封面图片，第 39 卷第 17 期

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-08-17 DOI:10.1111/mice.13328

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引用次数: 0

摘要

封面图像基于 Pang-jo Chun 和 Toshiya Kikuta 的文章《裂缝分割中无监督域适应的贝叶斯神经网络和空间先验的自我训练》（Self-training with Bayesian neural networks and spatial priors for unsupervised domain adaptation in crack segmentation），https://doi.org/10.1111/mice.13315。

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Cover Image, Volume 39, Issue 17

The cover image is based on the Article Self-training with Bayesian neural networks and spatial priors for unsupervised domain adaptation in crack segmentation by Pang-jo Chun and Toshiya Kikuta, https://doi.org/10.1111/mice.13315.

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来源期刊

Computer-Aided Civil and Infrastructure Engineering 工程技术-工程：土木

CiteScore

17.60

自引率

19.80%

发文量

146

审稿时长

1 months

期刊介绍： Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms. Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.