利用递归神经网络和盾构隧道掘进机数据预测地质成分

IF 1.7 3区工程技术 Q3 ENGINEERING, CIVIL

Civil Engineering and Environmental Systems Pub Date : 2023-11-23 DOI:10.1080/10286608.2023.2286248

Mohammad Pourhomayoun, Mehran Mazari, Luis Fisher, Kabir Nagrecha, Tonatiuh Rodriguez-Nikl, Michael Mooney, Ehsan Alavi

引用次数: 0

摘要

隧道掘进机是交通隧道施工中常用的大型开挖工具。在隧道掘进过程中，隧道掘进机产生的数据规模很大，通常是难以预测的水平。

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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Prediction of geological composition using recurrent neural networks and shield tunnel boring machine data

Tunnel Boring Machines (TBMs) are large-scale excavation tools used commonly in transportation tunnel construction. While tunnelling, TBMs generate data at large scales, often at levels difficult t...

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来源期刊

Civil Engineering and Environmental Systems 工程技术-工程：土木

CiteScore

3.30

自引率

16.70%

发文量

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