封面图片，第 39 卷第 16 期

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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

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引用次数: 0

摘要

封面图像基于 Samuel Olamide Aregbesola 和 Yong-Hoon Byun 的研究文章《通过机器学习自动评估基于信号的动态锥体阻力，用于地下表征》，https://doi.org/10.1111/mice.13294。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Cover Image, Volume 39, Issue 16

查看原文本刊更多论文

Cover Image, Volume 39, Issue 16

The cover image is based on the Research Article Automated signal-based evaluation of dynamic cone resistance via machine learning for subsurface characterization by Samuel Olamide Aregbesola and Yong-Hoon Byun, https://doi.org/10.1111/mice.13294.

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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.