基于 eXtreme梯度增强和SHapley添加式扩展的斜坡稳定性的灵敏度分析:一项探索性研究
Hanjie Lin1, Li Li1, Yue Qiang1
1Department of Civil Engineering, Chongqing Three Gorges University, Wanzhou 404100, Chongqing, China.
Heliyon
|September 2, 2024
概括
这项研究使用XGBoost和SHAP增强了斜率稳定性分析,识别了关键因素及其相关性. 一种新的数据驱动方法改善了人工智能解释,以获得更可靠的地质技术工程见解.
科学领域:
- 地质技术工程 地质技术工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 斜坡的不稳定性带来了重大风险,需要地质工程的强有力的分析方法.
- 传统的斜坡稳定性分析方法往往复杂且耗时.
- 现有的基于人工智能的方法识别了因素的重要性,但缺乏量化相关性分析.
研究的目的:
- 使用XGBoost和SHAP对斜坡稳定因素进行灵敏度分析.
- 量化关键地质技术参数与斜坡稳定性之间的相关性.
- 开发一种数据驱动的方法,以便在斜坡稳定性评估中更准确地解释AI.
主要方法:
- 使用XGBoost和SHAP算法进行敏感性分析.
- 使用GeoStudio软件模拟验证SHAP结果.
- 实施先验数据驱动的方法来增强AI解释.
主要成果:
- 确定斜坡高度和内部摩擦角分别是影响最大和最小的参数.
- GeoStudio的模拟显示了斜坡高度,斜坡角度,单位重量和孔水压系数的负相关性;凝聚力和内部摩擦角度的正相关性.
- 与原始AI解释相比,数据驱动的方法提供了更可靠的破坏稳定的临界值 (例如,凝聚力:18 Kpa,斜率角度:28°,内部摩擦角度32°,斜率高度30m,孔水压系数0.28).
结论:
- XGBoost和SHAP为倾斜稳定的灵敏度分析提供了有价值的工具.
- 以先验数据驱动的方法对于克服人工智能在理解现实世界地质技术机制方面的局限性至关重要.
- 拟议的方法可以产生更准确和可靠的结果,特别是在有限或低质量的数据的情况下.
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