Rock Mechanics Bulletin最新文献_第4页

An improved dual-strength reduction method of slope stability analysis using Hill Climbing Algorithm 一种改进的基于爬坡算法的边坡稳定性双强度折减法

IF 7

Rock Mechanics Bulletin

Pub Date : 2025-07-10 DOI: 10.1016/j.rockmb.2025.100220

Chunguang Wang , Ji Wang , Zhigang Tao , Manchao He , Haichao Liu , Shuai Li , Zhiyou Gao , Guojie Liang

How to determine reduction strategy between cohesion (c) and internal friction angle (ϕ) is crucial for slope stability evaluation using the dual-strength reduction method (DSRM). Given that the slope sliding evolution is recognized as a dynamically mechanical system, interaction between sliding mass and sliding bed is governed by minimization of the action, which is consistent with minimum factor of safety of Pan's Extremum Principle. This study introduces an improved dual-strength reduction method by using Hill Climbing Algorithm to determine reduction strategy for the dual-strength parameters. Through employing this approach to analyze the stability of an embankment slope under unsaturated steady seepage, the reduction path of dual-strength parameters is obtained. It is found that the internal friction angle degrades preferentially during the transition from stable state to critical state, followed by the cohesion degradation. The results are in agreement with the rate-and-state friction law. Pore water pressure can reduce frictional resistance, leading to greater degradation of friction angle at critical state. Conversely, the water-rock softening effect can lead to a smaller reduction in the friction angle than in cohesion. This method can provide a new insight into developing dual-strength parameters reduction strategy for the slope stability analysis.

如何确定黏聚力(c)和内摩擦角（φ）之间的折减策略是采用双强度折减法（DSRM）评价边坡稳定性的关键。考虑到边坡的滑动演化是一个动态力学系统，滑体与滑床之间的相互作用受作用最小的控制，这符合潘氏极值原理的最小安全系数。本文提出了一种改进的双强度折减方法，利用爬坡算法确定双强度参数的折减策略。通过对非饱和稳定渗流作用下路堤边坡稳定性分析，得到了双强度参数的折减路径。从稳态过渡到临界状态时，内摩擦角优先减小，黏聚力随之减小。结果与速率-状态摩擦规律一致。孔隙水压力会降低摩擦阻力，导致临界状态下摩擦角的退化更大。相反，水岩软化效应导致的摩擦角减小量小于黏聚力减小量。该方法为制定边坡稳定性分析的双强度参数折减策略提供了新的思路。

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引用次数: 0

Forecasting maximum magnitude of fluid-induced earthquakes: Bridging statistical extrapolation and physics-based forecasting 预测流体诱发地震的最大震级：桥接统计外推和基于物理的预测

IF 7

Rock Mechanics Bulletin

Pub Date : 2025-07-01 DOI: 10.1016/j.rockmb.2025.100207

Shaohua Zeng, Changsheng Jiang, Hongyu Zhai, Lingbin Meng, Ziang Wang

The forecasing of maximum magnitude (M_max) for fluid-induced seismicity is a critical challenge in risk management for geothermal energy development, unconventional hydrocarbon extraction, and wastewater disposal. This review systematically examines eight forecasting models spanning three categories: statistical models (G-R relation, extreme value theory, order statistics), semi-physical models (injection volume/time correlations), and physics-based models (rupture mechanics, rate-state friction, pressure diffusion). Through tripartite analysis of parameter sensitivity, scientific interpretability, and engineering validation, we reveal fundamental constraints: (1) Model performance is strictly governed by parameter coverage. Statistical models relying solely on seismic catalogs neglect fault mechanics parameters. Semi-physical models establish empirical ΔV/T-M_max relationships but fail to explain dynamic slip processes and post-injection "trailing effects". Physics-based models integrate fault stress, friction evolution, and fluid diffusion, offering unparalleled mechanistic insights, yet their computational demands pose challenges for real-time field applications (only 7.5% of 228 global cases). (2) Scientific interpretability exhibits hierarchical deficiencies. While physics-based models quantitatively resolve stress transfer and friction evolution (e.g., rate-state models distinguishing seismic/aseismic slip), forecasting of far-field triggering and multi-fault interactions remain reliant on immature theories like poroelastic coupling. (3) Systemic validation biases emerge: 57.5% of cases concentrate in stable North American terrains with underrepresentation of Pacific Rim high-stress zones; 79.8 % involve geothermal/shale gas operations lacking CO₂ sequestration validations; M ≥ 4.0 events constitute merely 12.7% of datasets, leaving M ≥ 5.0 predictive capacity unverified. We propose transformative pathways including hybrid “physics-core + machine learning” architectures, standardized multi-scenario validation protocols, and dynamic traffic-light systems, aiming to advance cross-disciplinary forecasting paradigms for engineering risk governance.

流体诱发地震活动的最大震级（Mmax）预测是地热能源开发、非常规油气开采和废水处理风险管理中的一个关键挑战。本文系统地研究了八种预测模型，包括三类：统计模型（G-R关系、极值理论、阶统计）、半物理模型（注入量/时间相关性）和基于物理的模型（破裂力学、速率状态摩擦、压力扩散）。通过对参数敏感性、科学可解释性和工程验证性的三方分析，揭示了模型性能的基本约束：(1)模型性能严格受参数覆盖率的制约。仅仅依靠地震表的统计模型忽略了断层力学参数。半物理模型建立了经验ΔV/T-Mmax关系，但无法解释动态滑移过程和注入后的“尾效应”。基于物理的模型整合了断层应力、摩擦演化和流体扩散，提供了无与伦比的力学见解，但其计算需求对实时现场应用构成了挑战（仅占全球228例案例的7.5%）。(2)科学可解释性存在层次缺陷。虽然基于物理的模型定量地解决了应力传递和摩擦演化（例如，区分地震/地震滑动的速率-状态模型），但远场触发和多断层相互作用的预测仍然依赖于不成熟的理论，如孔隙弹性耦合。(3)系统验证偏差出现：57.5%的病例集中在稳定的北美地区，环太平洋地区的高应力区代表性不足；79.8%的地热/页岩气作业缺乏二氧化碳封存验证；M≥4.0事件仅占数据集的12.7%，因此M≥5.0的预测能力尚未得到验证。我们提出了包括混合“物理核心+机器学习”架构、标准化多场景验证协议和动态红绿灯系统在内的变革路径，旨在推进工程风险治理的跨学科预测范式。

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引用次数: 0

Impact mechanism of fabric changes in different classes of redbeds under static water on their degradation of physical properties 静水作用下不同类型红床织物变化对其物理性能退化的影响机理

Rock Mechanics Bulletin

Pub Date : 2025-07-01 DOI: 10.1016/j.rockmb.2025.100194

Zhen Liu, Guangjun Cui, Cuiying Zhou, Chunhui Lan

Deterioration of the physical properties of redbeds is one of the leading causes of geological disasters, engineering problems, and ecological damage. Fabric changes are internal factors leading to the deterioration of physical properties. Existing research on fabric changes in redbeds is qualitative and fuzzy, and their impact on the decline of physical properties needs to be revealed urgently, making the efficient control of redbeds disasters challenging. Therefore, this study focuses on 22 types of redbeds samples and divides them into five classes based on their fabric (composition and structure). Physical property degradation experiments were conducted on different classifications of redbeds during water–rock interaction, and the impact of fabric changes on the degradation of physical properties was analyzed. The results indicate that a linear correlation exists between the internal changes in composition, structure, and physical properties under the action of static water. Moreover, the trend of changes between composition and structure, composition–physical properties, and structure–physical properties shows an exponential regression relationship. Based on this, an action mechanism between compositions such as redbeds, catastrophic minerals, elements, and oxides, as well as the void structure, was proposed, revealing the multifield degradation mechanism of the chemical reactions of the compositions, physical response of the structure, and mechanical reaction of the rock block under the influence of the fabric. The research results can provide a foundation for theoretical research and engineering practice of disaster modes, disaster mechanisms, and prevention and control principles of redbeds disasters.

红层物理性质的恶化是地质灾害、工程问题和生态破坏的主要原因之一。织物的变化是导致其物理性能恶化的内在因素。现有关于红层结构变化的研究定性模糊，其对物性下降的影响亟待揭示，为有效控制红层灾害带来了挑战。因此，本研究以22种红床样品为研究对象，根据其织物（成分和结构）将其分为5类。在水岩相互作用过程中，对不同类型红层进行了物性降解实验，分析了组构变化对红层物性降解的影响。结果表明，在静水作用下，其内部组分、结构和物性变化呈线性相关。组分与结构、组分-物性、结构-物性的变化趋势呈指数回归关系。在此基础上，提出了红层、灾变矿物、元素、氧化物等组成物与孔隙结构之间的作用机理，揭示了组构作用下岩石块体化学反应、结构物理反应和力学反应的多场降解机理。研究成果可为红床灾害的灾害方式、灾害机理、防治原则等方面的理论研究和工程实践提供基础。

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引用次数: 0

Analysis of spectrum characteristics for multi-attribute seismic data from shaking table test of tunnel underpass hauling sliding surface 隧道地下通道牵引滑面振动台试验多属性地震数据频谱特征分析

Rock Mechanics Bulletin

Pub Date : 2025-07-01 DOI: 10.1016/j.rockmb.2025.100196

Lifang Pai , Honggang Wu , Zhongqiang Yi

The objective of this research is to analyze the deformation characteristics in space and the dynamic reaction of tunnel underpass hauling sliding surfaces when subjected to potential seismic activities. The response characteristics and failure mode of tunnel linings are uncovered through an analysis of their time-frequency dynamic behavior. It is thoroughly discussed that there are correlation characteristics of plectrum amplitude in multivariate data under different excitation intensities and the spectrum difference characteristics during different shaking stages. The findings indicate that the slope's structural characteristics and failure modes correspond to the shallow slip type and the deformation trend of the deep weak basement slip type. The influence of topographic bias can be disregarded in tunnel underpass hauling sliding surfaces. The axial force (f_a) in the tunnel lining section primarily exhibits pressure, while the bending moment (M_b) is symmetrically distributed along the lining section. Tunnels are susceptible to collapse and invert uplift damage. The dominant frequency bands of dynamic strain, dynamic soil pressure, and acceleration are mainly concentrated within the 1–15 Hz range. Dynamic soil pressure and acceleration have a significant correlation, whereas the dynamic strain exhibits a weak correlation with both. The dynamic strain and acceleration exhibit sensitivity in their spectrum response before and during the main shock, whereas the dynamic soil pressure shows sensitivity in its spectrum response after the main shock. Based on the spectral response differences of multi-attribute data during various shaking stages, which can present a novel approach for dynamic monitoring and early warning of seismic actions.

本研究的目的是分析潜在地震活动作用下隧道地下通道牵引滑动面的空间变形特征和动力反应。通过对隧道衬砌时频动力特性的分析，揭示了隧道衬砌的响应特性和破坏模式。深入讨论了不同激励强度下多变量数据谱幅的相关特征和不同振动阶段的谱差特征。研究结果表明，该边坡的结构特征和破坏模式符合浅滑型和深弱基底滑移型的变形趋势。在隧道地下通道牵引滑动面时，可以忽略地形偏差的影响。隧道衬砌断面的轴向力主要表现为压力，而弯矩则沿衬砌断面对称分布。隧道易发生坍塌和倒拔破坏。动应变、动土压力和加速度的优势频带主要集中在1 ~ 15 Hz范围内。动土压力与加速度的相关性显著，而动应变与两者的相关性较弱。动应变和加速度在主震前和主震期间的谱响应表现出敏感性，动土压力在主震后的谱响应表现出敏感性。基于不同地震阶段多属性数据的谱响应差异，为地震动态监测和预警提供了一种新的方法。

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引用次数: 0

Exploring machine learning techniques for open stope stability prediction: A comparative study and feature importance analysis 机器学习技术在空场稳定性预测中的应用：比较研究与特征重要性分析

Rock Mechanics Bulletin

Pub Date : 2025-07-01 DOI: 10.1016/j.rockmb.2024.100146

Alicja Szmigiel , Derek B. Apel , Yashar Pourrahimian , Hassan Dehghanpour , Yuanyuan Pu

The stability of underground excavations is essential for ensuring the safety of mining operations. Classical stability assessment methods, established in empirical formulas and rock mass classification systems, have long been employed for evaluating stope stability in underground mining. Stability graphs, a popular empirical approach, utilize factors like rock stress, joint orientation, and surface orientation to calculate stability numbers critical for stope design. However, modern advancements in machine learning present new opportunities for enhancing predictive capabilities and understanding complex relationships influencing stope stability. Building upon research demonstrating the feasibility of using machine learning for stability prediction, our study investigates and compares several machine learning algorithms. By analyzing a dataset comprising stope dimensions and geomechanical properties, we explore the potential of machine learning models such as Random Forest, Support Vector Machine, AdaBoost, XGBoost, LightGBM, and Artificial Neural Network in predicting stope stability. Evaluation metrics including accuracy, precision, recall, and F1 score are employed to assess model performance, with the Artificial Neural Network emerging as the most effective. Furthermore, SHapley Additive exPlanations (SHAP) analysis enhances interpretability by explaining the contribution of individual features to model predictions.

地下掘进的稳定性是保证矿山安全生产的关键。长期以来，在经验公式和岩体分类体系中建立的经典稳定性评价方法一直被用于地下开采采场稳定性评价。稳定性图是一种流行的经验方法，它利用岩石应力、节理方向和地表方向等因素来计算对采场设计至关重要的稳定性数字。然而，现代机器学习的进步为提高预测能力和理解影响采场稳定性的复杂关系提供了新的机会。基于证明使用机器学习进行稳定性预测的可行性的研究，我们的研究调查并比较了几种机器学习算法。通过分析包含采场尺寸和地质力学特性的数据集，我们探索了随机森林、支持向量机、AdaBoost、XGBoost、LightGBM和人工神经网络等机器学习模型在预测采场稳定性方面的潜力。评估指标包括准确性、精密度、召回率和F1分数来评估模型的性能，其中人工神经网络是最有效的。此外，SHapley加性解释（SHAP）分析通过解释个体特征对模型预测的贡献来提高可解释性。

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引用次数: 0

Elastoplastic analysis of deep circular tunnels affected by blasting damage and hydraulic flow 爆破损伤和水力流对深埋圆形隧道弹塑性影响分析

Rock Mechanics Bulletin

Pub Date : 2025-07-01 DOI: 10.1016/j.rockmb.2025.100195

Saeed Karamipoor, Ali Reza Kargar, Abbas Majdi, Fariborz Matinpoor

In drill and blast method, due to uncontrolled blasting operations, the blast-induced damaged zone (BIDZ) is formed, whose mechanical and hydraulic properties are altered. This zone affects the behavior of the rock mass such that it reduces the strength of surrounding mass, and stability of the excavation. On the other hand, the groundwater is also effected by damaged zone induced stress and displacement, leading to a change in hydraulic flow around the tunnel which subsequently could produce new stress and displacement fields. In this research, an analytical solution for evaluating the stress and displacement of deep circular tunnels in elastoplastic rock mass is proposed, assuming the presence of BIDZ and hydraulic flow around the tunnel. The tunnel is subjected to in situ hydrostatic stresses, under radial hydraulic flow, and the damaged zone is supposed cylindrical shaped surrounding the cavity. Four different scenarios are predicted for stress evolution around the cavity considering the seepage zone, damage zone and plastic zone spread for elastic brittle-plastic behavior of surrounding mass. The analytical solution is validated using FLAC software, which shows excellent agreement. Examples are given to investigate the effect of BIDZ on the stress and displacement fields around the tunnel in both drained and undrained condition. The results show a significant impact on tunnel wall displacement especially for small magnitude of the ratio of seepage zone to damage zone radii, indicating its great significance in tunnel practice in terms of support and ground control.

在钻爆法中，由于无控制的爆破作业，形成了爆致损伤区（BIDZ），其力学和水力性能发生了改变。这个区域影响着岩体的行为，从而降低了周围岩体的强度和开挖的稳定性。另一方面，地下水也受到损伤区诱发的应力和位移的影响，导致隧洞周围水力流量发生变化，从而产生新的应力场和位移场。本文提出了弹塑性岩体中深埋圆形隧道的应力和位移的解析解，并在此基础上考虑隧道周围存在BIDZ和水力流动。在径向水力作用下，隧道受原位静水应力作用，损伤区假定为围绕空腔的圆柱形。考虑周围土体弹脆塑性特性的渗流区、损伤区和塑性区扩展，预测了孔洞周围应力演化的四种不同情景。用FLAC软件对解析解进行了验证，结果吻合良好。通过算例研究了排水和不排水两种情况下BIDZ对隧道周边应力场和位移场的影响。结果表明，渗流区与破坏区半径之比较小，对隧道壁位移影响较大，在隧道支护和地面控制实践中具有重要意义。

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引用次数: 0

Data-driven machine learning approaches for predicting the shear strength of rock joints 预测岩石节理抗剪强度的数据驱动机器学习方法

Rock Mechanics Bulletin

Pub Date : 2025-07-01 DOI: 10.1016/j.rockmb.2025.100209

Zhang Jinge , Jiang Yujing , Zhang Sunhao , Shang Dongqi , Sun Zhenjiao , Chen Hongbin

Accurate prediction of the shear strength of rock joints is crucial for assessing the stability of civil and mining engineering projects. Traditional methods for determining the shear strength of rock joints are time-consuming, costly, and computationally complex. Machine learning methods, which are driven by data, provide a cost-effective and rapid approach to predicting rock joint shear strength, overcoming the limitations of traditional techniques. This study employs nine machine learning models: eXtreme gradient boosting (XGBoost), random forest (RF), Support vector regression (SVR), decision tree (DT), Gaussian process regression (GPR), K-nearest neighbors (KNN), categorical boosting (CatBoost), extreme learning machine (ELM), and adaptive boosting (AdaBoost). A dataset of 288 data points was compiled from an extensive set of literature. Five input features, namely, normal stress, uniaxial compressive strength, Young’s modulus, joint roughness coefficient (JRC), and specimen length, were selected, with shear strength of the rock joints as the output variable. The performance of the nine ML models was assessed using the root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). Due to its unique ordered boosting mechanism and symmetric tree structure, CatBoost outperformed the other models, achieving RMSE, R², and MAE values of 0.4663, 0.9765, and 0.3508, respectively. Compared with the experimental results, the model yielded a mean square error (MSE) of 0.0360. The proposed ML method offers a cost-effective and efficient solution for predicting rock joint shear strength.

岩体节理抗剪强度的准确预测对于评价土矿工程的稳定性至关重要。确定岩石节理抗剪强度的传统方法耗时长、成本高、计算复杂。机器学习方法是由数据驱动的，为预测岩石节理抗剪强度提供了一种经济、快速的方法，克服了传统技术的局限性。本研究采用了9种机器学习模型：极限梯度增强（XGBoost）、随机森林（RF）、支持向量回归（SVR）、决策树（DT）、高斯过程回归（GPR）、k近邻增强（KNN）、分类增强（CatBoost）、极限学习机（ELM）和自适应增强（AdaBoost）。从广泛的文献中编制了288个数据点的数据集。选取法向应力、单轴抗压强度、杨氏模量、节理粗糙度系数（JRC）和试件长度5个输入特征，以岩石节理抗剪强度为输出变量。使用均方根误差（RMSE）、决定系数（R2）和平均绝对误差（MAE）对9个ML模型的性能进行评估。由于其独特的有序提升机制和对称树结构，CatBoost优于其他模型，RMSE、R2和MAE值分别为0.4663、0.9765和0.3508。与实验结果比较，该模型的均方误差（MSE）为0.0360。该方法为预测岩石节理抗剪强度提供了一种经济有效的方法。

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引用次数: 0

Definition and classification of rockburst 岩爆的定义和分类

Rock Mechanics Bulletin

Pub Date : 2025-07-01 DOI: 10.1016/j.rockmb.2025.100206

Manchao He , Ismet Canbulat , Fidelis T. Suorineni , Murat Karakus , Wen Nie , Dongqiao Liu , Chengguo Zhang , Alexey Nagibin , Bauyrzhan Rustembek

The rockburst is a violent failure in rock during mining and tunneling operations. Since tunnel constructions for hydropower and transportation purposes and mining operations in deep rock masses have been increasing recently, more frequent rockburst cases have been reported. This paper proposes a new definition for rockburst, considering the main components of the rockburst along with triggering mechanisms and reasons. For this purpose, the historical definitions of rockburst and its related classifications have been reviewed. In terms of triggering mechanisms, a rockburst must be induced by excavation resulting from three effects arising from the transition of stress state from 3- to 1-dimension, such as the transient radial stress loss, the time-dependent tangential stress increase, and the peak strength drop, which are explained by examining the stress transition in the shear stress vs normal stress space and energy transition in the stress-strain space. Based on the understanding of the three effects of excavation, a new definition of rockburst is proposed: “A rockburst is a sudden failure of rock mass surrounding the excavations caused by the rapid release of stored energy when induced stresses exceed the rock strength”. Additionally, rockbursts are classified according to transitions in the static and dynamic stress fields, with further subclassifications into instantaneous and delayed bursts based on the timing of occurrences relative to radial stress drop and tangential stress increase. Rockburst management strategies are also proposed to address stress and energy transitions in excavations.

岩爆是在采矿和隧道施工过程中岩石发生的剧烈破坏。近年来，随着水电、交通等隧洞建设和深部岩体采矿作业的增加，岩爆事故也越来越多。本文从岩爆的主要组成、触发机制和原因出发，提出了岩爆的新定义。为此，本文回顾了岩爆的历史定义及其相关分类。在触发机制上，应力状态从三维向一维转变所产生的径向瞬态应力损失、随时间变化的切向应力增加和峰值强度下降三种效应必然会导致开挖诱发岩爆，这可以通过考察剪应力-法向应力空间的应力转变和应力-应变空间的能量转变来解释。在认识开挖三种效应的基础上，提出了岩爆的新定义：“岩爆是指当诱导应力超过岩石强度时，由于储存的能量迅速释放而引起的开挖周围岩体的突然破坏”。此外，岩爆根据静态和动态应力场的转变进行分类，并根据相对于径向应力下降和切向应力增加的发生时间进一步细分为瞬时岩爆和延迟岩爆。针对开挖过程中的应力和能量转换，提出了岩爆管理策略。

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引用次数: 0

The phlogiston theory of rock mass Classification: Philosophical and mathematical critique of ordinal data usage 岩体分类的燃素理论：序数数据使用的哲学和数学批判

Rock Mechanics Bulletin

Pub Date : 2025-07-01 DOI: 10.1016/j.rockmb.2025.100205

Junzhe Liu , Yu Feng , Yuyong Jiao

The widespread use of rock mass classification systems in engineering practice relies on mathematical operations and assumptions that violate fundamental principles of measurement theory. This paper presents a critical analysis of current classification methodologies, focusing on the Rock Mass Rating (RMR), Q-system, and Geological Strength Index (GSI), drawing parallels with historical scientific misconceptions such as the phlogiston theory. Through detailed examination of measurement theory principles and their application to geological characterization, we demonstrate that these classification systems contain inherent flaws in their treatment of ordinal data and parameter independence. The paper identifies four critical issues: the invalid summation of ordinal ratings in the RMR system, the inappropriate multiplication and division operations in the Q-system, the unjustified visual interpolation in the GSI system, and the universal problem of assumed parameter independence. Through examination of measurement theory principles and their application to geological characterization, we demonstrate that current classification systems violate basic mathematical rules in their treatment of ordinal data and parameter independence. The implications of these violations extend beyond theoretical concerns, affecting practical engineering decisions and risk assessment. We also illustrate how these theoretical flaws manifest in practice and propose directions for developing more theoretically sound approaches to rock mass characterization. This critical analysis aims to initiate a necessary dialogue about the future of rock mass classification in engineering practice.

工程实践中广泛使用的岩体分类系统依赖于违背测量理论基本原则的数学运算和假设。本文对当前的分类方法进行了批判性分析，重点关注岩体等级（RMR）， q -系统和地质强度指数（GSI），并与历史上的科学误解（如燃素理论）进行了比较。通过对测量理论原理及其在地质表征中的应用的详细研究，我们证明了这些分类系统在处理有序数据和参数独立性方面存在固有缺陷。本文指出了RMR系统中序数求和无效、q系统中乘除运算不恰当、GSI系统中视觉插值不合理以及普遍存在的假设参数独立性问题。通过对测量理论原理及其在地质表征中的应用的考察，我们证明了目前的分类系统在处理有序数据和参数独立性方面违反了基本的数学规则。这些违反的含义超出了理论的关注，影响了实际的工程决策和风险评估。我们还说明了这些理论缺陷如何在实践中表现出来，并提出了发展理论上更合理的岩体表征方法的方向。这一批判性分析旨在就工程实践中岩体分类的未来展开必要的对话。

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引用次数: 0

Experimental study on the mechanical properties of 1G-NPR cable anchored rock with AE-DIC method 采用AE-DIC法对1G-NPR锚索锚固岩体力学特性进行试验研究

IF 7

Rock Mechanics Bulletin

Pub Date : 2025-06-21 DOI: 10.1016/j.rockmb.2025.100215

Jiong Wang , Jian Jiang , Yiwen Chang , Haosen Wang , Lei Ma , Manchao He , Peng Liu , Siyu Wang

The study investigates the mechanical properties of 1G-NPR (Negative Poisson's Ratio) cable-anchored sandstone under uniaxial compression, employing Acoustic Emission (AE) and Digital Image Correlation (DIC) methods to analyze deformation and fracture behavior. The research aims to provide insights into the failure mechanisms of rock anchored with 1G-NPR cables and their potential applications in engineering practices. A comparative analysis was performed on three anchoring methods—unanchored, conventional cable-anchored, and 1G-NPR cable anchored—under both lateral confinement and unconfined conditions during uniaxial compression. Results show that rock specimens anchored with 1G-NPR cables exhibit significantly higher uniaxial compressive strength compared to unanchored and conventional cable-anchored specimens. The 1G-NPR cables provide constant resistance at peak stress, followed by a stepped decrease in post-peak bearing capacity. Under lateral confinement, AE events are minimal in the early stage and become concentrated during the unstable crack propagation phase, accounting for around 75% of cumulative AE events. This phase features a pronounced AE activity peak at a strain level of 5.24 × 10⁻³, the highest among the six test groups. Post-failure analysis reveals that 1G-NPR cable-anchored rock exhibits the lowest degree of fragmentation, with cracks not extending through the cable position, indicating that failure did not penetrate the cable-anchored zone. Lateral confinement aids in restricting strain concentration along the anchoring direction. DIC analysis of principal strain fields further indicates that horizontal displacement zones in 1G-NPR cable-anchored specimens emerge at 0.6P_max at a later stage than in other groups, suggesting that these cables effectively control crack formation and propagation within the rock mass. Findings highlight the effectiveness of 1G-NPR cables in enhancing rock strength, limiting failure, and managing large deformations, thereby playing a critical role in stabilizing surrounding rock under high-ground stress in engineering applications.

研究了负泊松比（1G-NPR）锚固砂岩在单轴压缩下的力学特性，采用声发射（AE）和数字图像相关（DIC）方法分析了其变形和断裂行为。该研究旨在深入了解g - npr锚索锚固岩石的破坏机制及其在工程实践中的潜在应用。在单轴压缩条件下，对三种锚固方法（非锚固、常规锚固和1G-NPR锚固）进行了对比分析。结果表明，与非锚固和常规锚固试样相比，采用1G-NPR锚固的岩石试件具有更高的单轴抗压强度。1G-NPR电缆在峰值应力时提供恒定的电阻，随后在峰值后的承载能力逐步下降。侧向约束作用下，声发射事件在初期最小，在不稳定裂纹扩展阶段较为集中，约占累积声发射事件的75%。该阶段声发射活动峰值为5.24 × 10−3，是6个试验组中最高的。破坏后分析表明，1G-NPR锚索岩体破碎程度最低，裂缝未延伸至锚索位置，说明破坏未穿透锚索区域。侧向约束有助于限制沿锚固方向的应变集中。主应变场DIC分析进一步表明，1G-NPR锚索试件水平位移区出现在0.6Pmax时较其他组晚，说明1G-NPR锚索有效控制了岩体内裂缝的形成和扩展。研究结果强调了1G-NPR电缆在提高岩石强度、限制破坏和管理大变形方面的有效性，从而在工程应用中在高地应力下稳定围岩方面发挥了关键作用。

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引用次数: 0