Computer-Aided Civil and Infrastructure Engineering最新文献_第2页

Uncertainty-guided U-Net for soil boundary segmentation using Monte Carlo dropout

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-12-08 DOI: 10.1111/mice.13396

X. Zhou, B. Sheil, S. Suryasentana, P. Shi

{"title":"Uncertainty-guided U-Net for soil boundary segmentation using Monte Carlo dropout","authors":"X. Zhou, B. Sheil, S. Suryasentana, P. Shi","doi":"10.1111/mice.13396","DOIUrl":"https://doi.org/10.1111/mice.13396","url":null,"abstract":"Accurate soil stratification is essential for geotechnical engineering design. Owing to its effectiveness and efficiency, the cone penetration test (CPT) has been widely applied for subsurface stratigraphy, which relies heavily on empiricism for correlations to soil type. Recently, deep learning techniques have shown great promise in learning the relationship between CPT data and soil boundaries automatically. However, the segmentation of soil boundaries is fraught with model and measurement uncertainty. This paper introduces an uncertainty-guided U((-Net (UGU-Net) for improved soil boundary segmentation. The UGU-Net consists of three parts: (a) a Bayesian U-Net to predict a pixel-level uncertainty map, (b) reinforcement of original labels on the basis of the predicted uncertainty map, and (c) a traditional deterministic U-Net, which is applied to the reinforced labels for final soil boundary segmentation. The results show that the proposed UGU-Net outperforms the existing methods in terms of both high accuracy and low uncertainty. A sensitivity study is also conducted to explore the influence of key model parameters on model performance. The proposed method is validated by comparing the predicted subsurface profile with benchmark profiles. The code for this project is available at github.com/Xiaoqi-Zhou-suda/UGU-Net.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"52 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793160","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

Computational modeling of reinforced concrete dapped-end beams

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-12-03 DOI: 10.1111/mice.13390

Danilo D'Angela, Gennaro Magliulo, Chiara Di Salvatore, Edoardo Cosenza

引用次数: 0

Cover Image, Volume 39, Issue 24

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-12-03 DOI: 10.1111/mice.13389

引用次数: 0

Cover Image, Volume 39, Issue 24

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-12-03 DOI: 10.1111/mice.13388

引用次数: 0

Coupled lattice discrete particle model for the simulation of water and chloride transport in cracked concrete members

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-11-30 DOI: 10.1111/mice.13385

Yingbo Zhu, Dongge Jia, John C. Brigham, Alessandro Fascetti

{"title":"Coupled lattice discrete particle model for the simulation of water and chloride transport in cracked concrete members","authors":"Yingbo Zhu, Dongge Jia, John C. Brigham, Alessandro Fascetti","doi":"10.1111/mice.13385","DOIUrl":"https://doi.org/10.1111/mice.13385","url":null,"abstract":"A novel coupled mechanical and mass transport lattice discrete particle model is developed to quantitatively assess the impact of cracks on the mass transport properties in concrete members subjected to short‐ and long‐term loading conditions. In the developed approach, two sets of dual lattice networks are generated: one to resolve the mechanical response and another for mass transport analysis. The cracks simulated by the mechanical lattice are mapped onto the transport elements to investigate the effect of cracks on the global transport properties in concrete members. A new quantitative relationship is proposed for the estimation of the diffusion coefficient based on local crack information, and the developed model is capable of describing both convection and diffusion mechanisms. Moreover, creep behavior is incorporated to account for the influence of cracks induced by long‐term loading conditions. Numerical results, in the form of dynamic changes in cumulative water and chloride contents in concrete members under tension, compression, and bending with various stress levels show remarkable accuracy when compared to available experimental observations. The developed model provides an effective means for incorporating mesoscale information in simulations of water and chloride transport in concrete members under varying short‐ and long‐term loading conditions.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"3 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756118","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

Aeroelastic force prediction via temporal fusion transformers

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

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 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-11-26 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":"https://doi.org/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":"8 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","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 11.775 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":"https://doi.org/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":"19 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","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":"<p>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.</p>","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