Block Collapse Prediction and Reinforcement Optimization in Tunnels Based on Discontinuous Deformation Analysis and Machine Learning Models

IF 3.6 2区 工程技术 Q2 ENGINEERING, GEOLOGICAL
Hongyun Fan, Liping Li, Yuguang Fu, Hongliang Liu, Xiangyu Chang, Xin Gao
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引用次数: 0

Abstract

Block collapse is one of the most common geological hazards encountered during tunnel construction, characterized by its sudden occurrence and severe consequences. Currently, the prediction and prevention of tunnel block collapse rely primarily on theoretical analysis, numerical simulations, and physical experiments. However, these approaches often oversimplify real‐world conditions and lack efficiency. This study proposes a prediction and reinforcement optimization method for block collapse by integrating discontinuous deformation analysis (DDA) with machine learning method. First, DDA method was employed to simulate tunnel block collapse under structural plane inclination angles of 15°, 30°, and 45°. The corresponding simulation errors compared to model test results were 2.68%, 3.76%, and 1.01%, respectively, demonstrating the accuracy of the DDA approach in modeling block collapse. Next, a dataset of 142 block collapse scenarios under varying conditions was established, encompassing multiple parameters. Among them, the spacing, inclination angle, and internal friction angle of structural planes were identified as having the most significant influence on collapse behavior. Subsequently, five machine learning models were developed to predict collapse height, affected area, and perimeter deformation. All models achieved high coefficients of determination (R2), with XGBoost exhibiting the best performance. Finally, a data‐driven method for optimizing reinforcement parameters was introduced by integrating the DDA and XGBoost models. Based on prediction results for Cases 128–142, the number of required rock bolts was successfully reduced from 255 to 98 using the proposed optimization strategy. This research provides a valuable reference for the design of reinforcement measures in practical tunnel construction in rock masses.
基于不连续变形分析和机器学习模型的隧道块体塌陷预测及加固优化
块体塌陷是隧道施工中最常见的地质灾害之一,具有发生突然、后果严重的特点。目前,巷道块体坍塌的预测与防治主要依靠理论分析、数值模拟和物理实验。然而,这些方法往往过于简化了现实世界的条件,缺乏效率。提出了一种将不连续变形分析(DDA)与机器学习相结合的块体坍塌预测与加固优化方法。首先,采用DDA方法模拟了结构面倾角为15°、30°和45°时的隧道块体坍塌。与模型试验结果相比,相应的仿真误差分别为2.68%、3.76%和1.01%,说明DDA方法在块体坍塌建模中的准确性。其次,建立了包含多个参数的142个不同条件下的块体坍塌场景数据集。其中,结构面间距、倾角和内摩擦角对破坏行为的影响最为显著。随后,开发了五个机器学习模型来预测坍塌高度、受影响区域和周边变形。所有模型都获得了较高的决定系数(R2),其中XGBoost表现出最好的性能。最后,结合DDA模型和XGBoost模型,提出了一种数据驱动的加固参数优化方法。根据案例128-142的预测结果,采用所提出的优化策略,所需锚杆数量成功地从255个减少到98个。该研究为实际岩体隧道施工中的加固措施设计提供了有价值的参考。
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来源期刊
CiteScore
6.40
自引率
12.50%
发文量
160
审稿时长
9 months
期刊介绍: The journal welcomes manuscripts that substantially contribute to the understanding of the complex mechanical behaviour of geomaterials (soils, rocks, concrete, ice, snow, and powders), through innovative experimental techniques, and/or through the development of novel numerical or hybrid experimental/numerical modelling concepts in geomechanics. Topics of interest include instabilities and localization, interface and surface phenomena, fracture and failure, multi-physics and other time-dependent phenomena, micromechanics and multi-scale methods, and inverse analysis and stochastic methods. Papers related to energy and environmental issues are particularly welcome. The illustration of the proposed methods and techniques to engineering problems is encouraged. However, manuscripts dealing with applications of existing methods, or proposing incremental improvements to existing methods – in particular marginal extensions of existing analytical solutions or numerical methods – will not be considered for review.
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