基于改进的虫算法对混凝土进行动态构成性识别,以优化长期短期记忆模型
Ping Li1, Haonan Zhao2, Jiming Gu2
1School of Management Science and Engineering, Anhui University of Technology, Ma'anshan, 243032, China. 20150009@ahut.edu.cn.
Scientific reports
|March 16, 2024
概括
本研究介绍了一种改进的泥甲虫算法 (IDBO),优化了长期短期记忆 (LSTM) 网络,用于准确的混凝土动态主识别. IDBO-LSTM模型有效地识别了混凝土损坏,并预测了材料的行为,比现有方法表现出更高的性能.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 计算力学 计算力学 计算力学
背景情况:
- 准确识别混凝土的动态特性对于结构完整性至关重要.
- 现有用于具体损害识别的方法面临着与收精度和局部最佳的挑战.
- 了解混凝土的损伤演变和风学对于可靠的建模至关重要.
研究的目的:
- 开发一种新型模型,以更高的准确度来确定具体的动态主体.
- 增强虫优化算法 (DBO) 以在复杂的优化任务中获得更好的性能.
- 验证拟议模型在识别具体损坏和预测材料行为的有效性.
主要方法:
- 使用Split Hopkinson压力条 (SHPB) 测试来获得混凝土应力-张力曲线并捕捉损伤演变.
- 通过结合贪镜头成像反向学习,自适应加权因子和PID控制扰动,开发了一种改进的泥甲虫算法 (IDBO).
- 整合了IDBO算法与长短期记忆 (LSTM) 网络,创建了IDBO-LSTM模型,用于动态恒温识别.
主要成果:
- 与DBO,哈里斯优化,灰狼优化和果优化算法相比,IDBO算法展示了优越的优化能力.
- 在没有明确的损害考虑的情况下,IDBO-LSTM模型成功地识别了具体的物质损害.
- 从IDBO-LSTM模型的预测与SHPB测试中的试验曲线密切匹配,当考虑损伤时.
结论:
- 拟议的IDBO-LSTM模型为具体的动态主体识别提供了一种可行和优秀的方法.
- 增强的IDBO算法克服了原始DBO的局限性,从而实现更准确的识别.
- 这项研究提供了一个强大的计算工具,用于分析动态加载条件下的混凝土材料行为.
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