Investigating the influencing parameters with automated scour severity detection using Bayesian neural networks

IF 1.7 3区工程技术 Q3 ENGINEERING, CIVIL

Civil Engineering and Environmental Systems Pub Date : 2024-09-02 DOI:10.1080/10286608.2024.2397637

S. T. Vijaya Sarada, Gummadi Venkata Rao

引用次数: 0

Abstract

This research proposes a novel approach leveraging deep learning techniques to enhance scour severity prediction that aims to analyse various parameters’ effects on scour severity and scour pattern...

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利用贝叶斯神经网络研究冲刷严重程度自动检测的影响参数

本研究提出了一种利用深度学习技术加强冲刷严重程度预测的新方法，旨在分析各种参数对冲刷严重程度和冲刷模式的影响。

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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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