一个生物启发的适应性概率IVYPSO算法与适应性策略反向传播神经网络优化预测高性能混凝土强度
Kaifan Zhang1, Xiangyu Li2, Songsong Zhang1
1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
概括
这项研究介绍了AP-IVYPSO-BP,这是一种用于预测高性能混凝土 (HPC) 强度的新型混合模型. 该模型显著提高了复杂混凝土混合设计的预测准确性和可靠性.
科学领域:
- 土木工程
- 材料科学
- 计算智能
背景情况:
- 准确预测高性能混凝土 (HPC) 的强度对于结构完整性和可持续建筑至关重要.
- 在HPC组件中的复杂相互作用挑战了传统的预测模型,导致了不准确.
- 现有的方法难以处理高维度和杂的数据,因此存在过度匹配和局部最佳的风险.
研究的目的:
- 开发一种新的生物灵感混合优化模型,AP-IVYPSO-BP,用于增强HPC强度预测.
- 解决传统模型在高性能计算中捕捉非线性和多因素关系方面的局限性.
- 提高混凝土强度预测模型的准确性,稳定性和通用性.
主要方法:
- 将常春藤算法 (IVYA) 与粒子群优化 (PSO) 和适应性概率策略集成.
- 使用AP-IVYPSO算法优化反向传播神经网络 (BPNN) 的权重和偏差.
- 在1030个HPC混合样本的数据集上进行培训和验证.
主要成果:
- 与传统的BPNN,PSO-BP,GA-BP和IVY-BP模型相比,AP-IVYPSO-BP的性能更为优越.
- 在R2为0.9542,MAE为3.0404和RMSE为3.7991的测试组中实现了高精度.
- 这种模式有效地平衡了全球勘探和当地开采,缓解了融合问题.
结论:
- 拟议的AP-IVYPSO-BP模型为预测HPC压力强度提供了非常准确和可靠的解决方案.
- 这种以生物为灵感的方法为土木工程应用和材料设计提供了实际价值.
- 该模型的增强预测能力有助于更强大,更可持续的建筑实践.
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