计算复杂性及其对机器学习模型预测能力的影响,用于混凝土混合设计
1Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdansk, Poland.
Materials (Basel, Switzerland)
|September 9, 2023
概括
这项研究表明,用于混凝土混合设计的更复杂的机器学习模型更好地预测混凝土的压力强度. 模型复杂度的增加导致了具体属性预测的准确性提高.
科学领域:
- 混凝土技术 混凝土技术
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 传统的混凝土混合设计方法难以满足现代对强度,环保和效率的要求.
- 传统的方法往往导致过度工程和难以准确预测混凝土属性.
- 机器学习 (ML) 为预测混凝土混合物设计中的压力强度提供了一个有希望的替代方案.
研究的目的:
- 研究ML模型的计算复杂性与它们在预测混凝土压力强度方面的准确性之间的关系.
- 评估用于混凝土混合设计的不同复杂度的深度神经网络模型的性能.
主要方法:
- 评估了五种具有不同计算复杂性的深度神经网络模型.
- 模型经过训练和测试,使用大量混凝土混合设计数据库和相应的破坏性测试结果进行了测试.
- 使用像确定系数 (R2),平均平方误差和根平均平方误差这样的指标来评估性能.
主要成果:
- 在增加模型计算复杂性和预测准确性之间观察到正相关性.
- 复杂度更高的模型显示R2增加,错误指标减少 (MSE,RMSE等). ) 的情况.
- 这表明,更复杂的ML模型可以提高混凝土压力强度的预测.
结论:
- 机器学习模型中的计算复杂度增加对它们准确预测混凝土压力强度的能力产生了积极的影响.
- 这些发现支持改进人工智能驱动的方法,以实现更高效,更精确的混凝土混合设计.
- 未来的研究可以基于这些见解来进一步优化具体技术中的ML应用.
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