一篇关于土木工程中人工智能的做与不做的观点文章,并描绘了从数据驱动分析到因果知识发现的路径

IF 1.7 3区 工程技术 Q3 ENGINEERING, CIVIL
M. Z. Naser, Brandon Ross
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引用次数: 4

摘要

人工智能(AI)已经成为解决科学和工程问题的通用语言。尽管大数据的兴起,人工智能在平行领域的成功,以及在这一前沿领域发表的令人兴奋的作品,但土木工程界的一些人将人工智能与神秘的耻辱联系在一起。然而,也有越来越多的人不愿意完全接受人工智能。人工智能之所以神秘,是因为:(1)传统的土木工程课程中通常不会教授人工智能,(2)大多数土木工程师仍然是人工智能的应用者(而不是创造者),以及(3)通常采用的人工智能算法利用黑箱方法——与土木工程领域普遍接受的方法相反。我们写这篇评论文章的目的是全面了解将人工智能应用于土木工程的做法和禁忌。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An opinion piece on the dos and don’ts of artificial intelligence in civil engineering and charting a path from data-driven analysis to causal knowledge discovery
ABSTRACT Artificial intelligence (AI) has been established as a universal language for solving science and engineering problems. Despite the rise of big data, the success of AI in parallel fields, and exciting works published at this frontier, some in the civil engineering community tie AI to a mystique stigma. And yet, there is also ever-growing inertia to embrace AI fully. The mystique of AI arises because (1) AI is not typically taught in a traditional civil engineering curriculum, (2) the majority of civil engineers remain appliers (as opposed to creators) of AI, and (3) commonly adopted AI algorithms leverage blackbox methods – the opposite to that commonly accepted in the civil engineering domain. We write this opinion piece with the aim of presenting a holistic look into the dos and don’ts of adopting AI into civil engineering.
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来源期刊
Civil Engineering and Environmental Systems
Civil Engineering and Environmental Systems 工程技术-工程:土木
CiteScore
3.30
自引率
16.70%
发文量
10
审稿时长
>12 weeks
期刊介绍: Civil Engineering and Environmental Systems is devoted to the advancement of systems thinking and systems techniques throughout systems engineering, environmental engineering decision-making, and engineering management. We do this by publishing the practical applications and developments of "hard" and "soft" systems techniques and thinking. Submissions that allow for better analysis of civil engineering and environmental systems might look at: -Civil Engineering optimization -Risk assessment in engineering -Civil engineering decision analysis -System identification in engineering -Civil engineering numerical simulation -Uncertainty modelling in engineering -Qualitative modelling of complex engineering systems
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