Computer-Aided Civil and Infrastructure Engineering最新文献_第7页

Asphalt property prediction through high‐throughput molecular dynamics simulation 通过高通量分子动力学模拟预测沥青特性

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-08-17 DOI: 10.1111/mice.13325

Meng Wu, Miaomiao Li, Zhanping You

引用次数: 0

A multi-phase mechanical model of biochar–cement composites at the mesoscale 中尺度生物炭-水泥复合材料的多相力学模型

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-08-16 DOI: 10.1111/mice.13307

Muduo Li, Xiaohong Zhu, Yuying Zhang, Daniel C. W. Tsang

{"title":"A multi-phase mechanical model of biochar–cement composites at the mesoscale","authors":"Muduo Li, Xiaohong Zhu, Yuying Zhang, Daniel C. W. Tsang","doi":"10.1111/mice.13307","DOIUrl":"10.1111/mice.13307","url":null,"abstract":"<p>This study presents a five-phase mesoscale modeling framework specifically developed to investigate crack propagation and mechanical properties of biochar–cement composites. The multi-phase model includes porous biochar particles with precise geometric construction, sand aggregates, cement matrix, and interfacial transition zone adjunct to both the biochar particles and sand aggregates. The 3D porous biochar library was first proposed and established in this study, which could provide an external interface for describing different pore shapes, wall thicknesses, and pore areas. All the simulation results were experimentally validated using a digital image correlation. Through precise geometric modeling, the unique failure modes and timing of biochar particles within the mortar were identified. This is analogous to the “strong column–weak beam” concept, accounting for the enhanced ductility observed in the biochar–cement composites under compression test. This work can advance the geometric modeling of porous aggregates broadly and elucidate their mesoscopic failure mechanisms in cementitious materials, thus providing new insights for developing high-ductility and lightweight cement composites.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 23","pages":"3552-3572"},"PeriodicalIF":8.5,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13307","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141994463","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

Announcing the 2023 Hojjat Adeli Award for Innovation in Computing 宣布 2023 年 "霍贾特-阿德利计算机创新奖

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-08-05 DOI: 10.1111/mice.13316

Gillian Greenough

引用次数: 0

Cover Image, Volume 39, Issue 16 封面图片，第 39 卷第 16 期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-08-05 DOI: 10.1111/mice.13318

引用次数: 0

Cover Image, Volume 39, Issue 16 封面图片，第 39 卷第 16 期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-08-05 DOI: 10.1111/mice.13317

引用次数: 0

Self-training with Bayesian neural networks and spatial priors for unsupervised domain adaptation in crack segmentation 利用贝叶斯神经网络和空间先验进行自我训练，实现裂缝分割中的无监督域适应

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-29 DOI: 10.1111/mice.13315

Pang-jo Chun, Toshiya Kikuta

{"title":"Self-training with Bayesian neural networks and spatial priors for unsupervised domain adaptation in crack segmentation","authors":"Pang-jo Chun, Toshiya Kikuta","doi":"10.1111/mice.13315","DOIUrl":"10.1111/mice.13315","url":null,"abstract":"<p>This study proposes a novel self-training framework for unsupervised domain adaptation in the segmentation of concrete wall cracks using accumulated crack data. The proposed method incorporates Bayesian neural networks for uncertainty estimation of pseudo-labels, and spatial priors of cracks for screening noisy labels. Experiments demonstrate that the proposed approach achieves significant improvements in F1 score. Comparing the F1 scores, Bayesian DeepLabv3+ and Bayesian U-Net showed performance improvements of 0.0588 and 0.1501, respectively, after domain adaptation. Furthermore, the integration of Stable Diffusion for few-shot image generation enhances domain adaptation performance by 0.0332. The proposed framework enables high-precision crack segmentation with as few as 100 target images, which can be easily obtained at the site, reducing the cost of model deployment in infrastructure maintenance. The study also investigates the optimal number of iterations for domain adaptation based on the uncertainty score, providing insights for practical implementation. The proposed method contributes to the development of efficient and automated structural health monitoring using AI.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 17","pages":"2642-2661"},"PeriodicalIF":8.5,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141794753","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

Integrated vision language and foundation model for automated estimation of building lowest floor elevation 用于自动估算建筑物最低层标高的综合视觉语言和基础模型

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-26 DOI: 10.1111/mice.13310

Yu‐Hsuan Ho, Longxiang Li, Ali Mostafavi

{"title":"Integrated vision language and foundation model for automated estimation of building lowest floor elevation","authors":"Yu‐Hsuan Ho, Longxiang Li, Ali Mostafavi","doi":"10.1111/mice.13310","DOIUrl":"https://doi.org/10.1111/mice.13310","url":null,"abstract":"Street view imagery has emerged as a valuable resource for urban analytics research. Recent studies have explored its potential for estimating lowest floor elevation (LFE), offering a scalable alternative to traditional on‐site measurements, crucial for assessing properties' flood risk and damage extent. While existing methods rely on object detection, the introduction of image segmentation has expanded the utility of street view images for LFE estimation, although challenges still remain in segmentation quality and capability to distinguish front doors from other doors. To address these challenges in LFE estimation, this study integrates the Segment Anything model, a segmentation foundation model, with vision language models (VLMs) to conduct text‐prompt image segmentation on street view images for LFE estimation. By evaluating various VLMs, integration methods, and text prompts, the most suitable model was identified for street view image analytics and LFE estimation tasks, thereby improving the coverage of the current LFE estimation model based on image segmentation from 33% to 56% of properties. Remarkably, our proposed method, ELEV‐VISION‐SAM, significantly enhances the availability of LFE estimation to almost all properties in which the front door is visible in the street view image. In addition, the findings present the first baseline and quantified comparison of various vision models for street view image‐based LFE estimation. The model and findings not only contribute to advancing street view image segmentation for urban analytics but also provide a novel approach for image segmentation tasks for other civil engineering and infrastructure analytics tasks.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"109 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141768576","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 domain adaptation methodology for enhancing the classification of structural condition states in continuously monitored historical domes 增强连续监测历史穹顶结构状态分类的领域适应方法

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-26 DOI: 10.1111/mice.13313

V. Cavanni, R. Ceravolo, G. Miraglia

引用次数: 0

Multifidelity graph neural networks for efficient and accurate mesh‐based partial differential equations surrogate modeling 多保真度图神经网络用于高效、精确的基于网格的偏微分方程代理建模

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-26 DOI: 10.1111/mice.13312

Mehdi Taghizadeh, Mohammad Amin Nabian, Negin Alemazkoor

{"title":"Multifidelity graph neural networks for efficient and accurate mesh‐based partial differential equations surrogate modeling","authors":"Mehdi Taghizadeh, Mohammad Amin Nabian, Negin Alemazkoor","doi":"10.1111/mice.13312","DOIUrl":"https://doi.org/10.1111/mice.13312","url":null,"abstract":"Accurately predicting the dynamics of complex systems governed by partial differential equations (PDEs) is crucial in various applications. Traditional numerical methods such as finite element methods (FEMs) offer precision but are resource‐intensive, particularly at high mesh resolutions. Machine learning–based surrogate models, including graph neural networks (GNNs), present viable alternatives by reducing computation times. However, their accuracy is significantly contingent on the availability of substantial high‐fidelity training data. This paper presents innovative multifidelity GNN (MFGNN) frameworks that efficiently combine low‐fidelity and high‐fidelity data to train more accurate surrogate models for mesh‐based PDE simulations, while reducing training computational cost. The proposed methods capitalize on the strengths of GNNs to manage complex geometries across different fidelity levels. Incorporating a hierarchical learning strategy and curriculum learning techniques, the proposed models significantly reduce computational demands and improve the robustness and generalizability of the results. Extensive validations across various simulation tasks show that the MFGNN frameworks surpass traditional single‐fidelity GNN models. The proposed approaches, hence, provide a scalable and practical solution for conducting detailed computational analyses where traditional high‐fidelity simulations are time‐consuming.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"302 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141768571","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

Telescopic broad Bayesian learning for big data stream 针对大数据流的远景广义贝叶斯学习

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-24 DOI: 10.1111/mice.13305

Ka‐Veng Yuen, Sin‐Chi Kuok

引用次数: 0