基于机器学习的压力能量吸收在不同特征的鞋底的预测
Mohammad Mahdi Mohammadi1, Amir Nourani2
1Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran.
施加的力是影响鞋底拉伸能量的主要因素,几何形状和硬度也发挥着作用. 机器学习模型,特别是随机森林,可以预测这种能量,以优化鞋子设计和预防伤害.
科学领域:
- 生物力学和材料科学 生物力学和材料科学
- 运动工程和鞋类技术
背景情况:
- 了解鞋底机制对于性能和伤害预防至关重要.
- 鞋底设计显著影响体育活动期间的能量吸收和消耗.
研究的目的:
- 在各种负载条件下量化鞋底的拉伸能量.
- 确定影响单一性能的关键设计特征.
- 通过机器学习开发用于单一应变能量的预测模型.
主要方法:
- 在各种鞋底设计上进行了537次力控制压缩试验.
- 分析的因素包括中底/外底结构,几何,硬度,施加力,角度和加载速度.
- 应用统计分析 (相关性,ANOVA) 和机器学习算法 (SLR,SVR,RF,MLP) 进行预测.
主要成果:
- 应变能量与施加力 (r=0.89) 强烈相关,其次是几何和硬度 (r≈0.25).
- 辅助性中底结构提供了与减轻重量的简单中底相比较的应变能量.
- 最初的接触位置 (跟与脚) 影响了应变能量吸收.
结论:
- 施加的力是鞋底拉伸能量的主要因素.
- 机器学习模型,特别是随机森林模型,可以准确地预测应变能量.
- 使用这些洞察力的优化鞋底设计可以提高性能并降低受伤风险.
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