle body using dynamics simulation methods is large. To this end, this study proposes a physics‐informed neural operator framework for vehicle–track spatially coupled dynamics (PINO‐VTSCD) calculation, which can effectively acquire the vehicle dynamic response. The backbone structure of PINO‐VTSCD is established by the branched Fourier neural operator, which features one branch for outputting car body responses and the other branch for estimating the responses of bogie frames, wheelsets, and rails. The relative L2 loss (<jats:italic>rLSE</jats:italic>) of PINO‐VTSCD under the optimal hyperparameter combination is 4.96%, which is 57% lower than the convolutional neural network‐gated recurrent unit model. Evaluation cases from large‐scale simulations and real‐world track irregularities show that the proposed framework can achieve fast solution in scenarios such as different wavelength‐depth combinations and different wavelength ranges. Compared with the traditional vehicle–track coupled model, the speedup of the PINO‐VTSCD model is 32x. The improved computational efficiency of the proposed model can support many railway engineering tasks that require repetitive calculations.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"7 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142566141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predicting the performance of composite materials is crucial for their application in civil infrastructure, yet limited experimental data often hinder the development of accurate and generalizable models. This study introduces a deep neural network (DNN) approach that combines summarizing statistics (SS) and transfer learning (TL)—termed the SSTL‐DNN approach—to address data scarcity in modeling composite materials. The computational novelty lies in the SS method's ability to extract comprehensive information from limited datasets by converting complex constitutive laws into concise statistical representations, thereby enabling efficient and effective model training. Simultaneously, the TL method enhances computational efficiency by leveraging knowledge from related tasks with abundant data to improve learning in the target task with scarce data. This combination not only reduces dependency on large datasets but also significantly improves model generalization. The proposed SSTL‐DNN approach is validated through two case studies: fiber‐reinforced polymer confined concrete and engineered cementitious composites. In both case studies, the SSTL‐DNN model reduces the required dataset size by up to 75% and decreases the validation error by 39%, compared to traditional deep learning models. These results demonstrate that the SSTL‐DNN approach not only overcomes data scarcity but also provides accurate predictions and generalization to unseen data, offering a practical solution for modeling composite materials with limited data.
{"title":"Combining transfer learning and statistical measures to predict performance of composite materials with limited data","authors":"Xue Li, Zhongfeng Zhu, Yingwu Zhou, Zhihao Zhou, Liwen Zhang, Cheng Chen","doi":"10.1111/mice.13363","DOIUrl":"https://doi.org/10.1111/mice.13363","url":null,"abstract":"Predicting the performance of composite materials is crucial for their application in civil infrastructure, yet limited experimental data often hinder the development of accurate and generalizable models. This study introduces a deep neural network (DNN) approach that combines summarizing statistics (SS) and transfer learning (TL)—termed the SSTL‐DNN approach—to address data scarcity in modeling composite materials. The computational novelty lies in the SS method's ability to extract comprehensive information from limited datasets by converting complex constitutive laws into concise statistical representations, thereby enabling efficient and effective model training. Simultaneously, the TL method enhances computational efficiency by leveraging knowledge from related tasks with abundant data to improve learning in the target task with scarce data. This combination not only reduces dependency on large datasets but also significantly improves model generalization. The proposed SSTL‐DNN approach is validated through two case studies: fiber‐reinforced polymer confined concrete and engineered cementitious composites. In both case studies, the SSTL‐DNN model reduces the required dataset size by up to 75% and decreases the validation error by 39%, compared to traditional deep learning models. These results demonstrate that the SSTL‐DNN approach not only overcomes data scarcity but also provides accurate predictions and generalization to unseen data, offering a practical solution for modeling composite materials with limited data.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"116 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a distributed adaptive signal control and advisory speed coordination method based on shockwave theory, which accommodates diverse traffic conditions. In order to assess signal control efficiency under various scenarios, an innovative evaluation index termed synthetic delay is introduced based on the analysis of traffic dynamics at intersections. Considering the formation and dissipation of queue, and flow fluctuation of incoming traffic, it automatically evaluates control delay and throughput with distinctive significances. The distributed adaptive control method calculates the optimal green time in real time to minimize total synthetic delay at intersections. Furthermore, the coordination of advisory speed with the signal control schemes is addressed to ensure smooth progressions for vehicles. The proposed method considers the saturation of traffic and upstream traffic flow changes, leading to adaptability to changing traffic scenarios and effective coordination of traffic control. Several simulations were conducted and compared with the proposed method with other control methods. The results demonstrate that the proposed methods reduce the control delay and increase intersection throughput remarkably under different traffic saturations, confirming their effectiveness.
{"title":"Coordination of distributed adaptive signal control and advisory speed optimization based on shockwave theory","authors":"Ning Xie, Changyin Dong, Hao Wang","doi":"10.1111/mice.13364","DOIUrl":"https://doi.org/10.1111/mice.13364","url":null,"abstract":"This paper presents a distributed adaptive signal control and advisory speed coordination method based on shockwave theory, which accommodates diverse traffic conditions. In order to assess signal control efficiency under various scenarios, an innovative evaluation index termed synthetic delay is introduced based on the analysis of traffic dynamics at intersections. Considering the formation and dissipation of queue, and flow fluctuation of incoming traffic, it automatically evaluates control delay and throughput with distinctive significances. The distributed adaptive control method calculates the optimal green time in real time to minimize total synthetic delay at intersections. Furthermore, the coordination of advisory speed with the signal control schemes is addressed to ensure smooth progressions for vehicles. The proposed method considers the saturation of traffic and upstream traffic flow changes, leading to adaptability to changing traffic scenarios and effective coordination of traffic control. Several simulations were conducted and compared with the proposed method with other control methods. The results demonstrate that the proposed methods reduce the control delay and increase intersection throughput remarkably under different traffic saturations, confirming their effectiveness.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"30 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142490222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study introduces an asynchronous decentralized coordinated signal control (ADCSC) framework for multi‐agent traffic signal control in the urban road network. The controller at each intersection in the network optimizes its signal control decisions based on a prediction of the future traffic demand as an independent agent. The asynchronous framework decouples the entangled interdependence between decision‐making and state prediction among different agents in decentralized coordinated decision‐making problems, enabling agents to proceed with collaborative decision‐making without waiting for other agents’ decisions. Within the proposed ADCSC framework, each controller dynamically optimizes its signal timing strategy with a unique rolling horizon scheme. The scheme's individualized parameters for each controller are determined based on the vehicle travel time between the adjacent intersections, ensuring that controllers can make informed control decisions with accurate arrival flow information from upstream intersections. The signal optimization problem is formulated as a mixed integer linear program model, which adopts a flexible signal scheme without a fixed phase structure and sequence. Simulation results demonstrate that the proposed ADCSC strategy significantly outperforms the benchmark signal coordination methods in terms of average delay, travel speed, stop numbers, and energy consumption. Experimental analysis on computation time validates the applicability of the proposed optimization model for real‐time implementation. Sensitivity analysis on key parameters in the framework is conducted, offering insights for parameter selection in practice. Furthermore, the ADCSC framework is extended to a road network in Qinzhou City, China, with 45 signalized intersections, demonstrating its effectiveness and scalability in the real‐world road network.
{"title":"Asynchronous decentralized traffic signal coordinated control in urban road network","authors":"Jichen Zhu, Chengyuan Ma, Yuqi Shi, Yanqing Yang, Yuzheng Guo, Xiaoguang Yang","doi":"10.1111/mice.13362","DOIUrl":"https://doi.org/10.1111/mice.13362","url":null,"abstract":"This study introduces an asynchronous decentralized coordinated signal control (ADCSC) framework for multi‐agent traffic signal control in the urban road network. The controller at each intersection in the network optimizes its signal control decisions based on a prediction of the future traffic demand as an independent agent. The asynchronous framework decouples the entangled interdependence between decision‐making and state prediction among different agents in decentralized coordinated decision‐making problems, enabling agents to proceed with collaborative decision‐making without waiting for other agents’ decisions. Within the proposed ADCSC framework, each controller dynamically optimizes its signal timing strategy with a unique rolling horizon scheme. The scheme's individualized parameters for each controller are determined based on the vehicle travel time between the adjacent intersections, ensuring that controllers can make informed control decisions with accurate arrival flow information from upstream intersections. The signal optimization problem is formulated as a mixed integer linear program model, which adopts a flexible signal scheme without a fixed phase structure and sequence. Simulation results demonstrate that the proposed ADCSC strategy significantly outperforms the benchmark signal coordination methods in terms of average delay, travel speed, stop numbers, and energy consumption. Experimental analysis on computation time validates the applicability of the proposed optimization model for real‐time implementation. Sensitivity analysis on key parameters in the framework is conducted, offering insights for parameter selection in practice. Furthermore, the ADCSC framework is extended to a road network in Qinzhou City, China, with 45 signalized intersections, demonstrating its effectiveness and scalability in the real‐world road network.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"34 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In vibration testing of civil engineering structures, the first two vibration modes are crucial in representing the global dynamic behavior of the structure measured. In the present study, a comprehensive method is proposed to identify the first two vibration modes of wind turbine towers, which is based on the analysis of fractional order statistical moments (FSM). This study offers novel contributions in two key aspects: (1) theoretical derivations of the relationship between FSM and vibration mode; and (2) successful use of 32/7-order displacement statistical moment <span data-altimg="/cms/asset/c88d0200-4120-4dd6-a987-0b203283f98b/mice13361-math-0001.png"></span><mjx-container ctxtmenu_counter="162" ctxtmenu_oldtabindex="1" jax="CHTML" role="application" sre-explorer- style="font-size: 103%; position: relative;" tabindex="0"><mjx-math aria-hidden="true" location="graphic/mice13361-math-0001.png"><mjx-semantics><mjx-mrow data-semantic-children="8" data-semantic-content="0,9" data-semantic- data-semantic-role="leftright" data-semantic-speech="left parenthesis upper M Subscript d Superscript 32 divided by 7 Baseline right parenthesis" data-semantic-type="fenced"><mjx-mo data-semantic- data-semantic-operator="fenced" data-semantic-parent="10" data-semantic-role="open" data-semantic-type="fence" style="margin-left: 0.056em; margin-right: 0.056em;"><mjx-c></mjx-c></mjx-mo><mjx-msubsup data-semantic-children="1,2,6" data-semantic-collapsed="(8 (7 1 2) 6)" data-semantic- data-semantic-parent="10" data-semantic-role="latinletter" data-semantic-type="subsup"><mjx-mi data-semantic-annotation="clearspeak:simple" data-semantic-font="italic" data-semantic- data-semantic-parent="8" data-semantic-role="latinletter" data-semantic-type="identifier"><mjx-c></mjx-c></mjx-mi><mjx-script style="vertical-align: -0.317em; margin-left: -0.081em;"><mjx-mrow data-semantic-children="3,5" data-semantic-content="4" data-semantic- data-semantic-parent="8" data-semantic-role="division" data-semantic-type="infixop" size="s" style="margin-left: 0.191em;"><mjx-mn data-semantic-annotation="clearspeak:simple" data-semantic-font="normal" data-semantic- data-semantic-parent="6" data-semantic-role="integer" data-semantic-type="number"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mn><mjx-mo data-semantic- data-semantic-operator="infixop,/" data-semantic-parent="6" data-semantic-role="division" data-semantic-type="operator" rspace="1" space="1"><mjx-c></mjx-c></mjx-mo><mjx-mn data-semantic-annotation="clearspeak:simple" data-semantic-font="normal" data-semantic- data-semantic-parent="6" data-semantic-role="integer" data-semantic-type="number"><mjx-c></mjx-c></mjx-mn></mjx-mrow><mjx-spacer style="margin-top: 0.18em;"></mjx-spacer><mjx-mi data-semantic-annotation="clearspeak:simple" data-semantic-font="italic" data-semantic- data-semantic-parent="8" data-semantic-role="latinletter" data-semantic-type="identifier" size="s"><mjx-c></mjx-c></mjx-mi></mjx-script></mjx-msubsup><mjx-mo data-semantic-
{"title":"Modal identification of wind turbine tower based on optimal fractional order statistical moments","authors":"Yang Yang, Zhewei Wang, Shuai Tao, Qingshan Yang, Hwa Kian Chai","doi":"10.1111/mice.13361","DOIUrl":"https://doi.org/10.1111/mice.13361","url":null,"abstract":"In vibration testing of civil engineering structures, the first two vibration modes are crucial in representing the global dynamic behavior of the structure measured. In the present study, a comprehensive method is proposed to identify the first two vibration modes of wind turbine towers, which is based on the analysis of fractional order statistical moments (FSM). This study offers novel contributions in two key aspects: (1) theoretical derivations of the relationship between FSM and vibration mode; and (2) successful use of 32/7-order displacement statistical moment <span data-altimg=\"/cms/asset/c88d0200-4120-4dd6-a987-0b203283f98b/mice13361-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"162\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/mice13361-math-0001.png\"><mjx-semantics><mjx-mrow data-semantic-children=\"8\" data-semantic-content=\"0,9\" data-semantic- data-semantic-role=\"leftright\" data-semantic-speech=\"left parenthesis upper M Subscript d Superscript 32 divided by 7 Baseline right parenthesis\" data-semantic-type=\"fenced\"><mjx-mo data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"10\" data-semantic-role=\"open\" data-semantic-type=\"fence\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"><mjx-c></mjx-c></mjx-mo><mjx-msubsup data-semantic-children=\"1,2,6\" data-semantic-collapsed=\"(8 (7 1 2) 6)\" data-semantic- data-semantic-parent=\"10\" data-semantic-role=\"latinletter\" data-semantic-type=\"subsup\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"8\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-script style=\"vertical-align: -0.317em; margin-left: -0.081em;\"><mjx-mrow data-semantic-children=\"3,5\" data-semantic-content=\"4\" data-semantic- data-semantic-parent=\"8\" data-semantic-role=\"division\" data-semantic-type=\"infixop\" size=\"s\" style=\"margin-left: 0.191em;\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"6\" data-semantic-role=\"integer\" data-semantic-type=\"number\"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mn><mjx-mo data-semantic- data-semantic-operator=\"infixop,/\" data-semantic-parent=\"6\" data-semantic-role=\"division\" data-semantic-type=\"operator\" rspace=\"1\" space=\"1\"><mjx-c></mjx-c></mjx-mo><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"6\" data-semantic-role=\"integer\" data-semantic-type=\"number\"><mjx-c></mjx-c></mjx-mn></mjx-mrow><mjx-spacer style=\"margin-top: 0.18em;\"></mjx-spacer><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"8\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\" size=\"s\"><mjx-c></mjx-c></mjx-mi></mjx-script></mjx-msubsup><mjx-mo data-semantic- ","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"3 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The cover image is based on the Article A hybrid method for intercity transport mode identification based on mobility features and sequential relations mined from cellular signaling data by Fan Ding et al., https://doi.org/10.1111/mice.13229.
封面图像基于 Fan Ding 等人撰写的文章《基于移动性特征和蜂窝信令数据顺序关系的城际交通模式识别混合方法》，https://doi.org/10.1111/mice.13229。
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