开发新的机器学习智能模型来预测挖掘道的位移
Abdollah Tabaroei1, Muhand Jawad Jasim2, Ali Mohammed Al-Araji3
1Department of Civil Engineering, Eshragh Institute of Higher Education, Bojnourd, Iran. a.tabaroei@eshragh.ac.ir.
Scientific reports
|August 22, 2025
概括
新的机器学习模型预测深度城市挖掘的道迁移. 这些模型有助于评估地下基础设施的风险,
科学领域:
- 地质工程
- 计算力学
- 机器学习应用
背景情况:
- 城市发展需要对基础进行深度挖掘,
- 地铁道特别容易受到邻近深度挖掘所造成的移位的影响.
研究的目的:
- 开发智能模型来预测挖掘引发的道移位.
- 分析挖掘几何和道位置对道行为的影响.
主要方法:
- 根据案例研究验证了一个三维 (3D) 有限元素模型.
- 进行360次3D FE模拟,对挖掘和道参数进行变化.
- 开发了两种机器学习模型来预测特定的位移值.
主要成果:
- 挖掘道综合体的移位机制已经确定.
- 挖掘的几何结构对道的移位有很大影响.
- 道靠近挖掘对垂直移动产生影响,而更接近的道则会导致向下移动.
结论:
- 开发的机器学习模型准确地预测了挖掘和道移位.
- 这些发现为缓解深度挖掘道的风险提供了洞察力.
- 了解迁移机制对于安全的城市建设至关重要.
相关概念视频
01:24Sequence Networks of Rotating Machines
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...