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研究一种基于SSA-BP神经网络的强度预测模型,用于泥-水泥稳定土壤
Bei Zhang1, Xingyu Tao2, Han Zhang2
1School of Civil Engineering, Nantong Vocational University, Nantong 226007, China.
这项研究优化了使用SSA-BP神经网络 (Sparrow Search Algorithm-Back Propagation) 的渣泥稳定土壤强度预测. 在土壤工程中,SSA-BP模型显著提高了工业废物回收的准确性.
科学领域:
- 地质技术工程 地质技术工程
- 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:
- 工业废渣粉具有作为水泥添加剂的潜力,可以增强基于水泥的材料.
- 渣泥稳定土壤的强度受到多个因素之间的复杂,非线性相互作用的影响.
- 准确的预测和优化对于在土壤稳定中利用渣粉至关重要.
研究的目的:
- 开发一个准确的预测模型,以测量渣泥稳定土壤的强度.
- 优化使用工业废弃物的渣泥稳定土壤的混合比例.
- 增强废渣粉在环保固化土壤工程中的应用.
主要方法:
- 用砂土和粘土制备水泥稳定土壤样本,含有不同含量的渣粉.
- 不受限制的压力强度测试,以收集样本数据.
- 使用Sparrow搜索算法 (SSA) 开发和优化反向传播 (BP) 神经网络,以提高预测准确性.
主要成果:
- 与传统的BP模型相比,SSA-BP模型显示出更高的预测准确性.
- 使用SSA-BP模型,预测误差减少了53.4%.
- 该模型有效地描述了影响土壤强度的多因素非线性关系.
结论:
- 该SSA-BP模型提供了一个高效和准确的工具,用于预测渣泥稳定土壤强度.
- 这种方法支持工业废物回收和可持续的土壤工程设计.
- 优化使用渣粉提高了水泥稳定土壤的工程性能.
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