基于灰狼优化器-反向传播神经网络的灰-回收砂的压力强度的预测
Jing-Jing Shao1, Lin-Bin Li1, Guang-Ji Yin1
1School of Architecture and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China.
Materials (Basel, Switzerland)
|January 11, 2025
概括
这项研究优化了灰狼优化器-回传播神经网络 (GWO-BPNN) 模型,以预测飞灰回收砂 (FARM) 的压力强度. GWO-BPNN模型显著提高了FARM机械性能评估的预测准确性和效率.
科学领域:
- 材料科学与工程 材料科学与工程
- 土木工程 土木工程是指土木工程.
- 在工程领域的人工智能.
背景情况:
- 在砂中有效利用回收精细聚合物需要评估机械性能.
- 飞灰回收砂 (FARM) 是一种可持续的建筑材料选择.
- 准确预测FARM压力强度对于实际应用至关重要.
研究的目的:
- 设计和选可行的混合比例,以达到低水/水泥比率的FARM.
- 分析混合物设计参数 (水/水泥比,年龄) 对压力强度的影响.
- 开发和验证FARM压力强度的优化预测模型.
主要方法:
- 9个FARM混合比例的实验设计,水/水泥比率较低.
- 选六种混合比例以获得最佳的流动性.
- 开发一个灰狼优化器-反向传播神经网络 (GWO-BPNN) 模型用于强度预测,使用150个数据点进行训练和验证.
主要成果:
- 与传统的BPNN相比,GWO-BPNN模型显示出更高的预测准确性 (R2从0.85增加到0.93) 和计算效率 (MSE从0.018降低到0.015).
- 模型分析表明,水/水泥比率和压力强度之间存在强烈的负相关性.
- 在沙/水泥比率,年龄和压力强度之间观察到强烈的正相关性;飞灰和降水效应不那么明显.
结论:
- 该GWO-BPNN模型有效地预测FARM压力强度,优于标准BPNN.
- 水/水泥比率,沙/水泥比率和年龄是FARM压力强度的关键决定因素.
- 这项研究验证了优化人工智能模型在建筑中评估回收材料性能方面的潜力.
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