Computer-Aided Civil and Infrastructure Engineering -

Sujet:	Early View

Articles dans ce numéro

Auteur(s)	Titre	Page(s)
Tang, Zhixian / Wang, Ruoheng / Chung, Edward / Gu, Weihua / Zhu, Hong	An adversarial diverse deep ensemble approach for surrogate‐based traffic signal optimization
Wu, Meng / Li, Miaomiao / You, Zhanping	Asphalt property prediction through high‐throughput molecular dynamics simulation
Zhu, Jichen / Ma, Chengyuan / Shi, Yuqi / Yang, Yanqing / Guo, Yuzheng / Yang, Xiaoguang	Asynchronous decentralized traffic signal coordinated control in urban road network
Ma, Duo / Wang, Niannian / Fang, Hongyuan / Chen, Weiwei / Li, Bin / Zhai, Kejie	Attention‐optimized 3D segmentation and reconstruction system for sewer pipelines employing multi‐view images
Farhadi, Sasan / Corrado, Mauro / Ventura, Giulio	Automated acoustic event‐based monitoring of prestressing tendons breakage in concrete bridges
Tavasoli, Sina / Poorghasem, Sina / Pan, Xiao / Yang, T. Y. / Bao, Y.	Autonomous post‐disaster indoor navigation and survivor detection using low‐cost micro aerial vehicles
Liu, Yanyue / Zhang, Zhao / Mo, Lei / Yu, Bin / Li, Zhenhua	A bi‐level emergency evacuation traffic optimization model for urban evacuation problem
Pan, Xiao / Yang, T. Y.	Bolt loosening assessment using ensemble vision models for automatic localization and feature extraction with target‐free perspective adaptation
Wang, Qingjing / Sun, Huakun / He, Qing / Li, Peihai / Sun, Yu / Wu, Weijun / Lyu, Guanren / Wang, Ping	A branched Fourier neural operator for efficient calculation of vehicle–track spatially coupled dynamics
Yang, Yang / Xu, Wenming / Gao, Anguo / Yang, Qingshan / Gao, Yuqing	Bridge damage identification based on synchronous statistical moment theory of vehicle–bridge interaction
Cronin, Liam / Sen, Debarshi / Marasco, Giulia / Matarazzo, Thomas / Pakzad, Shamim	Bridge monitoring using mobile sensing data with traditional system identification techniques
Shafighfard, Torkan / Kazemi, Farzin / Bagherzadeh, Faramarz / Mieloszyk, Magdalena / Yoo, Doo‐Yeol	Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
Du, Jianting / Yuen, Ka‐Veng / Whittle, Andrew J. / Hu, Liming / Divoux, Thibaut / Meegoda, Jay N.	Characterization of mechanical properties of shale constituent minerals using phase‐identified nanoindentation
Tian, Aojia / Li, Wei / Yang, Ming / Ding, Jiangang / Pei, Lili / Weng, Yuhan	A coarse aggregate particle size classification method by fusing 3D multi‐view and graph convolutional networks
Li, Xue / Zhu, Zhongfeng / Zhou, Yingwu / Zhou, Zhihao / Zhang, Liwen / Chen, Cheng	Combining transfer learning and statistical measures to predict performance of composite materials with limited data
Cheng, YuYao / Jia, Siqi / Zhang, Jianliang / Zhang, Jian	A computer vision–aided methodology for bridge flexibility identification from ambient vibrations
Mao, Jiannan / Huang, Hao / Gu, Yu / Lu, Weike / Tang, Tianli / Ding, Fan	A convergent cross‐mapping approach for unveiling congestion spatial causality in urban traffic networks
Wu, Difei / Zhong, Sheng / Leong, Man Io / Li, Yishun / Tian, Boyuan / Liu, Chenglong / Du, Yuchuan	A cooperative methodology for multi‐roller automation in pavement construction considering trajectory planning and collaborative operation
Xie, Ning / Dong, Changyin / Wang, Hao	Coordination of distributed adaptive signal control and advisory speed optimization based on shockwave theory
Pereira, Mauricio / D'Altri, Antonio Maria / de Miranda, Stefano / Glisic, Branko	Crack pattern–based machine learning prediction of residual drift capacity in damaged masonry walls
Maeda, Keisuke / Ogawa, Naoki / Ogawa, Takahiro / Haseyama, Miki	Damage‐level classification considering both correlation between image and text data and confidence of attention map
Harati, Mojtaba / van de Lindt, John W.	Data‐driven machine learning for multi‐hazard fragility surfaces in seismic resilience analysis
Shabbir, Khurram / Noureldin, Mohamed / Sim, Sung‐Han	Data‐driven model for seismic assessment, design, and retrofit of structures using explainable artificial intelligence
Chen, De / Li, Yukun / Tao, Jiaxing / Li, Yuchen / Zhang, Shilong / Shan, Xuehui / Wang, Tingting / Qiao, Zhi / Zhao, Rui / Fan, Xiaoqiang / Zhou, Zhongrong	Deep learning‐based segmentation model for permeable concrete meso‐structures
Zhou, Xiaohang / Li, Qixuan / Cui, Ranting / Zhu, Xuan	Deep neural network based time–frequency decomposition for structural seismic responses training with synthetic samples
Tang, Xueyang / Wang, Yi / Cai, Xiaopei / Yang, Fei / Hou, Yue	Diagnosis of high‐speed railway ballastless track arching based on unsupervised learning framework
Cavanni, V. / Ceravolo, R. / Miraglia, G.	A domain adaptation methodology for enhancing the classification of structural condition states in continuously monitored historical domes
Wu, Kai / Matsuoka, Masashi / Oshio, Haruki	Earthquake damage detection and level classification method for wooden houses based on convolutional neural networks and onsite photos
Ren, Tianyu / Jebelli, Houtan	Efficient 3D robotic mapping and navigation method in complex construction environments
Jia, Dongge / Brigham, John C. / Fascetti, Alessandro	An efficient static solver for the lattice discrete particle model
Qiu, Jiandong / Fu, Sicheng / Ou, Jushang / Tang, Kai / Qu, Xinming / Liang, Shixiao / Wang, Xin / Ran, Bin	Estimating link flow through link speed with sparse flow data sampling
Liang, Minfei / Di Luzio, Giovanni / Schlangen, Erik / Savija, Branko	Experimentally informed modeling of the early‐age stress evolution in cementitious materials using exponential conversion from creep to relaxation
Berangi, Mohammadjavad / Lontra, Bernardo Mota / Anupam, Kumar / Erkens, Sandra / van Vliet, Dave / Snippe, Almar / Moenielal, Mahesh	Gradient boosting decision trees to study laboratory and field performance in pavement management
Wu, Xin / Zheng, Dongjian / Chen, Xingqiao / Liu, Yongtao / Qiu, Jianchun / Jiang, Haifeng	Gravity dam displacement monitoring using in situ strain and deep learning
Zhang, Chenyu / Wen, Weiping / Zhai, Changhai / Wei, Yuqiu / Ruan, Penghao	Hidden structural information reconstruction and seismic response analysis of high‐rise residential shear wall buildings with limited structural data
Liu, Zhihuan / Yang, Zaichun / Ren, Tingting / Wang, Zhenzhen / Deng, JinSheng / Deng, Chenxi / Zhao, Hongmin / Zhou, Guoxiong / Chen, Aibin / Li, Liujun	A hierarchical progressive recognition network for building change detection in high‐resolution remote sensing images
Ding, Jiawei / Lu, Dagang / Cao, Zhenggang	A hybrid non‐parametric ground motion model of power spectral density based on machine learning
Li, Hong‐Wei / Hao, Shuo / Ni, Yi‐Qing / Wang, You‐Wu / Xu, Zhao‐Dong	Hybrid structural analysis integrating physical model and continuous‐time state‐space neural network model
Ho, Yu‐Hsuan / Li, Longxiang / Mostafavi, Ali	Integrated vision language and foundation model for automated estimation of building lowest floor elevation
Gu, Yi / Huang, Yuli / Liao, Wenjie / Lu, Xinzheng	Intelligent design of shear wall layout based on diffusion models
Zhu, Yantao / Niu, Xinqiang / Tian, Jinzhang	A machine vision‐based intelligent segmentation method for dam underwater cracks using swarm optimization algorithm and deep learning
Xu, Zekun / Shen, Jiaxu / Wu, Huayong / Chen, Jun	Mainshock–aftershock sequence simulation via latent space encoding of generative adversarial networks
Yang, Yang / Wang, Zhewei / Tao, Shuai / Yang, Qingshan / Chai, Hwa Kian	Modal identification of wind turbine tower based on optimal fractional order statistical moments
Fan, Xudong / Hackl, Jürgen	Modeling of spatially embedded networks via regional spatial graph convolutional networks
Tong, Liang‐yu / Liu, Qing‐feng / Xiong, Qingxiang / Meng, Zhaozheng / Amiri, Ouali / Zhang, Mingzhong	Modeling the chloride transport in concrete from microstructure generation to chloride diffusivity prediction
Wang, Pengfei / Liu, Caiwei / Wang, Xinyu / Tian, Libin / Miao, Jijun / Liu, YanChun	Multicategory fire damage detection of post‐fire reinforced concrete structural components
Taghizadeh, Mehdi / Nabian, Mohammad Amin / Alemazkoor, Negin	Multifidelity graph neural networks for efficient and accurate mesh‐based partial differential equations surrogate modeling
Yao, Kefu / Wen, Zhiping / Shao, Chenfei / Yang, Jiaquan / Su, Huaizhi	A multisource data‐driven monitoring model for assessing concrete dam behavior
Li, Muduo / Zhu, Xiaohong / Zhang, Yuying / Tsang, Daniel C. W.	A multi‐phase mechanical model of biochar–cement composites at the mesoscale
Gu, D. L. / Shuai, Q. W. / Zhang, N. / Jin, N. / Zheng, Z. X. / Xu, Z. / Xu, Y. J.	Multi‐view street view image fusion for city‐scale assessment of wind damage to building clusters
Zhang, Pei‐Fu / Zhang, Daxu / Zhao, Xiao‐Ling / Zhao, Xuan / Iqbal, Mudassir / Tuerxunmaimaiti, Yiliyaer / Zhao, Qi	Natural language processing‐based deep transfer learning model across diverse tabular datasets for bond strength prediction of composite bars in concrete
Iannuzzo, A. / Musone, V. / Ruocco, E.	A neural network‐based automated methodology to identify the crack causes in masonry structures
Yun, Da Yo / Seon Park, Hyo	Noise‐robust structural response estimation method using short‐time Fourier transform and long short‐term memory
Gong, Yunhai / Zhong, Shaopeng / Zhao, Shengchuan / Xiao, Feng / Wang, Wenwen / Jiang, Yu	Optimizing green splits in high‐dimensional traffic signal control with trust region Bayesian optimization
Mostofi, Fatemeh / Tokdemir, Onur Behzat / Bahadır, Ümit / Toğan, Vedat	Performance‐driven contractor recommendation system using a weighted activity–contractor network
Orinaite, Ugne / Burdzik, Rafal / Ranjan, Vinayak / Ragulskis, Minvydas	Prediction of approaching trains based on H‐ranks of track vibration signals
Gong, Weibing / Zuo, Linlong / Li, Lin / Wang, Hui	Prediction of stratified ground consolidation via a physics‐informed neural network utilizing short‐term excess pore water pressure monitoring data
Li, Kuangying / Atik, Asya / Zheng, Dayang / Hajibabai, Leila / Hajbabaie, Ali	A relaxation‐based Voronoi diagram approach for equitable resource distribution
Chu, Honghu / Yu, Diran / Chen, Weiwei / Ma, Jun / Deng, Lu	A rendering‐based lightweight network for segmentation of high‐resolution crack images
Wang, Yang / Xiao, Hong / Zhang, Zhihai / Guo, Xiaoxuan / Liu, Qiang	Self‐supervised representation learning of metro interior noise based on variational autoencoder and deep embedding clustering
Zhang, Yuxiao / Shi, Jin / Tan, Shehui / Wang, Yingjie	A smoothness control method for kilometer‐span railway bridges with analysis of track characteristics
Mirzahossein, H. / Najafi, P. / Kalantari, N. / Waller, T.	Solving discrete network design problem using disjunctive constraints
Yuen, Ka‐Veng / Kuok, Sin‐Chi	Telescopic broad Bayesian learning for big data stream
Azimi, M. / Yang, T. Y.	Transformer‐based framework for accurate segmentation of high‐resolution images in structural health monitoring
Bavelos, Angelos Christos / Anastasiou, Efthymios / Dimitropoulos, Nikos / Michalos, George / Makris, Sotiris	Virtual reality‐based dynamic scene recreation and robot teleoperation for hazardous environments
Jiang, Tengjiao / Frøseth, Gunnstein T. / Rønnquist, Anders / Kong, Xuan / Deng, Lu	A visual inspection and diagnosis system for bridge rivets based on a convolutional neural network
Zhang, Chenyu / Li, Ke / Yin, Zhaozheng / Qin, Ruwen	Weakly‐supervised structural component segmentation via scribble annotations

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sur cette fiche
IssueID
28501
Publié(e) le:
28.06.2019
Modifié(e) le:
01.07.2019

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Structurae coopère avec

International Association for Bridge and Structural Engineering (IABSE)

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Computer-Aided Civil and Infrastructure Engineering -

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Computer-Aided Civil and Infrastructure Engineering -

Articles dans ce numéro

Vous avez dépassé votre limite de téléchargement mensuelle ! Les abonné·es à Structurae Plus peuvent télécharger 30 images ou ensembles de données par mois. Les utilisateurs·rices de Structurae Pro sont limités à 50.

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Les abonné·es à Structurae Plus peuvent télécharger 30 images ou ensembles de données par mois.
Les utilisateurs·rices de Structurae Pro sont limités à 50.