基于PSO-KNN的基岩纤维钢筋混凝土构成模型的预测
Meng Zhu1, Jiajian Lin1, Guangyong Cao2,3
1School of Electrical Engineering and Automation, Anhui University, Hefei 230601, PR China.
Heliyon
|December 13, 2024
概括
机器学习,特别是粒子群优化K-近邻 (PSO-KNN) 算法,提供了一种简化方法来研究玄武纤维增强混凝土 (BFRC) 的机械性能. 这种方法准确地预测BFRC应力-应变曲线,减少实验成本和时间.
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:
- 研究基岩纤维增强混凝土 (BFRC) 的机械性能传统上涉及耗时和昂贵的实验程序.
- 开发有效的方法来预测BFRC在各种压力条件下的行为对于工程应用至关重要.
研究的目的:
- 引入一种基于机器学习的简化方法来研究玄武岩纤维对混凝土机械性能的影响.
- 使用实验数据建立和评估BFRC行为的预测模型.
主要方法:
- 制造具有不同纤维组成的BFRC标本.
- 使用MTS816岩石测试系统在不同应力状态下对BFRC标本进行压缩测试.
- 使用极端梯度提升,支持向量机,K-近邻和粒子集群优化K-近邻 (PSO-KNN) 算法开发BFRC预测模型.
主要成果:
- 一个实验数据集生成了纤维体积分数,纤维长度,环压和应变作为输入,压力作为输出.
- 与其他经过测试的机器学习模型相比,PSO-KNN算法展示了优越的预测性能.
- 该PSO-KNN模型成功预测了BFRC的应力-应变适配曲线,使参数贡献分析成为可能.
结论:
- PSO-KNN算法提供了一种有效和高效的方法来预测BFRC的机械性质.
- 这种机器学习方法显著减少了与传统实验研究相关的时间和费用.
- 该研究为工程应用和未来关于BFRC的研究提供了宝贵的参考资料.
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