使用混合极端梯度增强模型评估土壤的弹性模量
1Imperial College London, London, UK. xiangfeng.duan@outlook.com.
Scientific reports
|December 31, 2024
概括
一个新的BKA-XGBOOST模型准确地预测了路面设计的土壤弹性模量 (MR). 这种高效的方法比现有技术提供了更高的准确性,有助于路面工程.
科学领域:
- 地质技术工程 地质技术工程
- 铺路工程工程 铺路工程
- 机器学习应用 机器学习应用
背景情况:
- 准确的土壤弹性模量 (MR) 估计对于路面设计和监测至关重要.
- 实验,回归和构成模型等现有方法在时间,成本,适用性和准确性方面都有局限性.
- 目前用于MR预测的机器学习模型需要进一步提高预测准确性.
研究的目的:
- 为预测土壤弹性模块 (MR) 提出一个高效和准确的模型.
- 开发一种新的混合模型,将黑翼风算法 (BKA) 和极端梯度增强 (XGBOOST) 结合起来,用于MR预测.
- 在工程应用开发的模型基础上创建实用软件.
主要方法:
- 这项研究提出了一种名为BKA-XGBOOST的混合模型.
- 使用XGBOOST来建模地质技术因素与MR之间的非线性关系.
- 黑翼风算法 (BKA) 优化了XGBOOST的超参数,提高了其预测能力.
主要成果:
- 与其他九种模型相比,BKA-XGBOOST模型显示出优越的MR预测准确度.
- 该模型实现了0.995的高确定系数 (R2) 和0.975 MPa的低平均绝对误差 (MAE).
- 基于BKA-XGBOOST模型开发了一个交互式软件,以增强其对工程师的实际实用性.
结论:
- BKA-XGBOOST模型提供了一种稳定而准确的方法,用于在各种土壤数据中预测土壤弹性模块.
- 开发的软件为工程师提供了一个实用的工具,促进了有效的路面财产评估.
- 这项研究为路面工程的MR估计做出了有前途的贡献.
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