使用神经电磁场优化估计混凝土的压力
Mohammad Reza Akbarzadeh1, Hossein Ghafourian2, Arsalan Anvari3
1Department of Civil Engineering, Sharif University of Technology, Tehran 1136511155, Iran.
Materials (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了一种通过电磁场优化 (EFO) 优化的人工神经网络 (ANN),用于预测混凝土压力强度 (CCS). 与可靠的CCS估计的其他优化方法相比,ANN-EFO模型表现出卓越的准确性和速度.
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:
- 混凝土的压力强度 (CCS) 是混凝土结构的关键机械性质.
- 准确预测CCS对于质量控制和结构完整性至关重要.
- 现有的预测方法可能缺乏效率或准确性.
研究的目的:
- 开发一种新且高效的方法来预测混凝土的压力强度 (CCS).
- 使用电磁场优化 (EFO) 为CCS预测优化一个人工神经网络 (ANN).
- 将EFO的性能与其他优化算法 (WCA,SCA,CFOA) 的性能进行比较.
主要方法:
- 一个人工神经网络 (ANN) 模型被开发用于CCS预测.
- 通过使用电磁场优化 (EFO) 算法来优化ANN.
- EFO的性能与水循环算法 (WCA),正弦共弦算法 (SCA) 和鱼优化算法 (CFOA) 相比进行了基准测试.
- 作为输入,主要的混凝土参数 (水泥,渣,飞灰,水,超级可塑剂,聚合物,年龄) 被使用.
主要成果:
- 使用EFO (ANN-EFO) 优化的ANN在CCS预测中实现了最低的平均绝对误差 (5.6236).
- 与ANN-WCA (5.8363),ANN-CFOA (7.6538) 和ANN-SCA (7.8248) 相比,ANN-EFO的预测准确度更高.
- EFO算法比其他评估的优化策略快得多.
结论:
- 混合ANN-EFO模型是一种高效和可靠的方法,用于早期预测混凝土的压力强度.
- ANN-EFO提供了一个易于使用,易于解释和明确的预测公式,用于方便的CCS估计.
- 该研究建议ANN-EFO用于需要准确和快速CCS评估的实际应用.
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