贝叶斯深度强化学习用于不确定性量化和适应性支持优化在深度基础坑工程中的贝叶斯深度强化学习
1Architectural Engineering Institute, Yancheng Polytechnic College, Yancheng, 224005, Jiangsu, China. gwming0228@126.com.
Scientific reports
|October 9, 2025
概括
本研究介绍了深层基础坑支的智能框架,结合了贝叶斯推理和深度强化学习. 该系统通过优化支持和降低成本,提高了地质工程的安全性和效率.
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:
- 深层基础坑需要强大的支持系统来管理复杂的多物理相互作用.
- 传统方法往往缺乏适应性和精确的不确定性量化.
研究的目的:
- 开发一个完整的框架,用于确定不确定性量化和适应性支持优化深度基础坑系统.
- 通过智能自动化提高地质工程施工的安全性,效率和成本效益.
主要方法:
- 贝叶斯推理 (马尔科夫链蒙特卡罗) 与深度强化学习的整合.
- 开发一个多物理合数值模型 (机械-液压-热).
- 实时监控数据的整合用于适应性支持调整.
主要成果:
- 实现了高预测准确度 (R2=0.91) 和可靠性 (覆盖概率=96.8%).
- 墙壁位移 (35%) 和表面沉积 (42%) 的显著减少.
- 报告了18%的成本节约,12%的建筑时间缩短,零安全事故.
结论:
- 这种新的框架比传统的决定性方法提供了更高的性能.
- 智能适应性支持优化可提高变形控制和施工效率.
- 这项研究为先进的城市地质工程提供了实用工具.
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