使用随机搜索合并的神经网络预测混凝土崩的预测分析
Yunwen Zhou1, Zhihai Jiang2, Xizhen Zhu1
1School of Architecture and Engineering, JiangXi Institute of Applied Science and Technology, Nan Chang, 330000, China.
Heliyon
|May 23, 2024
概括
这项研究引入了一种新的模型,使用随机分形搜索 (SFS) 准确预测混凝土条,这是可行性的关键指标. 通过SFS增强的神经网络 (NN-MLP) 为现实世界项目提供了传统方法的成本效益高的替代方案.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算智能是一种计算智能.
- 材料科学 材料科学 材料科学
背景情况:
- 混凝土沉是可行性的关键指标,但传统的测量方法昂贵而复杂.
- 基于元启发的机器学习提供了高效的间接模型,用于近似混凝土崩.
- 评估先进的优化算法对于更新和改进具体的低迷预测模型至关重要.
研究的目的:
- 为了增强一个多层感知神经网络 (NN-MLP) 使用随机碎形搜索 (SFS) 优化算法.
- 根据混合物成分和固化年龄,开发一个具有成本效益和准确的模型来预测混凝土的下降.
- 将SFS增强模型的性能与其他元启发算法 (如EHO和SMA) 进行比较.
主要方法:
- 使用多层感知神经网络 (NN-MLP) 架构.
- 使用随机分数搜索 (SFS) 算法来优化NN-MLP参数.
- 验证SFS-NN-MLP模型的预测准确性与大象群群优化 (EHO) 和泥模算法 (SMA) 增强的NN-MLP模型相比.
主要成果:
- 与EHO-NN-MLP和SMA-NN-MLP相比,SFS-NN-MLP模型在预测混凝土条方面表现出更高的准确性.
- 定量指标显示,SFS-NN-MLP实现了最低的平均平方误差 (5.6526) 和平均绝对误差 (4.6657).
- SFS-NN-MLP模型实现了更高的百分比-皮尔森相关系数 (78.06%),表明了更强的预测关系.
结论:
- 该SFS-NN-MLP模型提供了一个高度准确和可靠的方法,用于近似混凝土沉降.
- 随机分数搜索是一种有效的优化器,用于提高神经网络在具体技术应用中的性能.
- 建议采用SFS-NN-MLP模型,用于在建筑项目中进行实际,成本高效的混凝土沉降估计.
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