随机森林的元启发性优化,用于预测FRP增强混凝土梁的冲击切割强度
Peixi Yang1, Chuanqi Li2, Yingui Qiu1
1School of Resources and Safety Engineering, Central South University, Changsha 410083, China.
Materials (Basel, Switzerland)
|June 10, 2023
概括
使用元启发式算法优化机器学习模型,可以准确预测纤维增强聚合物增强混凝土 (FRP-RC) 梁的穿孔切削强度 (PSS). 狮子优化器-随机森林模型表现出卓越的性能,突出了板块有效深度作为一个关键的预测因素.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
背景情况:
- 在纤维增强聚合物增强混凝土 (FRP-RC) 梁的穿孔切削强度 (PSS) 预测对于结构安全至关重要.
- 现有的模型在预测复杂的FRP-RC梁设计的PSS中可能缺乏准确性.
研究的目的:
- 开发和优化机器学习模型,用于FRP-RC光束中准确的PSS预测.
- 评估元启发式算法在预测模型的超参数调整中的有效性.
主要方法:
- 使用狮优化器 (ALO),火焰优化器 (MFO) 和Salp Swarm算法 (SSA) 来进行超参数优化.
- 采用随机森林 (RF) 作为基础机器学习模型.
- 输入特征包括柱子截面类型,柱子截面面积,板材有效深度,跨度深度比,混凝土压力强度,钢筋承压强度和钢筋强度比.
主要成果:
- ALO-RF模型实现了最高的预测准确性,R2值为0.9820 (训练) 和0.941 (测试).
- 低有效深度 (SED) 被确定为PSS预测中最有影响力的特征.
- 优化的混合模型在准确性和错误控制方面明显优于传统方法.
结论:
- 超启发式优化机器学习模型为预测FRP-RC光束中的PSS提供了强大的方法.
- 薄弱的有效深度是控制穿孔切削强度的关键设计参数.
- 本研究提供了一个验证的框架,以加强FRP-RC元素的结构评估.
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