集成机器学习用于建模浅层基础的承载能力
1Bim School of Technology and Industry, Changchun Institute of Technology, Changchun, 130012, Jilin, China.
Scientific reports
|April 9, 2024
概括
这项研究引入了两种人工神经网络 (ANN) 模型,这些模型使用回溯搜索算法 (BSA) 和平衡优化器 (EO) 进行训练,以预测双层土壤中的足位稳定性. EO-ANN模型在稳定性值预测方面取得了更高的准确性.
科学领域:
- 地质技术工程 地质技术工程
- 土木工程 土木工程是指土木工程.
- 计算力学 计算力学 计算力学
背景情况:
- 在地质工程中,脚下的稳定性分析至关重要.
- 双层土壤质量对传统分析方法构成复杂的挑战.
- 准确预测承载能力对于结构安全至关重要.
研究的目的:
- 开发和评估新型预测工具,以确定双层土壤上的稳定性.
- 采用通过回溯搜索算法 (BSA) 和平衡优化器 (EO) 优化的人工神经网络 (ANN).
- 为了近似地确定脚的稳定性值 (SV) 和承载能力.
主要方法:
- 利用人工神经网络 (ANN) 进行预测建模.
- 在ANN培训中使用回溯搜索算法 (BSA) 和平衡优化器 (EO).
- 根据结算标准 (5厘米门) 进行有限元分析以确定系统稳定性.
主要成果:
- BSA和EO都显著改善了ANN培训,将根平均平方误差 (RMSE) 降低了高达17.46% (EO).
- 根据接收运行特征曲线 (AUROC) 下面的面积来衡量预测准确度,增加到94.3% (BSA) 和94.1% (EO).
- 均衡优化器 (EO) 显示出更高的准确性,而回溯搜索算法 (BSA) 显示出更高的时间效率.
结论:
- 开发的ANN模型,特别是EO-ANN,是分析双层土壤中基底稳定的有效工具.
- 来自EO-ANN模型的明确数学公式提供了稳定性值预测的实用方法.
- 该研究强调了优化算法的潜力,以增强地质工程分析.
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