Tunnelling and Underground Space Technology最新文献

{"title":"Cross-system modeling and analysis of cascading failure propagation in large-scale metro stations under extreme flooding events","authors":"Hai-Yun Li ,&nbsp;Dong-Mei Zhang ,&nbsp;Huan-Feng Duan ,&nbsp;Xu-Wei Zhao","doi":"10.1016/j.tust.2026.107483","DOIUrl":"10.1016/j.tust.2026.107483","url":null,"abstract":"<div><div>Climate-driven intensification of extreme precipitation is significantly increasing unprecedented flooding risks for underground transportation infrastructure. Current flood risk assessment approaches for large-scale metro stations, characterized by extensive subsystem integration, high passenger volumes, and complex spatial configurations, often fail to capture critical cross-system interactions. This limitation arises mainly from the artificial separation of simulations using computational fluid dynamics (CFD) from cross-system network analysis, which hinders the accurate prediction of cascading infrastructure failures. This study develops an integrated framework that combines CFD simulations with multi-layer network theory to concurrently analyze flood dynamics and system interdependencies. This framework models four critical subsystems of large-scale metro stations, including power, drainage, communication, and pedestrian, as interconnected networks based on established engineering standards. Flood-depth-dependent functions determine infrastructure node states, with thresholds calibrated from engineering standards to ensure physical consistency. The validations with the Shanghai Eastern Hub during a 500-year rainfall event (327 mm over 6 h, with a peak intensity of 90 mm/h) demonstrate that power systems exhibit the highest vulnerability, with functionality declining to 51.5% within 60 min. Furthermore, an analysis of 13,241 cascading failure events reveals that 67.3% are driven by water depth, while 32.7% are influenced by inter-system dependencies. Network analysis uncovers a critical importance-vulnerability paradox: power systems, serving as the network backbone with the highest importance score (0.446), simultaneously exhibit disproportionately elevated vulnerability (0.172) compared to other subsystems. The developed framework incorporates minute-level temporal coupling, validated to capture the dominant characteristics of infrastructure responses while maintaining computational tractability for engineering applications.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107483"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071958","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":"Advance prediction of rock mass classification in tunneling using improved D-S fusion and hybrid machine learning","authors":"Shikuo Chen ,&nbsp;Yifan Hou ,&nbsp;Rui Wang ,&nbsp;Xiaoyan Zhao","doi":"10.1016/j.tust.2026.107453","DOIUrl":"10.1016/j.tust.2026.107453","url":null,"abstract":"<div><div>Accurate prediction of rock mass classification is imperative for optimizing safety and cost-efficiency in underground tunnel engineering. Despite its critical importance, conventional single-classification models often exhibit limitations in robustness and accuracy, hindering reliable risk assessment and design optimization. To overcome these persistent challenges, this study proposes a multi-model fusion framework grounded in D-S evidence theory, significantly enhancing classification reliability. Furthermore, an LSTM-based model is developed for ahead-of-face rock class prediction, leveraging geological data from excavated sections. Utilizing 325 field case datasets, unsupervised learning and SMOTE preprocessing were applied, with t-SNE visualization confirming markedly enhanced feature separability. Based on seven key geological indicators, five predictive models spanning classical rock mass rating systems and data-driven machine learning methods were established. These outputs were fused via a D-S evidence theory framework, significantly enhancing classification robustness. Furthermore, hyperparameters of the BP and RF models were optimized via global search algorithms to enhance base classifiers performance. Building upon their test-set metrics, we propose a refinement of the Basic Probability Assignment (BPA) function by integrating precision and accuracy. This modified BPA is adopted as the fusion index with an improved D-S evidence theory framework, establishing a robust rock mass classification model. Validated across three tunnels, the improved D-S model achieved 89.13% accuracy—outperforming all base classifiers. The integrated LSTM predictor further demonstrated robustness to temporal parameter variations. This integrated approach effectively mitigates single-model instability, significantly boosting classification accuracy and robustness. Crucially, its short-range ahead-of-face predictive capability enables proactive support design, enhancing tunnel construction safety.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107453"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001005","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":"Model tests and discrete element simulations on the cracking characteristics of layered soft rock tunnel under different bedding features","authors":"Wenbo Yang ,&nbsp;Haoyu Li ,&nbsp;Changhui Zhao ,&nbsp;Fangyin Wu ,&nbsp;Liangliang Zhao","doi":"10.1016/j.tust.2026.107454","DOIUrl":"10.1016/j.tust.2026.107454","url":null,"abstract":"<div><div>The angle and direction of bedding have a significant influence on the cracking and damage observed in tunnels excavated through layered soft rock. To investigate the cracking characteristics of tunnel lining structures under varying bedding conditions, this study initially conducted layered soft rock tunnel cracking tests. By analyzing the acoustic emission signals, lining deformation, and evolution of mechanical behavior during the loading process, the spatial distribution of lining cracking locations was determined for different bedding characteristics. Based on this, the cracking characteristics and crack patterns of the lining and surrounding rock were revealed using discrete element simulation. The study results indicated that bedding planes in their normal orientation represented a high-risk area for lining cracking due to stress concentration and inward compression of the lining, which resulted in the rupture of contact force chains and the initiation of cracks. Besides, the bedding characteristics exerted a significant impact on the crack pattern of the tunnel lining. Among these, the crack pattern of the lining evolved from tension/compression to shear, and then back to tension/compression, with deformation and internal forces showing an increasing and then decreasing trend, reaching a peak at an angle of 45°. As the bedding direction increased, cracks transitioned from a dispersed distribution to longitudinal penetration, while shear failure became more pronounced. The deformation and internal forces continued to increase, and the tunnel’s bearing capacity was weakest at a direction of 90°. This study systematically uncovers the failure mechanisms of layered soft rock tunnels, offering direct support for optimizing tunnel support parameters and a theoretical basis for maintaining existing tunnels.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107454"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048428","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":"Structural fire behavior of tunnel sections: assessing the effects of full burnout and spalling effects","authors":"Amin Emadi ,&nbsp;Nima Tajik ,&nbsp;Alexandre Gomes ,&nbsp;Negar Elhami-Khorasani","doi":"10.1016/j.tust.2026.107461","DOIUrl":"10.1016/j.tust.2026.107461","url":null,"abstract":"<div><div>This paper outlines a study to evaluate the structural response of a reinforced concrete (RC) tunnel lining subjected to a full-burnout RABT ZTV (train) fire using a coupled thermo-mechanical finite element model that simultaneously accounts for soil-structure interaction, cooling phase of fire, and concrete spalling, aspects often neglected in performance evaluations. A simplified, rate-based spalling model to bound outcomes (no-spalling maps to lower bound; spalling maps to upper bound) is implemented, and damage is classified using four indicators: reinforcement temperature, depth of concrete above 300°C, residual displacements, and cracking potential on the soil-facing side. Results show that spalling increases heat penetration and shifts damage class: circumferential rebar peaks at about 650°C with spalling versus about 400°C without; the heated-concrete depth above 300°C increases from 75 mm to 105 mm; and peak steel temperature occurs during cooling, underscoring the need to model the cooling phase. Soil stiffness mainly affects residual crown displacements (dense: 5 mm; loose: 9 mm) but does not change damage class for the considered case study, and no cracking was found on the unexposed side. The framework supports post-fire assessment and performance-based design of tunnel linings where spalling risk is non-negligible, acknowledging the use of a uniform spalling representation with a 2D plane-strain model.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107461"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014890","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":"Groundwater inflow into tunnels: semi-empirical methods for estimating steady state inflow associated with excavation induced drawdown","authors":"Simone L. Markus,&nbsp;Mark S. Diederichs","doi":"10.1016/j.tust.2025.107430","DOIUrl":"10.1016/j.tust.2025.107430","url":null,"abstract":"<div><div>Estimation of groundwater inflow is essential for tunnel design and construction; however, analytical solutions used in current engineering practice have limited applicability, especially in cases where drawdown of the water table occurs due to excavation associated drainage. Existing methods for estimating steady state groundwater inflow under a drawn-down water table require improvement, as they typically assume fixed water table boundaries. This article unites and compares classical inflow formulations and reviews their applicability and limitations. Based on numerical modelling, the study proposes two novel methods for estimating inflow into tunnels based on drawdown: one for inflow into shallow tunnels where full drawdown of the water table occurs (trench-like inflow), and one for inflow into moderately deep tunnels, where partial drawdown of the water table occurs. The inflow-drawdown relationship devised accounts for the influence of tunnel size and depth on drawdown shape, and is independent of assumptions of boundary conditions, which limit applicability of other equations. Additionally, practical guidance for numerical modelling of tunnels below the water table is provided, including sensitivity analysis of boundary conditions.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107430"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903168","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":"Installation timing and deformation prediction of multi-layer supporting structures for deep-buried and soft-rock tunnels under high geo-stress","authors":"Zihan Zhou ,&nbsp;Bingyan Wang ,&nbsp;Wei Meng ,&nbsp;Keping Zhang ,&nbsp;Ziquan Chen ,&nbsp;Yuanfu Zhou ,&nbsp;Yuanming Lai","doi":"10.1016/j.tust.2026.107487","DOIUrl":"10.1016/j.tust.2026.107487","url":null,"abstract":"<div><div>Multi-layer supporting structures have excellent flexibility in dynamically regulating the stress release of surrounding rock. Therefore, accurately determining the installation timing and combination scheme is of great significance for preventing and controlling severe large squeezing deformation. To systematically sort out the principles for determining the installation timing, the evolution law of mechanical mechanism of the double-layer primary supports in Maoxian tunnel was analyzed. A theoretical analytical method for determining installation timing was proposed by combining the viscoelastic-plastic creep constitutive model, which considers the damage tensor of layered rock mass, with the elastoplastic model during the construction influence period and the failure model of the supporting structure. Based on the continuity requirement of deformation under a multi-layer supporting system, an artificial intelligence algorithm was used to establish a multi-level deformation prediction model, assisting in determining the installation timing. The results indicated that for continuous medium surrounding rock and large squeezing deformation, the concept of flexible support followed by strong support and appropriately delaying the installation timing, can significantly reduce the surrounding rock pressure. By adopting the above concept, the maximum surrounding rock pressure of Maoxian tunnel can be reduced from 950.7 kPa to 665.9 kPa. The applicability of the proposed theoretical analysis method and multi-level deformation prediction model was verified through engineering examples. The maximum analytical solution error of the average surrounding rock pressure in Maoxian tunnel was only 41.2 kPa, while the average deformation prediction error was only 8.3%.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107487"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071955","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":"Plastic-hardening constitutive model-based hybrid machine learning framework for three-dimensional tunnel deformation prediction","authors":"Siyu Yin ,&nbsp;Zheng Yang ,&nbsp;Kunpeng Cao ,&nbsp;Ben Wu ,&nbsp;Siau Chen Chian","doi":"10.1016/j.tust.2025.107415","DOIUrl":"10.1016/j.tust.2025.107415","url":null,"abstract":"<div><div>Accurately predicting tunnel deformation induced by excavation is critical for ensuring urban underground safety and optimizing reinforcement schemes. This study proposes a hybrid machine-learning framework that integrates particle swarm optimization (PSO), convolutional neural networks (CNN), and extreme gradient boosting (XGBoost). A large-scale database is generated through finite-difference analyses using a Plastic-Hardening (PH) soil model encompassing 242 excavation scenarios and 29,282 monitoring points that cover diverse excavation-tunnel configurations. The proposed PSO-CNN-XGBoost hybrid model demonstrates high predictive accuracy (R² = 0.96, RMSE = 0.91 mm, MAE = 0.63 mm), outperforming standalone CNN and XGBoost models. Shapley Additive Explanations (SHAP) analysis identifies dimensionless parameters (Δ<em>H</em>/<em>H</em>, <em>D/B</em>, and <em>Y/L</em>) as the dominant geometric drivers and quantifies interaction thresholds that are valuable for design control. A closed-form predictive expression derived via symbolic regression enables rapid screening of tunnel deformation zones. The proposed framework offers an efficient solution for point-level assessment of excavation-induced tunnel deformation, supporting low-disturbance development of underground space.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107415"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928417","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":"Influence of inclined groove on rock-crushing behavior of TBM cutter","authors":"Xuhui Zhang ,&nbsp;Zeren Peng ,&nbsp;Hanwen Lai ,&nbsp;Shenghui Kang ,&nbsp;Yashi Liao ,&nbsp;Yimin Xia","doi":"10.1016/j.tust.2025.107404","DOIUrl":"10.1016/j.tust.2025.107404","url":null,"abstract":"<div><div>To investigate the impact of groove angle on rock-crushing behavior of a TBM cutter, the finite element method was employed to simulate both rock cutting and rock-crushing processes. Then, the average vertical load, average rolling load, and specific energy required for rock-crushing by TBM cutter were calculated and analyzed under varying groove angles and groove spacings. Furthermore, some rock-crushing tests were performed to show the cutter’s crushing behavior regarding the groove angle and groove spacing. The study’s results indicate that the effectiveness of crack propagation to the groove is influenced by the groove angle and groove spacing. Specifically, for the given groove angle, when the groove spacing remains at a low value, the cracks produced can effectively extend to grooves. However, when the groove spacing surpasses a certain threshold, the cracks fail to sufficiently reach the grooves. This threshold is referred to as the critical groove spacing, which varies with different groove angles. Notably, as the groove angle increases, the critical groove spacing also tends to increase. Furthermore, when the two cutting grooves can facilitate rock crushing, a rise from 0° to 60° in the groove angle results in a decrease in both the cutter’s vertical load and rolling load. Additionally, the specific energy initially falls and then rises with the growth of the groove angle. An optimal groove spacing that minimizes the specific energy exists for a certain groove angle. In particular, the optimal groove spacings at groove angles of 0°, 15°, 30°, 45°, and 60° are 70 mm, 70 mm, 80 mm, 80 mm, and 90 mm, respectively. Notably, when the grooves with different groove angles and groove spacings can provide an auxiliary crushing effect, the crushing load of the cutter is minimized at a groove angle of 60°, while the specific energy of the cutter reaches its lowest point at a groove angle of 30°.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107404"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928421","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":"2026-05-01","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":"Study on the characteristics of recirculating smoke flow in dead-end tunnel fire based on full-scale experiments","authors":"Shunyu Yue ,&nbsp;Le Wu ,&nbsp;Zejian Lu ,&nbsp;Pingyu Zhang ,&nbsp;Peng Hu ,&nbsp;Junxian Xie ,&nbsp;Maohua Zhong","doi":"10.1016/j.tust.2025.107396","DOIUrl":"10.1016/j.tust.2025.107396","url":null,"abstract":"<div><div>With the acceleration of the construction of underground space engineering in China, the number of single-ended tunnels in the construction process has increased year by year. In order to study the smoke spread characteristics in a single-ended tunnel formed during the construction phase of a long distance tunnel, a full-scale field experiment was carried out in the construction section of Shengli Tunnel in Tianshan Mountain. By analyzing the overall temperature distribution, wind speed distribution, smoke layer height and other parameters, combined with field observation, the law of smoke diffusion and settlement in the single-ended tunnel was studied. The temperature prediction models of different diffusion directions are given. The results show that:(1) Under natural ventilation conditions, the diffusion velocity of flue gas towards the closed end is slower than that towards the connected end, and the temperature of flue gas in the connected end is faster than that at the closed end. (2) At the connected end of the ceiling, the maximum flue gas temperature distribution basically conforms to the classical exponential decay model. (3) While at the closed end, the flue gas temperature distribution can be regarded as the superposition of the two parts of the flue gas flow due to the phenomenon of recirculating flue gas flow.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107396"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033343","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}