基于多目标数据挖掘的盾道稳定性评估的智能决策方法
Xin Li1, Yiguo Xue2, Zhiqiang Li3
1Institute of Marine Science and Technology, Shandong University, Qingdao, Shandong, People's Republic of China.
概括
盾牌道施工面临着来自挖掘的风险,面临着不稳定. 本研究开发了一个模型,使用形模型和BP神经网络来评估稳定性和限制支压力,为水下道提供了关键的参考.
科学领域:
- 地质技术工程 地质技术工程
- 道工程工程 道工程
- 基础设施中的人工智能
背景情况:
- 在复杂的岩层中建造盾构会带来很大的风险,特别是挖掘面部不稳定.
- 准确评估挖掘面的稳定性对于防止地下项目发生灾难性事故至关重要.
研究的目的:
- 为了建立一个分析公式,在盾牌道挖掘期间,在土壤中限制支压力.
- 通过使用支安全系数,对挖掘面的稳定性进行定量评估.
- 开发一个可靠的评估模型,用于水下盾挖掘面部稳定性.
主要方法:
- 使用形模型开发了极限支压力的分析公式.
- 采用粗略设置算法来降低挖掘面稳定性的评估指数.
- 利用反向传播 (BP) 神经网络创建了一个预测评估模型.
- 与TOPSIS和云模型相比,验证了BP模型的性能.
主要成果:
- 建立了一种量化方法来评估挖掘面的稳定性.
- 开发了一种具有较低预测误差 (5.7675 × 10 - 4) 的BP神经网络模型.
- 与其他模型相比,证明了BP模型的优越预测性能.
结论:
- 拟议的评估方法为评估水下盾牌道挖掘面部稳定性提供了至关重要的参考.
- 分析公式和人工智能模型的整合提高了盾构的安全性和可靠性.
- 这项研究有助于运输基础设施使用人工智能的故障分析领域.
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