基于离散元件和神经网络的神经网络的岩石斜坡断裂网络模型的智能施工方法
Zhe Zhang1, Huailin Chen1, Honggang Wu2,3
1State Key Laboratory of Intelligent Geotechnics and Tunnelling, Southwest Jiaotong University, No.111, North Section 1, Erhuan Road, Chengdu, Sichuan, China.
Scientific reports
|November 25, 2025
概括
本研究介绍了一种智能方法,可以使用addit数据和AI构建离散断层网络 (DFN). 它准确地模拟了复杂的岩石斜坡断裂,帮助工程项目.
科学领域:
- 地质技术工程 地质技术工程
- 人工智能在地球科学中的应用
背景情况:
- 在的岩石斜坡中描述复杂的断裂网络是一个重大的工程挑战.
- 现有的方法往往与这些地质结构的复杂性质作斗争.
研究的目的:
- 开发一个智能离散断层网络 (DFN) 构建方法,集成现场的数据和AI算法.
- 准确地建模高岩斜坡中复杂的内部断裂网络.
主要方法:
- 利用工程地质调查从添加站点收集空间断裂数据.
- 使用蒙特卡洛原理和3DEC软件模拟建立了一个3D DFN生成模型框架.
- 使用逆向传播 (BP) 和级联相关 (CC) 神经网络将添加断裂参数映射到DFN模型参数中.
主要成果:
- 优化的神经网络模型从有限的附加数据中实现了高精度的断裂网络反转.
- 在使用三层BP网络的灾难性岩石区域中,预测误差低至8.0%.
- 验证了智能算法的适用性,用于在不同的岩石学单位中的断裂网络建模.
结论:
- 拟议的智能DFN施工方法为模拟复杂的岩石质结构提供了一种新的方法.
- 这种技术提高了工程应用中断裂网络表征的准确性.
- 这些发现与大规模基础设施项目,如达杜河大桥有直接相关性.
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