预测填充了 SFRC 增强混凝土的钢管柱的火灾诱发结构性能:使用人工神经网络方法

Christo George, Edwin Zumba, Maria Alexandra Procel Silva, S. S. Selvan, Mary Subaja Christo, Rakesh Kumar, Atul Kumar Singh, Sathvik S., Kennedy Onyelowe
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引用次数: 0

摘要

预测混凝土填充钢管(CFST)柱的轴向缩短强度是本研究试图为土木工程项目解决的一个重要问题。考虑到钢管与核心混凝土之间错综复杂的关系,我们建议使用基于深度学习的人工神经网络(ANN)模型来解决这一问题。该模型被称为 ANN-SFRC(钢纤维增强混凝土),其 R2 临界值超过了 0.90,并在不同类型的 CFST 柱中取得了令人印象深刻的 R2 值。与传统的线性回归方法相比,ANN-SFRC 模型显著提高了准确性,与实际值相比,观察到的误差小于 3%。这种高性能仪器采用可靠的方法预测 CFST 柱在轴向压缩下的行为,提高了土木工程设计和规划阶段的安全性和准确性。
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Predicting the fire-induced structural performance of steel tube columns filled with SFRC-enhanced concrete: using artificial neural networks approach
Predicting the axial Shortening strength of concrete-filled steel tubular (CFST) columns is an important problem that this study attempts to solve for civil engineering projects. We suggest using a deep learning-based artificial neural network (ANN) model to address this issue, taking into account the intricate relationship between steel tube and core concrete. The model, called ANN-SFRC (Steel Fibre Reinforced Concrete), surpasses an R2 threshold of 0.90 and achieves impressive R2 values across different types of CFST columns. Compared to traditional linear regression methods, the ANN-SFRC model significantly improves accuracy, with an observed inaccuracy of less than 3% compared to actual values. With its reliable approach to forecasting the behavior of CFST columns under axial compression, this high-performance instrument enhances safety and accuracy during the design and planning stages of civil engineering.
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