人工神经网络的性能分析及其对回收水泥砂压力预测的优化模型的性能分析
Lin-Bin Li1, Guang-Ji Yin1, Jing-Jing Shao1
1Department of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China.
Materials (Basel, Switzerland)
|December 31, 2025
概括
这项研究使用了用灰狼优化器 (GWO),粒子群优化 (PSO) 和遗传算法 (GA) 优化的人工神经网络 (ANN) 来预测回收水泥砂 (RCM) 的强度. PSO-ANN模型实现了最佳性能,为可持续建筑提供了高效的混合设计.
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
- 计算智能是一种计算智能.
背景情况:
- 回收水泥砂 (RCM) 对于可持续建筑至关重要.
- 机械性能是RCM工程应用的关键.
- 预测模型是RCM混合设计所需的.
研究的目的:
- 开发和比较由GWO,PSO和GA优化的人工神经网络 (ANN) 模型来预测RCM压力强度.
- 分析数据库大小对模型性能的影响.
- 确定影响RCM压力强度的关键因素.
主要方法:
- 开发了一个人工神经网络 (ANN) 模型.
- 使用智能优化算法 (GWO,PSO,GA) 来优化ANN.
- 创建了一个包含八个输入变量 (W/C,C/S,FA,ARR,治愈年龄) 的综合数据库.
- 用不同的数据库大小评估模型性能并进行比较.
主要成果:
- 智能优化显著改善了ANN预测性能.
- 在PSO-ANN模型显示最佳结果 (R2 = 0.92,MSE = 0.007,MAE = 0.0632).
- 通过SHAP分析,水-水泥比率 (W/C),水泥-砂比率 (C/S) 和固化年龄被确定为关键影响因素.
结论:
- 优化的ANN模型,特别是PSO-ANN,为RCM混合设计提供了一种高效的数据驱动方法.
- 该研究为RCM的工程应用提供了理论支持.
- 数据集大小对计算时间的影响最小,这主要取决于算法复杂性.
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