基于机器学习算法的堆周围堆顶部和地面表面的位移预测
Penglin Li1, Shaolong Guo2, Manman Liang3
1China Harbor Engineering Co., LTD., Beijing, 100027, China.
Scientific reports
|January 23, 2026
概括
人工智能,包括自适应提升 (AdaBoost) 和深度神经网络 (DNN),准确预测由土壤挤压效应造成的水平移位. 影响这些迁移的关键因素包括距离,角度,休息时间和土壤特性.
科学领域:
- 地质技术工程 地质技术工程
- 计算力学 计算力学 计算力学
- 人工智能的人工智能
背景情况:
- 堆群的土壤挤压效应会导致严重的地面和堆顶部移位.
- 这些迁移带来了诸如对邻近建筑物和基础设施的结构损坏等风险.
- 预测这些影响对于基础设施安全和设计至关重要.
研究的目的:
- 探索人工智能 (AI) 的应用,以预测由土壤挤压引起的水平移位.
- 将各种AI模型的准确性与传统方法进行比较.
- 确定影响这些土壤引起的迁移的关键因素.
主要方法:
- 通过将自适应增强 (AdaBoost) 与反向传播 (BP) 神经网络集成,开发了一个Adaboost-BP模型.
- 评估了Adaboost-BP,随机森林 (RF) 和深度神经网络 (DNN) 模型的性能.
- 将AI模型的预测与传统的圆柱形孔扩张方法进行比较.
主要成果:
- 人工智能模型,特别是Adaboost-BP,RF和DNN,显示出比基本BP模型更高的预测准确度,特别是在小型数据集中.
- 平面距离和平台与顶之间的角度是主要影响因素.
- 休息时间,水分含量,相对密度和内部摩擦角度显著影响移位.
结论:
- 人工智能模型为预测土壤挤压引起的迁移提供了比传统方法更好的替代方案.
- 机器学习算法在地质技术移位预测中提供了更高的准确性.
- 了解关键影响因素,可以更好地进行风险评估和减缓策略,在堆基础设计.
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