Tunnelling and Underground Space Technology最新文献

{"title":"Mode-Dependent optimal positioning of evacuation Guides: An Experimental–Modeling study on static and dynamic guidance effect","authors":"Weisong Liu ,&nbsp;Jun Zhang ,&nbsp;Rui Ba ,&nbsp;Weiguo Song","doi":"10.1016/j.tust.2026.107458","DOIUrl":"10.1016/j.tust.2026.107458","url":null,"abstract":"<div><div>The guides can help pedestrians find exits, thereby improving evacuation efficiency and reducing casualties in emergency. This study investigates how guides affect crowd evacuations via controlled experiments and modeling. To investigate the influence of guide on crowd evacuation, a Social Force Model with Guidance (SFMG) has been established by embedding the guidance term into the social force framework. The interaction mechanism between a guide and pedestrians was studied by performing crowd movement experiments under different guidance modes (dynamic/static), movement speeds and crowd densities. The guidance attraction force formula involving the above variables has been proposed. It is revealed that the guidance attraction field is influenced by the guidance mode and speed. Subsequently, the simulations in a subway platform under varying visibility conditions were conducted and the influence of the initial layout of guides on crowd evacuation efficiency was studied. The results revealed that arranging guides in the areas far from the exit can facilitate the crowd evacuation. The distance between the guide’s initial position and exit was denoted as <em>D.</em> in the simulation analyses. A variable <em>DR</em> (distance ratio) calculated by the ratio of <em>D</em> to the length of the platform subzone was adopted to quantity guide’s initial position. Dynamic guide: The higher <em>DR</em> results in the shorter evacuation time under low visibility. But the optimal initial position of guide shifts slightly closer to the exit with increasing visibility. Static guide: The U-shaped relationship between evacuation time and <em>DR</em> is observed. The optimal position falls within the 40 %∼60 % <em>DR</em>. These findings are helpful to design indoor emergency guidance plan, and the optimal positioning rules are transferable to common building layouts.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107458"},"PeriodicalIF":7.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent prediction of surface settlement troughs induced by twin shields tunnelling: Insights from a numerical modelling-empirical formulation-interpretable automated machine learning fusion method","authors":"Chengwen Wang,&nbsp;Xiaoli Liu,&nbsp;Weiqiang Xie,&nbsp;Yanlin Su,&nbsp;Yingtong Ju","doi":"10.1016/j.tust.2026.107449","DOIUrl":"10.1016/j.tust.2026.107449","url":null,"abstract":"<div><div>The construction of twin shield tunnels has become increasingly prevalent in densely populated urban areas. Accurately predicting the surface settlement induced by twin-shield tunnelling is of great significance for risk mitigation and refined settlement control. This study proposes a novel intelligent approach that integrates numerical modelling, empirical formula, and automated machine learning (AutoML) to predict surface settlement troughs induced by twin-shield tunnelling. Using a well-validated numerical model that considered 11 input parameters (including geological, geometric, and operational factors), 2000 settlement trough datasets were generated through numerical modelling. Subsequently, an improved superposition method was applied to extract six characteristic control parameters of the settlement troughs, thereby constructing a high-quality dataset. A multi-output AutoML model was then developed to predict the control parameters of the twin-tunnel-induced settlement troughs. Compared with six conventional machine learning models and two classical ensemble strategies, the AutoML model exhibited superior predictive accuracy and generalization capability, achieving average <em>R</em>² values of 0.9977 and 0.9835 for the training and test sets, respectively. The Shapley Additive Explanations (SHAP) method was employed to analyze the interpretability of the AutoML model. The results highlight the significant influence of construction parameters (e.g., tunnelling contraction ratio) on the maximum settlement, as well as the regulatory effects of geometric parameters (tunnel diameter, burial depth, and twin-tunnel spacing) on the shape of the settlement trough, thereby providing valuable guidance for design optimization and precise construction control. Finally, the proposed AutoML model was validated using five real-world engineering cases, where the predicted settlement troughs closely matched the measured data, thereby confirming the robustness, reliability, and practical applicability of the model and demonstrating its promising potential for engineering practice.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107449"},"PeriodicalIF":7.4,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transferable prediction of TBM long-distance tunneling construction duration considering uncertainties in surrounding rock distribution and the evolution of tunneling efficiency","authors":"Jianming Zhang ,&nbsp;Kebin Shi ,&nbsp;Peixuan Lin ,&nbsp;Lei Li ,&nbsp;Qiyong Mao ,&nbsp;Haibo Jiang ,&nbsp;Xinjun Yan ,&nbsp;Jingwei Gong","doi":"10.1016/j.tust.2026.107446","DOIUrl":"10.1016/j.tust.2026.107446","url":null,"abstract":"<div><div>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 (<em>e</em>) 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 <em>e</em>, 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 <em>e</em> reached 0.25–0.40, which significantly impacted construction duration. Furthermore, introducing <em>e</em> 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.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107446"},"PeriodicalIF":7.4,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting disc cutter forces for hard rock TBM cutterhead modeling: a comparative analysis of modified CSM semi-theoretical model and hybrid deep learning approach","authors":"Mohammad Matin Rouhani ,&nbsp;Jamal Rostami","doi":"10.1016/j.tust.2026.107448","DOIUrl":"10.1016/j.tust.2026.107448","url":null,"abstract":"<div><div>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 &lt; 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.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107448"},"PeriodicalIF":7.4,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New sensing-inversion integrated method for mechanical behavior analysis of shield tunnels during heavy rainfall","authors":"Haoran Wang ,&nbsp;Chengchao Guo ,&nbsp;DingFeng Cao ,&nbsp;Jin Tang ,&nbsp;Fuming Wang","doi":"10.1016/j.tust.2025.107441","DOIUrl":"10.1016/j.tust.2025.107441","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107441"},"PeriodicalIF":7.4,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-physics integration model for predicting tunnel convergence subject to water level fluctuations and lining structure degradation","authors":"Junjie Liu ,&nbsp;Qing Ai ,&nbsp;Lulu Zhang ,&nbsp;Junyi Zhu ,&nbsp;Hui Wang ,&nbsp;Xingchun Huang ,&nbsp;Yong Yuan","doi":"10.1016/j.tust.2025.107440","DOIUrl":"10.1016/j.tust.2025.107440","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107440"},"PeriodicalIF":7.4,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable and adaptive tunnel water inflow prediction method using data augmentation and AHP-Enhanced OP-LightGBM","authors":"Jingqi Cui ,&nbsp;Shunchuan Wu ,&nbsp;Haiyong Cheng ,&nbsp;Xiaowei Hou ,&nbsp;Jiaxin Wang ,&nbsp;Weihua Liu ,&nbsp;Chaoqun Chu","doi":"10.1016/j.tust.2026.107445","DOIUrl":"10.1016/j.tust.2026.107445","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107445"},"PeriodicalIF":7.4,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent identification and deformation analysis of subsurface cavities in deep excavations using CNN-based inverse modeling","authors":"Wei Zhang ,&nbsp;Ya-Dong Xue ,&nbsp;Jin-Zhang Zhang ,&nbsp;Gang Zheng ,&nbsp;Zeng-Zhi Qian ,&nbsp;Yu-Xin Zhai","doi":"10.1016/j.tust.2025.107419","DOIUrl":"10.1016/j.tust.2025.107419","url":null,"abstract":"<div><div>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 (<em>H</em>_e) horizontally from the diaphragm wall and at depths of 1.5 <em>H</em>_e to 2.25 <em>H</em>_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 &gt; 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.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107419"},"PeriodicalIF":7.4,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation into the stress corrosion behavior of cable bolts under different tensile stresses","authors":"Yongliang Li ,&nbsp;Sheng Zhang ,&nbsp;Renshu Yang ,&nbsp;Shuaiyang Shi","doi":"10.1016/j.tust.2026.107452","DOIUrl":"10.1016/j.tust.2026.107452","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107452"},"PeriodicalIF":7.4,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering the time-dependent behavior of underground rock tunnels: Insights from a generalized non-associative thermo-viscoplastic damage model","authors":"Zhi-Jie Wen ,&nbsp;Jian Tao ,&nbsp;Yu-Jun Zuo ,&nbsp;Xing Zhu","doi":"10.1016/j.tust.2025.107428","DOIUrl":"10.1016/j.tust.2025.107428","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107428"},"PeriodicalIF":7.4,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}