Tunnelling and Underground Space Technology最新文献_第10页

Transferable prediction of TBM long-distance tunneling construction duration considering uncertainties in surrounding rock distribution and the evolution of tunneling efficiency 考虑围岩分布不确定性和掘进效率演化的TBM长距离隧道施工工期可转移预测

IF 7.4 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2026-01-10 DOI: 10.1016/j.tust.2026.107446

Jianming Zhang , Kebin Shi , Peixuan Lin , Lei Li , Qiyong Mao , Haibo Jiang , Xinjun Yan , Jingwei Gong

During the construction planning phase, accurately predicting the construction duration of long-distance tunnels built using Tunnel Boring Machines (TBM) is critical for optimizing construction organization and controlling costs. However, the uncertainty of geological conditions and the variability of tunneling efficiency pose challenges in making precise predictions during the planning phase. To address this issue, this study proposes a Monte Carlo model based on Latin Hypercube Sampling (LHS), incorporating the uncertainties in surrounding rock distribution and the evolution of tunneling efficiency. The prediction process is divided into two core stages. The first stage involves integrating borehole data and surrounding rock information obtained from preliminary geological surveys. Using a Markov chain corrected by Bayes’ formula, the uncertainty in geological spatial characteristics is continuously deduced. In the second stage, we first propose a tunneling efficiency decay factor (e) and couple it with the uncertainty in the surrounding rock distribution to establish simulation rules for the construction duration of long-distance TBM tunnels. Subsequently, the Monte Carlo method under LHS sampling is applied for the duration simulation. Finally, two targeted model transfer strategies are proposed to enhance the model’s applicability across different projects. The effectiveness of the proposed method was validated using the Xinjiang KS super‑long tunnel as a case study. The results demonstrated: (1) After considering the spatial distribution uncertainty of geological conditions and parameter e, the proposed model accurately forecasted the construction duration of long‑distance TBM tunneling, and the average prediction error was less than 4 days. Moreover, the model outperformed existing approaches in accuracy and robustness, and exhibited excellent stability and lower computational resource requirements. (2) Global sensitivity analysis indicated that uncertainty in surrounding rock distribution was the primary driver of duration fluctuations, and the proposed model effectively reduced the impact of this uncertainty on construction duration. Dynamic sensitivity further showed that as the excavation distance increased (beyond 6700 m), the sensitivity index of e reached 0.25–0.40, which significantly impacted construction duration. Furthermore, introducing e reduced the prediction error range by 76.47 %–95.83 %. (3) The proposed model exhibited good transferability, and the effectiveness of both model transfer strategies was demonstrated on the new project. This approach provides a valuable reference for predicting construction durations of long-distance TBM tunneling projects in complex geological conditions.

在施工规划阶段，准确预测隧道掘进机施工工期是优化施工组织和控制成本的关键。然而，地质条件的不确定性和隧道掘进效率的可变性给规划阶段的精确预测带来了挑战。为了解决这一问题，本文提出了一种基于拉丁超立方采样（LHS）的蒙特卡罗模型，该模型考虑了围岩分布的不确定性和掘进效率的演化。预测过程分为两个核心阶段。第一阶段包括综合钻孔数据和从初步地质调查中获得的围岩信息。利用经贝叶斯修正的马尔可夫链，连续推导出地质空间特征的不确定性。在第二阶段，我们首先提出了隧道效率衰减因子(e)，并将其与围岩分布的不确定性相结合，建立了长距离TBM隧道施工工期的模拟规则。随后，采用LHS采样下的蒙特卡罗方法进行持续时间模拟。最后，提出了两种有针对性的模型迁移策略，以增强模型在不同项目中的适用性。以新疆KS超长隧道为例，验证了该方法的有效性。结果表明：(1)在考虑了地质条件和参数e的空间分布不确定性后，所建模型能较准确地预测长距离TBM隧道施工工期，平均预测误差小于4天。此外，该模型在精度和鲁棒性方面优于现有方法，具有优异的稳定性和较低的计算资源需求。(2)全局敏感性分析表明，围岩分布的不确定性是工期波动的主要驱动因素，该模型有效降低了这种不确定性对工期的影响。动态敏感性进一步表明，随着开挖距离的增加（超过6700 m）， e的敏感性指数达到0.25 ~ 0.40，对施工工期影响显著。引入e后，预测误差范围减小了76.47% ~ 95.83%。(3)模型具有良好的可转移性，两种模型转移策略的有效性在新项目上得到了验证。该方法为复杂地质条件下长距离隧道掘进机工程工期预测提供了有价值的参考。

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引用次数: 0

Predicting disc cutter forces for hard rock TBM cutterhead modeling: a comparative analysis of modified CSM semi-theoretical model and hybrid deep learning approach 硬岩TBM刀盘建模中刀盘力预测：改进CSM半理论模型与混合深度学习方法的比较分析

IF 7.4 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2026-01-10 DOI: 10.1016/j.tust.2026.107448

Mohammad Matin Rouhani , Jamal Rostami

Prediction of the cutting forces acting on disc cutters is essential for accurate modeling and performance assessment of hard rock tunnel boring machines (TBM). This study investigates two methodologies to enhance the quality of force prediction: the modification of the Colorado School of Mines (CSM) model and the application of advanced machine learning algorithms. The modified CSM model presents rock-type-specific formulas for sedimentary, metamorphic, and igneous rocks, utilizing dimensionless parameters including the Lame brittleness index, internal friction angle, and wave velocity ratio. Three state-of-the-art machine learning architectures, including SAINT (Self-Attention and Intersample Attention Transformer), TabNet, and TabM, are tested with hyperparameter optimization carried out using the Geometric Mean Optimization and Reptile Search Optimization algorithms. The modified CSM model shows statistically significant improvement compared to the original CSM model for all rock groups (p < 0.001), with the most notable enhancement for igneous rocks. Among the machine learning models, GMO-SAINT achieved the highest accuracy for normal force prediction (R² = 0.98 for training and 0.96 for testing), while RSO-TabNet performs best for rolling force prediction (R² = 0.94 for testing). SHAP analysis shows that tip width and cutting depth are the two primary factors that affect normal and rolling forces, respectively, while UCS consistently emerges as a secondary factor with all models. Overall, this combined methodology offers a more reliable cutting force estimation for improving TBM performance prediction.

盘式铣刀切削力的预测对于硬岩隧道掘进机的准确建模和性能评估至关重要。本研究探讨了两种提高力预测质量的方法：修改科罗拉多矿业学院（CSM）模型和应用先进的机器学习算法。修正后的CSM模型利用Lame脆性指数、内摩擦角和波速比等无量纲参数，给出了沉积岩、变质岩和火成岩的岩石类型专用公式。三种最先进的机器学习架构，包括SAINT （Self-Attention and Intersample Attention Transformer）、TabNet和TabM，使用几何平均优化和爬行动物搜索优化算法进行超参数优化测试。与原始CSM模型相比，改进后的CSM模型在所有岩石组中都有统计学上显著的改进（p < 0.001），其中火成岩的改进最为显著。在机器学习模型中，GMO-SAINT法向力预测准确率最高（训练R2 = 0.98，测试R2 = 0.96）， RSO-TabNet滚动力预测准确率最高（测试R2 = 0.94）。SHAP分析表明，刀尖宽度和切削深度分别是影响法向力和轧制力的两个主要因素，而UCS始终是所有模型的次要因素。总的来说，这种组合方法为改进TBM性能预测提供了更可靠的切削力估计。

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引用次数: 0

New sensing-inversion integrated method for mechanical behavior analysis of shield tunnels during heavy rainfall 强降雨条件下盾构隧道力学行为综合分析新方法

IF 7.4 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2026-01-09 DOI: 10.1016/j.tust.2025.107441

Haoran Wang , Chengchao Guo , DingFeng Cao , Jin Tang , Fuming Wang

In this study, a sensing-inversion method was proposed to investigate the mechanical response mechanisms of shield tunnels under heavy rainfall conditions, integrating displacement monitoring, distributed fiber optic sensing, and a strain–displacement-internal force recursive inversion method. Physical model tests were conducted to simulate interactions between heavy rainfall, soil strata, and tunnel structures. Laser displacement sensors and distributed optical fibers were used to monitor dynamic structural deformations and strains. An inversion model based on elastic foundation curved beam theory was developed to quantitatively analyze tunnel deformation evolution, load development mechanisms, and internal force distribution characteristics. The results indicate that the proposed inversion method improved accuracy by over 80% compared to conventional models and effectively captured radial displacements and internal force distributions. Under rainfall loading, the tunnel lining exhibited elliptical deformation and settlement, accompanied by compressive stresses at the crown and invert. The region of compressive stress expanded with increasing overburden thickness, whereas tensile stress developed at the haunches. The compressive stress at the crown exceeded that at the invert. When the tunnel was deeply buried, longer rainfall infiltration paths delayed structural responses to water penetration. Furthermore, deep overburden facilitated the dispersion localized stress concentrations in the lining caused by rainfall.

基于位移监测、分布式光纤传感和应变-位移-内力递推反演相结合的方法，研究强降雨条件下盾构隧道的力学响应机制。进行了物理模型试验，模拟强降雨、土层和隧道结构之间的相互作用。采用激光位移传感器和分布式光纤监测结构的动态变形和应变。建立了基于弹性地基弯曲梁理论的隧道变形演化、荷载发展机理和内力分布特征的反演模型。结果表明，与传统模型相比，该方法的反演精度提高了80%以上，并能有效地捕获径向位移和内力分布。在降雨荷载作用下，隧道衬砌表现为椭圆形变形和沉降，顶冠和仰拱均存在压应力。压应力区域随着覆岩厚度的增加而扩大，而拉应力区域则在后端发育。冠部压应力大于倒部压应力。当隧道埋深时，较长的降雨入渗路径会延迟结构对水侵彻的响应。此外，深厚覆盖层加剧了降雨引起的衬砌局部应力集中的分散。

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引用次数: 0

Data-physics integration model for predicting tunnel convergence subject to water level fluctuations and lining structure degradation 水位波动和衬砌结构退化影响下隧道收敛预测的数据-物理集成模型

IF 7.4 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2026-01-09 DOI: 10.1016/j.tust.2025.107440

Junjie Liu , Qing Ai , Lulu Zhang , Junyi Zhu , Hui Wang , Xingchun Huang , Yong Yuan

Monitoring data from underwater tunnels are critical for operations and maintenance. However, they are often corrupted by noise from water level fluctuations, and the degradation process within them is difficult to extract, which limits the utility of these data. To address this issue, this study proposes a data-physics integration model for predicting tunnel convergence considering water level fluctuations and lining structure degradation. In the data-driven part, an improved Seasonal and Trend decomposition using Loess (STL) is developed to separate seasonal and trend components while accounting for gradual stiffness degradation of the tunnel lining, thereby producing more realistic time-variant seasonal component. In the physics-based part, a probabilistic degradation model is constructed on the modified rigid ring model, with parameters dynamically updated via a dynamic Bayesian network. By embedding the physics-based degradation model into the STL framework, the proposed approach enhances the prediction accuracy of trend component and strengthens physical interpretability. Comparative analysis using convergence monitoring data from a real underwater tunnel shows that, the proposed integration model achieves higher prediction accuracy and better captures the underlying degradation mechanism.

水下隧道的监测数据对运营和维护至关重要。然而，它们经常被水位波动的噪声所破坏，并且它们内部的退化过程难以提取，这限制了这些数据的利用。为了解决这一问题，本文提出了考虑水位波动和衬砌结构退化的隧道收敛预测数据-物理集成模型。在数据驱动部分，提出了一种改进的基于黄土的季节和趋势分解（STL）方法，在考虑隧道衬砌刚度逐渐退化的同时，分离季节和趋势分量，从而得到更真实的时变季节分量。在基于物理的部分，在改进的刚性环模型的基础上建立了概率退化模型，并通过动态贝叶斯网络动态更新参数。该方法通过将基于物理的退化模型嵌入到STL框架中，提高了趋势分量的预测精度，增强了物理可解释性。与实际水下隧道的收敛监测数据对比分析表明，所提出的集成模型具有较高的预测精度，能较好地捕捉到潜在的退化机制。

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引用次数: 0

An interpretable and adaptive tunnel water inflow prediction method using data augmentation and AHP-Enhanced OP-LightGBM 基于数据增强和AHP-Enhanced OP-LightGBM的可解释自适应隧道涌水预测方法

IF 7.4 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2026-01-08 DOI: 10.1016/j.tust.2026.107445

Jingqi Cui , Shunchuan Wu , Haiyong Cheng , Xiaowei Hou , Jiaxin Wang , Weihua Liu , Chaoqun Chu

Accurate prediction of tunnel water inflow is critical for ensuring construction safety and risk control in tunnel engineering. However, traditional regression methods face significant challenges, including limited sample sizes, imbalanced data, complex feature interactions, and difficulty in engineering deployment. To address these issues, this study proposes an intelligent prediction framework that integrates data augmentation, model optimization, interpretability, and online deployment, and additionally possesses strong adaptability to dynamic field conditions. First, the SMOGN undersampling method is employed to balance and augment the training dataset, effectively expanding sparse samples and suppressing the influence of outliers, thereby enhancing the model’s generalization ability. Subsequently, LightGBM is improved through Optuna-based hyperparameter optimization and Analytic Hierarchy Process (AHP)-based feature weight adjustment, forming the AHP-OP-LightGBM hybrid model. This approach reduces prediction error by 15.89 % while aligning feature weights more closely with physical constraints. Compared with conventional optimization strategies, the model demonstrates superior capability in representing hydrogeological characteristics due to the dual mechanism of automated hyperparameter tuning and feature weight correction. Correlation analysis and SHAP-based interpretability further clarify the nonlinear synergistic mechanisms governing the coupled geomechanical-hydrological processes controlling tunnel water inflow. To support engineering application, a cloud-deployed online prediction system is developed using web technologies, integrating SHAP for transparent decision support. Additionally, an incremental learning module is incorporated to accommodate dynamic data variations. Validation using a small set of local incremental samples yields a maximum prediction error of only 1.9169 m³/h, demonstrating strong compatibility and accuracy across different engineering scenarios. Comparative experiments show that, on average, the proposed model reduces prediction error by 39.65 % and improves fitting accuracy by 18.43 % compared with traditional regression methods. Overall, this study provides a high-precision, interpretable, and generalizable intelligent solution for predicting tunnel water inflow under complex geological conditions.

隧道涌水的准确预测是保证隧道工程施工安全和风险控制的关键。然而，传统的回归方法面临着样本量有限、数据不平衡、特征交互复杂、工程部署困难等重大挑战。针对这些问题，本研究提出了一种集数据扩充、模型优化、可解释性和在线部署于一体的智能预测框架，并对动态现场条件具有较强的适应性。首先，采用SMOGN欠采样方法对训练数据集进行平衡和扩充，有效扩展稀疏样本，抑制离群值的影响，从而增强模型的泛化能力。随后，通过基于optuna的超参数优化和基于AHP （Analytic Hierarchy Process）的特征权值调整对LightGBM进行改进，形成AHP- op -LightGBM混合模型。该方法将预测误差降低了15.89%，同时将特征权重与物理约束更紧密地对齐。与传统优化策略相比，该模型具有自动超参数整定和特征权值校正的双重机制，具有较好的表征水文地质特征的能力。相关分析和基于shap的可解释性进一步阐明了控制隧道涌水的耦合地质力学-水文过程的非线性协同机制。为了支持工程应用，利用web技术开发了一个云部署的在线预测系统，集成了SHAP以提供透明的决策支持。此外，还包含了一个增量学习模块，以适应动态数据变化。使用一小组局部增量样本进行验证，最大预测误差仅为1.9169 m3/h，显示了不同工程场景的强兼容性和准确性。对比实验表明，与传统回归方法相比，该模型的预测误差平均降低了39.65%，拟合精度平均提高了18.43%。总体而言，本研究为复杂地质条件下的隧道涌水预测提供了高精度、可解释性和通用性的智能解决方案。

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引用次数: 0

Intelligent identification and deformation analysis of subsurface cavities in deep excavations using CNN-based inverse modeling 基于cnn逆建模的深基坑地下空腔智能识别与变形分析

IF 7.4 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2026-01-08 DOI: 10.1016/j.tust.2025.107419

Wei Zhang , Ya-Dong Xue , Jin-Zhang Zhang , Gang Zheng , Zeng-Zhi Qian , Yu-Xin Zhai

Subsurface cavities in soft soil poses significant geotechnical challenges to the rapid expansion of urban underground spaces. This study presents a comprehensive framework for quantifying cavity-induced deformation amplification during deep excavation and develops an intelligent inversion system for cavity characterization using monitoring data. Through 1,800 finite element simulations, parametric analyses reveal that cavities located within 0.5 times the excavation depth (H_e) horizontally from the diaphragm wall and at depths of 1.5 H_e to 2.25 H_e constitute the most critical influence zone, amplifying horizontal wall displacement by up to 1.67 times and ground settlement by up to 2.2 times. K-means clustering analysis identifies five distinct settlement deformation patterns (Modes I–V) strongly correlated with cavity size and location. A convolutional neural network (CNN) based inversion model is developed to predict cavity dimensions and positions from deformation monitoring data, achieving over 85 % accuracy (R² > 0.85) on test datasets. The model demonstrates robust performance under soil parameter uncertainties modeled with random fields, maintaining acceptable prediction accuracy when spatial variability is considered. This integrated framework provides a practical tool for real-time cavity detection and risk mitigation in deep excavation projects within cavity-bearing strata, offering valuable guidance for construction safety management in complex urban geological conditions.

软土地下空腔对城市地下空间的快速扩张提出了重大的岩土工程挑战。本研究提出了一个综合框架来量化深基坑开挖过程中空洞引起的变形放大，并开发了一种利用监测数据进行空洞表征的智能反演系统。通过1800次有限元模拟，参数化分析表明，在距连续墙水平方向0.5倍开挖深度（He）范围内，深度在1.5 ~ 2.25 He范围内的空腔构成了最关键的影响区，使水平墙位移放大1.67倍，地面沉降放大2.2倍。K-means聚类分析确定了5种不同的沉降变形模式（I-V模式），这些模式与空洞的大小和位置密切相关。开发了一种基于卷积神经网络（CNN）的反演模型，从变形监测数据中预测空腔尺寸和位置，在测试数据集上实现了85%以上的精度（R2 > 0.85）。该模型在随机场模拟的土壤参数不确定性下表现出鲁棒性，在考虑空间变异性时保持可接受的预测精度。该集成框架为含空腔地层中深基坑工程的实时空腔探测和风险缓解提供了实用工具，为复杂城市地质条件下的施工安全管理提供了有价值的指导。

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引用次数: 0

Investigation into the stress corrosion behavior of cable bolts under different tensile stresses 不同拉应力作用下锚杆的应力腐蚀行为研究

IF 7.4 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2026-01-08 DOI: 10.1016/j.tust.2026.107452

Yongliang Li , Sheng Zhang , Renshu Yang , Shuaiyang Shi

Cable bolts are essential support materials in underground engineering. While subjected to long-term tensile stress, they are also exposed to harsh corrosive environments. The stress corrosion cracking (SCC) failure problem of cable bolts, caused by the coupling effect of stress and corrosion, is prominent and seriously threatens the safety and stability of underground engineering. To investigate the effect of different tensile stresses on the stress corrosion behavior of cable bolts, stress corrosion tests of cable bolts under different tensile stresses were carried out by using ammonium thiocyanate corrosion solution (NH₄SCN). The variation law of cable bolt SCC failure time was studied, and the macroscopic and microscopic characteristics of cable bolt SCC fracture surface were analyzed. The propagation laws of SCC cracks in cable bolts were obtained, and the influence mechanism of tensile stress on the stress corrosion behavior of cable bolts was revealed. The results indicate that an increase in tensile stress accelerates the SCC process of the cable bolt, and the failure time of the cable bolt SCC is negatively correlated with the stress level. As the stress increases, the range of the crack propagation zone decreases while the range of the overload fracture zone increases, and the crack deflection angle decreases. There are significant differences in the microscopic morphology of the crack initiation zone, crack propagation zone, and overload fracture zone of the cable bolt fracture under different tensile stresses. Tensile stress affects the initiation and propagation of stress corrosion cracks by affecting the passive film and microstructure of the cable bolt, stress intensity factor at the crack tip, diffusion and aggregation of hydrogen atoms in the solution. The greater the tensile stress, the higher the risk of SCC failure of the cable bolt.

锚杆是地下工程中必不可少的支护材料。在经受长期拉伸应力的同时，它们还暴露在恶劣的腐蚀环境中。由于应力与腐蚀的耦合作用，锚杆的应力腐蚀开裂破坏问题十分突出，严重威胁着地下工程的安全与稳定。为研究不同拉应力对电缆螺栓应力腐蚀行为的影响，采用硫氰酸铵腐蚀溶液（NH4SCN）对不同拉应力下的电缆螺栓进行了应力腐蚀试验。研究了锚杆SCC破坏时间的变化规律，分析了锚杆SCC断裂面的宏观和微观特征。获得了锚杆SCC裂纹的扩展规律，揭示了拉应力对锚杆应力腐蚀行为的影响机理。结果表明：拉应力的增大加速了锚杆自裂过程，锚杆自裂破坏时间与应力水平呈负相关；随着应力的增大，裂纹扩展区范围减小，过载断裂区范围增大，裂纹挠度减小。不同拉应力作用下锚杆断裂的裂纹起裂区、裂纹扩展区和过载断裂带的微观形貌存在显著差异。拉应力通过影响锚杆的钝化膜和组织、裂纹尖端的应力强度因子、溶液中氢原子的扩散和聚集等因素影响应力腐蚀裂纹的萌生和扩展。拉应力越大，锚杆SCC破坏风险越高。

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引用次数: 0

Deciphering the time-dependent behavior of underground rock tunnels: Insights from a generalized non-associative thermo-viscoplastic damage model 解读地下岩石隧道的时间依赖行为：来自广义非关联热粘塑性损伤模型的见解

IF 7.4 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2026-01-07 DOI: 10.1016/j.tust.2025.107428

Zhi-Jie Wen , Jian Tao , Yu-Jun Zuo , Xing Zhu

The time-dependent deformation of underground rock tunnels in coupled geopressure and geothermal environments poses significant challenges to sustainable resource extraction and subsurface space utilization. In this study, a novel non-associative thermo-viscoplastic damage model is proposed within the thermodynamic framework for characterizing the rock creep behavior. By integrating the temperature and damage variables directly into the free energy and energy dissipation functions, the derived yield criterion can automatically capture the pressure- and thermally-induced rock hardening/softening response. The proposed model is systematically validated by laboratory triaxial compression and creep tests, and is then applied to investigate the long-term creep performance of underground rock tunnels at different temperatures. The underlying mechanisms responsible for time-dependent tunnel deformation and cracking are quantitatively elucidated through stress-displacement-damage coupling analysis. The calculated results reveal that the surrounding rock in major principal stress directions generally exhibits limited damage but significant displacement, prone to inducing rock extrusion and bulging. In contrast, the tunnel region in minor principal stress directions will experience smaller time-dependent deformation yet severe damage accumulation, rendering it susceptible to localized rock fracturing. Moreover, elevated temperatures are shown to accelerate the creep failure of underground tunnels owing to thermally-induced rock deterioration, and the timely support measures are thus indispensable for ensuring tunnel stability in geothermal settings. The findings of our study are believed to enhance the understanding of time-dependent rock deformation in coupled thermal–mechanical conditions and can thus provide a theoretical basis for guiding the adaptive support design of deep geothermal tunnels.

地压-地热耦合环境下地下岩石隧道的时效变形对资源的可持续开采和地下空间的利用提出了重大挑战。本文在热力学框架内提出了一种新的非关联热粘塑性损伤模型来表征岩石的蠕变行为。通过将温度和损伤变量直接积分到自由能和能量耗散函数中，导出的屈服准则可以自动捕获压力和热致岩石硬化/软化响应。通过室内三轴压缩和蠕变试验对该模型进行了系统验证，并应用于不同温度下地下岩石隧道的长期蠕变特性研究。通过应力-位移-损伤耦合分析，定量地阐明了随时间变化的隧道变形和开裂的潜在机制。计算结果表明，主应力方向上的围岩一般损伤有限，但位移较大，易发生岩石挤压胀形。而在主应力较小的隧洞区域，随时间的变形较小，但损伤积累严重，容易发生局部岩石破裂。此外，高温会加速地下隧道因热致岩石劣化而发生蠕变破坏，及时采取支护措施是保证地热环境下隧道稳定的必要条件。研究结果有助于加深对热-力耦合条件下岩石变形随时间变化的认识，为指导深部地热隧道的自适应支护设计提供理论依据。

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引用次数: 0

An integrated analytical approach for predicting structural performance and cracking behavior in composite linings of deep hydraulic tunnels 深层水工隧道复合衬砌结构性能与开裂行为预测的综合分析方法

IF 7.4 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2026-01-07 DOI: 10.1016/j.tust.2025.107429

Xueming Zhang , Zhiyun Deng , Baoguo Liu , Xiang Ma

Safety and serviceability of deep hydraulic tunnels are threatened by lining cracks, while links between mechanical response and key metrics, such as capacity, cracking load, and crack width, are rarely established under staged construction and complex loading, limiting performance-based design and risk-informed decisions. Therefore, an integrated analytical model grounded in complex-variable theory is developed to evaluate the stress field, ultimate bearing capacity, cracking load, and crack width, with staged construction explicitly considered. The model is validated against 3D numerical simulations (WMAPE ≤ 5.82 %, R² > 0.993) and physical model tests (WMAPE ≤ 18.95 %, R² > 0.919). A threshold effect of secondary-lining installation timing is revealed, contributing 10–25 % to ultimate capacity and markedly influencing cracking behavior. Internal water pressure is identified as the dominant driver of cracking (contribution >52 %), whereas ultimate capacity is governed primarily by the interaction between in situ stress and construction timing (contribution >80 %). A response-surface-based design envelope has been developed to optimize lining thickness and support installation timing for crack control, offering a tool for selecting the optimal secondary lining thickness and support timing under specified burial depths and internal water pressures. By linking mechanical response analysis to performance criteria under realistic construction and loading sequences, a key methodological gap is closed and performance-based design and evaluation of deep hydraulic tunnels are enabled. The approach provides a transparent, computationally efficient alternative to computationally intensive simulations and offers quantitative guidance for crack-control design.

深层水工隧道的安全性和可使用性受到衬砌裂缝的威胁，而机械响应与关键指标（如容量、裂缝载荷和裂缝宽度）之间的联系，在阶段施工和复杂荷载下很少建立起来，这限制了基于性能的设计和风险决策。因此，建立了基于复变量理论的综合分析模型，明确考虑了阶段施工的应力场、极限承载力、裂缝荷载和裂缝宽度。通过三维数值模拟（WMAPE≤5.82%,R2 > 0.993）和物理模型试验（WMAPE≤18.95%,R2 > 0.919）对模型进行了验证。二次衬砌时间存在阈值效应，对极限承载力的贡献为10 ~ 25%，对开裂行为有显著影响。内部水压被认为是裂缝的主要驱动因素（贡献>； 52%），而最终承载力主要由原位应力和施工时间之间的相互作用（贡献>； 80%）决定。研究人员开发了一种基于响应面的设计包线，用于优化衬砌厚度和支架安装时间，以控制裂缝，为在特定埋深和内部水压下选择最佳二次衬砌厚度和支架安装时间提供了一种工具。通过将力学响应分析与实际施工和加载顺序下的性能标准联系起来，缩小了关键的方法差距，使基于性能的深水水工隧道设计和评估成为可能。该方法提供了一种透明的、计算效率高的替代计算密集型模拟方法，并为裂缝控制设计提供了定量指导。

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引用次数: 0

A damage constitutive model of gas-bearing coal under pre-static loading and cyclic impact 含气煤在预静载荷和循环冲击作用下的损伤本构模型

IF 7.4 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2026-01-06 DOI: 10.1016/j.tust.2025.107421

Wei Wang , Hanpeng Wang , Xinyuan Xie , Zicheng Wang , Weibing Cai , Yunhao Wu , Yuguo Zhou

The increasing depth of coal mining has led to more severe dynamic disasters, such as coal and gas outbursts, under complex environments characterized by high in-situ stress, elevated gas pressure, and cyclic excavation-induced disturbances. However, existing damage constitutive models rarely comprehensively consider the combined effects of static loading, gas, and cyclic impact. To address this, a novel time-dependent damage constitutive model for gas-bearing coal under pre-static loading and cyclic impact is developed in this study. Based on the framework of the generalized Kelvin model, the elastic elements are replaced with damage elements. Following the strain equivalence principle, a coupling damage factor integrating static loading and gas effects is derived. The cyclic impact-induced damage, considering strain rate effects, is represented by a parallel configuration of a damage element and a viscous element. Meanwhile, an inverted S-shaped cyclic impact damage factor is established based on the inverse logistic function, effectively capturing the three-stage damage evolution (initial rapid increase, stabilization, and subsequent acceleration) by incorporating the effects of impact number, frequency, and peak amplitude. Numerical simulations of a coal and gas outburst induced by roadway excavation with cyclic disturbance are conducted using the proposed model. The results demonstrate consistency with physical simulations under identical conditions regarding stress evolution, gas pressure variation, and outburst cavity location, confirming the validity and applicability of the proposed damage constitutive model. The proposed model can accurately capture the inherent laws of damage evolution of gas containing coal under complex loads, providing a theoretical tool for understanding and predicting dynamic disasters induced by cyclic disturbances in deep mining.

随着煤层开采深度的增加，在高地应力、高瓦斯压力、循环开挖扰动等复杂环境下，煤与瓦斯突出等动力灾害更加严重。然而，现有的损伤本构模型很少全面考虑静载荷、气体和循环冲击的联合作用。为了解决这一问题，本文建立了含气煤在预静载荷和循环冲击作用下的时效损伤本构模型。在广义开尔文模型框架下，将弹性单元替换为损伤单元。根据应变等效原理，导出了静载荷和气体作用的耦合损伤系数。考虑应变率效应的循环冲击损伤由损伤单元和粘性单元的平行结构表示。同时，基于logistic逆函数建立倒s型循环冲击损伤因子，结合冲击次数、频率和峰值幅值的影响，有效地捕捉了损伤演化的三个阶段（初始快速增加、稳定和随后加速）。利用该模型对具有循环扰动的巷道开挖引起的煤与瓦斯突出进行了数值模拟。结果表明，在相同条件下，应力演化、瓦斯压力变化和突出空腔位置与物理模拟结果一致，验证了所建立的损伤本构模型的有效性和适用性。该模型能够准确捕捉复杂载荷下含气煤损伤演化的内在规律，为理解和预测深部开采循环扰动引起的动力灾害提供了理论工具。

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引用次数: 0