封面图片,第 39 卷第 21 期

IF 8.5 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-10-22 DOI:10.1111/mice.13360
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引用次数: 0

摘要

封面图像基于李哲等人的文章《沥青路面裂缝长度和宽度的自动化量化》,https://doi.org/10.1111/mice.13344。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Cover Image, Volume 39, Issue 21

The cover image is based on the Article Automated quantification of crack length and width in asphalt pavements by Zhe Li et al., https://doi.org/10.1111/mice.13344.

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来源期刊
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.
期刊最新文献
Automated seismic event detection considering faulty data interference using deep learning and Bayesian fusion Smartphone-based high durable strain sensor with sub-pixel-level accuracy and adjustable camera position Reinforcement learning-based approach for urban road project scheduling considering alternative closure types Issue Information Cover Image, Volume 39, Issue 23
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