Computer-Aided Civil and Infrastructure Engineering最新文献_第10页

Aeroelastic force prediction via temporal fusion transformers 基于时间融合变压器的气动弹性力预测

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-11-28 DOI: 10.1111/mice.13381

Miguel Cid Montoya, Ashutosh Mishra, Sumit Verma, Omar A. Mures, Carlos E. Rubio‐Medrano

{"title":"Aeroelastic force prediction via temporal fusion transformers","authors":"Miguel Cid Montoya, Ashutosh Mishra, Sumit Verma, Omar A. Mures, Carlos E. Rubio‐Medrano","doi":"10.1111/mice.13381","DOIUrl":"https://doi.org/10.1111/mice.13381","url":null,"abstract":"Aero‐structural shape design and optimization of bridge decks rely on accurately estimating their self‐excited aeroelastic forces within the design domain. The inherent nonlinear features of bluff body aerodynamics and the high cost of wind tunnel tests and computational fluid dynamics (CFD) simulations make their emulation as a function of deck shape and reduced velocity challenging. State‐of‐the‐art methods address deck shape tailoring by interpolating discrete values of integrated flutter derivatives (FDs) in the frequency domain. Nevertheless, more sophisticated strategies can improve surrogate accuracy and potentially reduce the required number of samples. We propose a time domain emulation strategy harnessing temporal fusion transformers (TFTs) to predict the self‐excited forces time series before their integration into FDs. Emulating aeroelastic forces in the time domain permits the inclusion of time‐series amplitudes, frequencies, phases, and other properties in the training process, enabling a more solid learning strategy that is independent of the self‐excited forces modeling order and the inherent loss of information during the identification of FDs. TFTs' long‐ and short‐term context awareness, combined with their interpretability and enhanced ability to deal with static and time‐dependent covariates, make them an ideal choice for predicting unseen aeroelastic forces time series. The proposed TFT‐based metamodel offers a powerful technique for drastically improving the accuracy and versatility of wind‐resistant design optimization frameworks.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"12 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142753007","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}

引用次数: 0

A bridge point cloud databank for digital bridge understanding 用于数字桥梁理解的桥梁点云数据库

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-11-25 DOI: 10.1111/mice.13384

Hongwei Zhang, Yanjie Zhu, Wen Xiong, C. S. Cai

{"title":"A bridge point cloud databank for digital bridge understanding","authors":"Hongwei Zhang, Yanjie Zhu, Wen Xiong, C. S. Cai","doi":"10.1111/mice.13384","DOIUrl":"10.1111/mice.13384","url":null,"abstract":"Despite progress in automated bridge point cloud segmentation based on deep learning, challenges persist. For instance, the absence of a public point cloud dataset specifically designed for bridge instances, and the existing bridge point cloud datasets display a lack of diversity in bridge types and inconsistency in component labeling. These factors may hinder the further improvement of accuracy in bridge point cloud segmentation. In this paper, a universal multi-type bridge point cloud databank, named BrPCD, consisting of a total of 98 point cloud data (PCD; 10 of them are obtained from scanning, and the rest is obtained by data augmentation) from small to long-span bridges, is established. Additionally, a method for augmenting bridge PCD is proposed, significantly enriching the spatial feature information of bridges within the dataset. Furthermore, based on the introduced data annotation rules, a uniform categorization of semantic labels for bridge components is implemented, enhancing the applicability of our dataset across various semantic segmentation tasks for different types of bridges. A benchmark testing was conducted on the BrPCD using the PointNet model. The segmentation results indicate that the parameters learned through the BrPCD enable accurate segmentation at the level of various types of bridge components. In other words, the BrPCD can function as a universal dataset, applicable for testing various networks aimed at bridge point cloud segmentation.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 9","pages":"1253-1271"},"PeriodicalIF":8.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13384","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696505","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}

引用次数: 0

Machine learning-aided prediction of windstorm-induced vibration responses of long-span suspension bridges 机器学习辅助预测大跨度悬索桥的风灾诱发振动响应

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-11-25 DOI: 10.1111/mice.13387

Alireza Entezami, Hassan Sarmadi

{"title":"Machine learning-aided prediction of windstorm-induced vibration responses of long-span suspension bridges","authors":"Alireza Entezami, Hassan Sarmadi","doi":"10.1111/mice.13387","DOIUrl":"10.1111/mice.13387","url":null,"abstract":"Long-span suspension bridges are significantly susceptible to windstorm-induced vibrations, leading to critical challenges of field measurements along with multicollinearity and nonlinearity between wind features and bridge dynamic responses. To address these issues, this article proposes an innovative machine learning-assisted predictive method by integrating a predictor selector developed from regularized neighborhood components analysis and kernel regression modeling through a regularized support vector machine adjusted by Bayesian hyperparameter optimization. The crux of the proposed method lies in advanced machine learning algorithms including metric learning, kernel learning, and hybrid learning integrated in a regularized framework. Utilizing the Hardanger Bridge subjected to different windstorms, the performance of the proposed method is validated and then compared with state-of-the-art regression techniques. Results highlight the effectiveness and practicality of the proposed method with the minimum and maximum R-squared rates of 89% and 98%, respectively. It also surpasses the state-of-the-art regression techniques in predicting bridge dynamics under different windstorms.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 8","pages":"1043-1060"},"PeriodicalIF":8.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13387","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713158","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}

引用次数: 0

Automated seismic event detection considering faulty data interference using deep learning and Bayesian fusion 利用深度学习和贝叶斯融合技术自动检测地震事件（考虑到错误数据干扰

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-11-21 DOI: 10.1111/mice.13377

Zhiyi Tang, Jiaxing Guo, Yinhao Wang, Wei Xu, Yuequan Bao, Jingran He, Youqi Zhang

{"title":"Automated seismic event detection considering faulty data interference using deep learning and Bayesian fusion","authors":"Zhiyi Tang, Jiaxing Guo, Yinhao Wang, Wei Xu, Yuequan Bao, Jingran He, Youqi Zhang","doi":"10.1111/mice.13377","DOIUrl":"https://doi.org/10.1111/mice.13377","url":null,"abstract":"Structural health monitoring (SHM) aims to assess civil infrastructures' performance and ensure safety. Automated detection of in situ events of interest, such as earthquakes, from extensive continuous monitoring data, is important to ensure the timeliness of subsequent data analysis. To overcome the poor timeliness of manual identification and the inconsistency of sensors, this paper proposes an automated seismic event detection procedure with interpretability and robustness. The sensor-wise raw time series is transformed into image data, enhancing the separability of classification while endowing with visual understandability. Vision Transformers (ViTs) and Residual Networks (ResNets) aided by a heat map–based visual interpretation technique are used for image classification. Multitype faulty data that could disturb the seismic event detection are considered in the classification. Then, divergent results from multiple sensors are fused by Bayesian fusion, outputting a consistent seismic detection result. A real-world monitoring data set of four seismic responses of a pair of long-span bridges is used for method validation. At the classification stage, ResNet 34 achieved the best accuracy of over 90% with minimal training cost. After Bayesian fusion, globally consistent and accurate seismic detection results can be obtained using a ResNet or ViT. The proposed approach effectively localizes seismic events within multisource, multifault monitoring data, achieving automated and consistent seismic event detection.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"73 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142678353","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}

引用次数: 0

Smartphone-based high durable strain sensor with sub-pixel-level accuracy and adjustable camera position 基于智能手机的高耐用应变传感器，精度达到亚像素级，摄像头位置可调

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-11-19 DOI: 10.1111/mice.13383

Pengfei Wu, Bo Lu, Huan Li, Weijie Li, Xuefeng Zhao

{"title":"Smartphone-based high durable strain sensor with sub-pixel-level accuracy and adjustable camera position","authors":"Pengfei Wu, Bo Lu, Huan Li, Weijie Li, Xuefeng Zhao","doi":"10.1111/mice.13383","DOIUrl":"10.1111/mice.13383","url":null,"abstract":"Computer vision strain sensors typically require the camera position to be fixed, limiting measurements to surface deformations of structures at pixel-level resolution. Also, sensors have a service term significantly shorter than the designed service term of the structures. This paper presents research on a high durable computer vision sensor, microimage strain sensing (MISS)-Silica, which utilizes a smartphone connected to an endoscope for measurement. It is designed with a range of 0.05 ε, enabling full-stage strain measurement from loading to failure of structures. The sensor does not require the camera to be fixed during measurements, laying the theoretical foundation for embedded computer vision sensors. Measurement accuracy is improved from pixel level to sub-pixel level, with pixel-based measurement errors around 8 µε (standard deviation approximately 7 µε) and sub-pixel calculation errors around 6 µε (standard deviation approximately 5 µε). Sub-pixel calculation has approximately 30% enhancement in measurement accuracy and stability. MISS-Silica features easy data acquisition, high precision, and long service term, offering a promising method for long-term measurement of both surface and internal structures.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 24","pages":"3805-3820"},"PeriodicalIF":8.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673114","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}

引用次数: 0

Reinforcement learning-based approach for urban road project scheduling considering alternative closure types 基于强化学习的城市道路项目调度方法，考虑替代封闭类型

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-11-19 DOI: 10.1111/mice.13365

S. E. Seilabi, M. Saneii, M. Pourgholamali, M. Miralinaghi, S. Labi

{"title":"Reinforcement learning-based approach for urban road project scheduling considering alternative closure types","authors":"S. E. Seilabi, M. Saneii, M. Pourgholamali, M. Miralinaghi, S. Labi","doi":"10.1111/mice.13365","DOIUrl":"10.1111/mice.13365","url":null,"abstract":"Growth in urban population, travel, and motorization continue to cause an increased need for urban projects to expand road capacity. Unfortunately, these projects also cause travel delays, emissions, driver frustration, and other road user adversities. To alleviate these ills, road agencies often face two work zone design choices: close the road fully and re-reroute traffic or implement partial closure. Both options have significant implications for peri-construction road capacity, traveler costs, and the project duration and cost. This study presents a decision-making methodology to facilitate the choice between full road closure and partial closure. The presented decision-making methodology is a bi-level optimization problem: at the upper level, the road agency seeks to optimally schedule road construction work to minimize net vehicle emissions and road construction costs. The lower-level of the problem captures two types of travelers’ route choice behaviors: rational travelers who minimize their travel time and path-loyal travelers who do not change their routes from their pre-construction routes. The bi-level mixed integer nonlinear model is solved using a reinforcement learning-based algorithm (the multi-armed bandit-guided particle swarm optimization [PSO] technique). The computational experiments suggest the superiority of the proposed algorithm, compared to the classic PSO algorithm in terms of solution quality. The numerical results suggest that if the percentage of path-loyal travelers increases, the agency needs to invest more in road project construction to implement under partial closure to avoid a significant increase in vehicle emissions.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 6","pages":"721-740"},"PeriodicalIF":8.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673123","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}

引用次数: 0

Cover Image, Volume 39, Issue 23 封面图片，第 39 卷第 23 期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-11-17 DOI: 10.1111/mice.13380

引用次数: 0

A multi-perspective fusion model for operating speed prediction on highways using knowledge-enhanced graph neural networks 使用知识增强图神经网络的高速公路运行速度预测多视角融合模型

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-11-17 DOI: 10.1111/mice.13382

Jianqiang Gao, Bo Yu, Yuren Chen, Kun Gao, Shan Bao

{"title":"A multi-perspective fusion model for operating speed prediction on highways using knowledge-enhanced graph neural networks","authors":"Jianqiang Gao, Bo Yu, Yuren Chen, Kun Gao, Shan Bao","doi":"10.1111/mice.13382","DOIUrl":"10.1111/mice.13382","url":null,"abstract":"This study proposes a multi-perspective fusion model for operating speed prediction based on knowledge-enhanced graph neural networks, named RoadGNN-S. By utilizing message passing and multi-head self-attention mechanisms, RoadGNN-S can effectively capture the coupling impacts of multi-perspective alignment elements (i.e., two-dimensional design, 2.5-dimensional driving, and three-dimensional spatial perspectives). The results of driving simulation data show that root mean squared error, mean absolute error, mean absolute percentage error, and R-squared values of RoadGNN-S are superior to those of other classic deep learning algorithms. Then, prior knowledge (i.e., highway geometry supply, driver expectations, and vehicle dynamics) is introduced into RoadGNN-S, and the models’ prediction accuracy and transferability are verified by field observation experiments. Compared to the above data-driven models, knowledge-enhanced RoadGNN-S effectively avoids the fundamental errors, improving the R-squared value in predicting passenger cars’ and trucks’ operating speed by 7.9% and 10.7%, respectively. The findings of this study facilitate the intelligent highway geometric design with multi-perspective fusion and knowledge enhancement techniques.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 8","pages":"1004-1027"},"PeriodicalIF":8.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665228","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}

引用次数: 0

Adaptive compensation using long short-term memory networks for improved control performance in real-time hybrid simulation 利用长短期记忆网络进行自适应补偿，提高实时混合模拟的控制性能

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-11-16 DOI: 10.1111/mice.13378

Zhenfeng Lai, Yanhui Liu, Zhipeng Zhai, Jiajun Zhang

{"title":"Adaptive compensation using long short-term memory networks for improved control performance in real-time hybrid simulation","authors":"Zhenfeng Lai, Yanhui Liu, Zhipeng Zhai, Jiajun Zhang","doi":"10.1111/mice.13378","DOIUrl":"10.1111/mice.13378","url":null,"abstract":"Real-time hybrid simulation (RTHS) divides structural systems into numerical and experimental substructures, providing a cost-effective solution for analyzing structural systems, especially those that are large or complex. However, the actuation systems between these substructures inevitably introduce delays, affecting the stability and accuracy of RTHS. To address this issue, this study proposes an adaptive compensation method based on a conditional adaptive time series (CATS) compensator and a long short-term memory (LSTM) network, termed CATS-LSTM. The LSTM model predicts actuator responses for parameter estimation and calculates prediction errors, improving control performance and reducing delays. The effectiveness of the proposed CATS-LSTM method and the accuracy of the LSTM prediction are validated through a series of simulations and experiments. The results indicate that the proposed CATS-LSTM method outperforms both the CATS and phase lead (PL) methods. Compared to the CATS method, the proposed method reduces the maximum delay, root mean square error, and peak error by 3 ms, 3.66%, and 4.78%, respectively, while achieving reductions of 12 ms, 8.4%, and 10.05%, compared to the PL method. Furthermore, the CATS-LSTM method is significantly less sensitive to initial parameter estimates, compared to the CATS method, enhancing robustness and mitigating the effects of inaccurate or varying initial parameter estimates.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 9","pages":"1208-1234"},"PeriodicalIF":8.5,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142642664","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}

引用次数: 0

A dynamic neural network model for the identification of asbestos roofings in hyperspectral images covering a large regional area 在覆盖大面积区域的高光谱图像中识别石棉屋顶的动态神经网络模型

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-11-14 DOI: 10.1111/mice.13376

Donatella Gubiani, Giovanni Sgrazzutti, Massimiliano Basso, Elena Viero, Denis Tavaris, Gian Luca Foresti, Ivan Scagnetto

{"title":"A dynamic neural network model for the identification of asbestos roofings in hyperspectral images covering a large regional area","authors":"Donatella Gubiani, Giovanni Sgrazzutti, Massimiliano Basso, Elena Viero, Denis Tavaris, Gian Luca Foresti, Ivan Scagnetto","doi":"10.1111/mice.13376","DOIUrl":"10.1111/mice.13376","url":null,"abstract":"Asbestos has been used extensively in several applications. Once it is known as a dangerous mineral, its usage has been prohibited and its identification and remediation play a very important role from the health safety point of view. Nowadays, deep learning techniques are used in many applications, especially for image analysis. They can be used to significantly reduce the time and cost of traditional detection methods. In this paper, taking advantage of asbestos spectral signature, a deep neural network is introduced in order to implement a complete methodology to identify asbestos roofings starting from hyperspectral images in a regional context. The novelty of the proposed approach is a dynamic mixing of models with different features, in order to accommodate classifications on widespread areas of both urban and rural territories. Indeed, the dataset used during the experiments described in this paper is a large one, consisting of many wide hyperspectral images with a geometric resolution of 1 m and with 186 bands, covering an entire region of approximately 8,000 <math>\u0000 <semantics>\u0000 <msup>\u0000 <mi>km</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 <annotation>${rm km}^2$</annotation>\u0000 </semantics></math>. This is in contrast to other works in the literature where the analyzed areas are limited in size and uniform for physical features.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 7","pages":"879-894"},"PeriodicalIF":8.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142610563","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}

引用次数: 0