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钢筋混凝土梁行为的比较分析:传统模型与传统模型对比. 人工神经网络预测预测
Muhammad Mahtab Ahmad1, Ayub Elahi1, Salim Barbhuiya2
1Civil Engineering Department, University of Engineering and Technology Taxila, Taxila 47050, Pakistan.
Materials (Basel, Switzerland)
|December 23, 2023
概括
人工神经网络 (ANN) 准确预测钢筋混凝土梁的强度,与传统模型相匹配. 这种基于知识的方法为没有杆的梁提供了精确的结构分析.
科学领域:
- 结构工程 结构工程
- 计算智能是一种计算智能.
- 材料科学 材料科学 材料科学
背景情况:
- 钢筋混凝土梁是关键的结构元素.
- 当前的设计规范 (ACI) 提供了常规的分析方法.
- 预测钢筋混凝土梁的行为,特别是没有杆的,带来了挑战.
研究的目的:
- 将人工神经网络 (ANN) 的预测准确度与传统的ACI代码模型进行比较,用于没有杆的钢筋混凝土梁.
- 使用ANN开发基于知识的结构分析模型.
- 评估ANN在预测初裂负荷,屈曲强度和剪切强度方面的性能.
主要方法:
- 收集了110个没有杆的钢筋混凝土梁的数据集.
- 一个多层反向传播神经网络使用 MATLAB 进行了训练.
- 将ANN预测与传统ACI建筑代码模型的结果进行了比较.
主要成果:
- 该ANN模型实现了高的确定系数:0.9404的第一个裂变负荷,0.9756的屈曲强度,和0.9787的剪切强度.
- ANN模型的结果与传统模型紧密结合.
- 线性回归分析证实了ANN预测与实验/传统结果之间的强烈相关性.
结论:
- 人工神经网络在准确预测钢筋混凝土梁的结构性行为方面具有显著的潜力.
- 该ANN模型为结构分析提供了可靠和精确的替代方案,补充了传统的设计代码.
- 这项研究建立了一个强大的基于知识的模型,用于增强结构分析和设计.
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