基于IWOA-LSTM的钢纤维混凝土的内在结构识别
Ping Li1, Jie Feng1, Shiwei Duan2
1School of Management Science and Engineering, Anhui University of Technology, Maanshan, 243032, China.
Scientific reports
|July 17, 2025
概括
这项研究引入了一种优化了改进鱼算法的长期短期记忆神经网络,以模拟钢纤维混凝土的高温损伤. 该模型准确地预测了材料的行为和高温下损坏的演变.
科学领域:
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
- 土木工程 土木工程是指土木工程.
背景情况:
- 钢纤维混凝土 (SFRC) 中的断裂损伤涉及合变形和损伤过程.
- 准确的构成模型对于理解高温下SFRC的行为至关重要.
- 现有的优化算法面临着缓慢的融合和局部优化的挑战.
研究的目的:
- 为SFRC开发一个高温构成型识别模型,使用一个优化了长短期记忆 (LSTM) 神经网络的改进鱼算法 (IWOA).
- 通过修改鱼优化算法 (WOA) 来增强优化能力.
- 准确预测SFRC在高温下的损伤演变和构成性行为.
主要方法:
- 改进的鱼算法 (IWOA) 是通过结合拉普拉斯交叉,最佳邻里扰动,自适应加权和可变螺旋位置策略来开发的.
- IWOA与WOA,CPO,BOA和GWO进行了基准测试,证明了优越的优化性能.
- 使用实验数据训练了一个LSTM模型,并将其性能与WOA-LSTM和IWOA-LSTM模型进行比较.
主要成果:
- 与WOA-LSTM和LSTM模型相比,IWOA-LSTM模型在平均平方误差 (MSE) 中分别提高了47.66%和65.60%.
- 该模型成功地在200°C,400°C和520°C的温度下在SFRC中脱损伤和塑料应变.
- IWOA-LSTM模型展示了高学习精度和良好的概括能力,与实验数据非常接近.
结论:
- 开发的IWOA-LSTM模型为识别SFRC的高温组成模型提供了有效的工具,考虑到损害演变.
- 该模型准确地预测了SFRC在各种升高温度下的损伤及其演变规律.
- 这些发现支持该模型在结构工程中用于高温场景的应用.
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