Tunnelling and Underground Space Technology最新文献

{"title":"A quantitative prediction method for water inflow based on FDIP-derived hydraulic conductivity in tunnels","authors":"Lichao Nie, Yu Zhou, Yuancheng Li, Zhi-qiang Li, Wei Zhou, Yue Xiao","doi":"10.1016/j.tust.2026.108082","DOIUrl":"https://doi.org/10.1016/j.tust.2026.108082","url":null,"abstract":"Water inrush is one of the most hazardous events during tunnel construction and may lead to severe economic losses, construction delays, and casualties. Accurate prediction of tunnel-face water inflow remains challenging because groundwater boundaries are strongly affected by surface topography, while hydraulic conductivity ahead of the tunnel face is difficult to constrain using conventional methods. To address this problem, this study proposes a quantitative water inflow prediction method that integrates full-decay induced polarization (FDIP)-derived hydraulic conductivity with a multiscale three-dimensional seepage model. The main methodological contribution of this study lies in the coupling of a topography-driven regional groundwater model with a refined local seepage model ahead of the tunnel face. In this framework, the regional model provides physically constrained hydraulic-head boundaries, while FDIP inversion provides spatially heterogeneous hydraulic conductivity for the local seepage model. First, a conceptual hydrogeological model was established by integrating geological, hydrogeological, and topographic information, and a large-scale regional groundwater model was constructed to reproduce the background hydraulic-head field. Second, a refined three-dimensional seepage model covering the 30 m zone ahead of the tunnel face was developed. Third, FDIP measurements were conducted at the tunnel face, and the relaxation-time distribution obtained from inversion was converted into hydraulic conductivity using a site-specific relationship calibrated by laboratory tests on tunnel-core samples. The calibrated relationship yielded a correlation coefficient of R<ce:sup loc=\"post\">2</ce:sup> = 0.8829, indicating good agreement between FDIP relaxation time and hydraulic conductivity. Finally, the FDIP-derived hydraulic conductivity field and the hydraulic heads transferred from the regional model were incorporated into the refined seepage model to predict tunnel-face water inflow. The proposed method was applied to a diversion tunnel in southwestern China. The predicted results identified a high-conductivity anomaly between DLIBK0 + 855 and DLIBK0 + 865, with a maximum hydraulic conductivity of approximately 1.49 m/d. This anomalous interval corresponded well to the section where field measurements showed a sharp increase in water inflow during excavation. The results demonstrate that the proposed framework can effectively capture water-bearing anomalies ahead of the tunnel face and reproduce the observed variation trend of tunnel water inflow. By integrating topography-controlled regional groundwater conditions with FDIP-resolved local hydraulic heterogeneity, the proposed method provides a practical and field-oriented framework for short-range quantitative water inflow prediction in tunnel engineering.","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"18 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2026-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148883971","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":"Abrupt disturbance effect and treatment of large-section multi-arch tunnel under biased pressure: Based on field investigation","authors":"Jinxing Lai, Jiawei Xu, Peilong Yuan, Guanhua Cui, Yibo Zhang","doi":"10.1016/j.tust.2026.108057","DOIUrl":"https://doi.org/10.1016/j.tust.2026.108057","url":null,"abstract":"Large-section multi-arch tunnels in biased ground are prone to instability accidents. Based on rock mechanics theory, field monitoring, this study investigates the abrupt disturbance effects and instability mechanisms during the construction of large-section twin-arch tunnels under biased pressure conditions which is an interesting phenomenon. The results indicate that biased pressure and abrupt disturbance effects lead to uneven distribution of surrounding rock pressure, resulting in localized stress concentration. The failure process of the tunnel structure involves the collapse of initial support, load transfer to steel arches, instability of steel frames, and eventual tunnel collapse. The expansion of the loosening circle caused by the excavation of the rear tunnel is the medium for the transmission of the abrupt disturbance effect. An abrupt disturbance model for large-section multi-arch tunnels under complex construction sequences was established, and the sensitivity of various construction parameters was calculated. Based on the findings, a “three-in-one” coordinated control system was proposed, incorporating surface anchor cables combined with base-stabilizing bolt and innovative support structures to fully utilize the suspension effect of anchor cables. The effectiveness of the new collaborative support system was verified through model tests. This case study provides valuable insights for similar large-section multi-arch tunnel projects.","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"27 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884015","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":"Physics-Informed online characterization of Mixed-Face geology along tunnel alignments integrating Multi-Source information","authors":"Penghui Lin, Siyu Lu, Chao Shi","doi":"10.1016/j.tust.2026.108063","DOIUrl":"https://doi.org/10.1016/j.tust.2026.108063","url":null,"abstract":"Mixed-face conditions pose significant challenges for mechanized tunnelling due to asymmetric ground-machine interaction and significant stratigraphic variability. Conventional geological models based on site investigation data alone often fail to capture localized mixed-ground conditions, while pure data-driven approaches lack geological consistency and physical interpretability. To address these limitations, this study proposes a physics-informed online framework for mixed-face characterization along tunnel alignments by integrating geological prior knowledge and real-time TBM operational data. A probabilistic prior geological model is first constructed from sparse borehole logs using a distance-based representation with quantified uncertainty. An Online Hidden Markov Model is then employed to sequentially demarcate homogeneous and mixed-face geological states by fusing established prior information with ring-wise TBM responses. Subsequently, a physics-informed variational autoencoder is introduced to estimate continuous soil-rock proportions within the tunnel diameter, enabling refined mixed-face characterization under physical constraints. Application to a Singapore metro tunnelling project demonstrates that the proposed framework can identify mixed-face zones that cannot be resolved by site investigation data alone, improve soil-rock interface prediction accuracy, and provide stable and interpretable online geological refinement. The results highlight the practical value of integrating geological priors and TBM data for real-time decision support in complex tunnelling conditions.","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"136 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884014","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":"Coupled thermo-mechanical simulation of lining cracking evolution and sealing system mechanical response in CAES lined rock caverns using finite-discrete element method","authors":"Zhangxing Wang ,&nbsp;Jiao Wang ,&nbsp;Guanhua Sun ,&nbsp;Shan Lin ,&nbsp;Zhijun Liu ,&nbsp;Hong Zheng","doi":"10.1016/j.tust.2026.107460","DOIUrl":"10.1016/j.tust.2026.107460","url":null,"abstract":"<div><div>Lined rock caverns (LRCs) have become a key underground solution for large-scale compressed air energy storage (CAES). Clarifying the lining’s cracking pattern is a prerequisite for achieving coordinated performance with the sealing layer. This study proposes a coupled thermo-mechanical numerical framework based on the finite-discrete element method, which can effectively predict the random cracking process and crack evolution patterns of the lining. The accuracy and applicability of the proposed framework are verified through comparison with results from laboratory model tests. Finally, an engineering-scale model is constructed to investigate the effects of factors such as thermal effects, surrounding rock stiffness, and reinforcement parameters on the cracking characteristics and mechanical performance of the lining-sealing system. Results show that thermally induced circumferential compression offsets tensile stresses caused by internal pressure, reducing the maximum crack width and the steel liner stress amplitude by approximately 30%. Surrounding rock stiffness governs deformation compatibility: a higher elastic modulus suppresses plastic zone expansion, significantly reduces cracking, and improves the stress uniformity of the steel liner. Reinforcement factors (including reinforcement type, bar diameter, and spacing) have a limited effect on crack development and overall stress in the steel liner but influence the uniformity of stress in the sealing layer. Lining thickness exhibits a dual effect: thicker linings generate fewer but wider cracks, whereas thinner linings produce more but narrower cracks. The proposed framework provides a reliable theoretical and engineering basis for safety assessment and design optimization of LRCs in CAES applications.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"172 ","pages":"Article 107460"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098893","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":"Impact of aftershocks on the response of a post-mainshock damaged metro station structure in seismic subsidence site","authors":"Zhong-Liang Zhang ,&nbsp;Zhen-Dong Cui ,&nbsp;Pengpeng He ,&nbsp;Ronald Y.S. Pak","doi":"10.1016/j.tust.2026.107456","DOIUrl":"10.1016/j.tust.2026.107456","url":null,"abstract":"<div><div>This study investigates the impact of aftershocks on the seismic response of a post-mainshock damaged metro station structure, with a particular focus on the complex dynamic characteristics of seismic subsidence sites. A three-dimensional finite element model was developed to replicate the collapse evolution of a post-mainshock damaged metro station under aftershocks. The results show that under strong mainshocks, the aftershock-induced displacement increment ratio can reach 1.37. Even following a weak mainshock, aftershocks can trigger approximately 40% additional site subsidence. The structural uplift increment ratio decreases with increasing aftershock intensity ratio, with an average value of about 4.4%. The EPWP increment ratio can reach up to 2.4 during aftershocks. Notably, the damage evolution of metro stations exhibits a mainshock threshold effect, i.e., stronger mainshocks lead to earlier damage initiation, with damage ratios exceeding 30%. Critically, aftershocks can exacerbate the damage, forming pervasive damage zones. Importantly, the inter-story drift shows a positive correlation with the damage ratio, surrounding soil displacement increment ratio, and EPWP increment ratio. A modified damage index is proposed to accurately evaluate structural damage under mainshock-aftershock sequences. The findings provide a valuable reference for the seismic design and post-earthquake rescue of metro stations in urban soft soil areas.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107456"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962609","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":"A damage constitutive model of gas-bearing coal under pre-static loading and cyclic impact","authors":"Wei Wang ,&nbsp;Hanpeng Wang ,&nbsp;Xinyuan Xie ,&nbsp;Zicheng Wang ,&nbsp;Weibing Cai ,&nbsp;Yunhao Wu ,&nbsp;Yuguo Zhou","doi":"10.1016/j.tust.2025.107421","DOIUrl":"10.1016/j.tust.2025.107421","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107421"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928418","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":"2026-05-01","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":"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":"2026-05-01","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":"Dynamic prediction of surrounding rock grades in TBM tunnels based on physics–data dual-driven model","authors":"Kang Fu ,&nbsp;Yiguo Xue ,&nbsp;Daohong Qiu ,&nbsp;Fanmeng Kong ,&nbsp;Jianning Wang","doi":"10.1016/j.tust.2025.107311","DOIUrl":"10.1016/j.tust.2025.107311","url":null,"abstract":"<div><div>Accurate and dynamic identification of surrounding rock grades in TBM tunnels is crucial for ensuring excavation safety and improving construction efficiency. This study proposes a hybrid modeling method based on a physics-data dual-driven approach to achieve high-precision dynamic identification of surrounding rock grades. First, the Isolation Forest model is employed to eliminate outliers from the raw tunneling data, and the key tunneling parameters influencing rock grades are identified using mutual information. Then, the Seasonal and Trend decomposition using LOESS (STL) model is used to perform multimodal decomposition on the dominant tunneling parameters, obtaining the corresponding trend, periodic, and residual components. Subsequently, an Improved Refined Composite Multiscale Sample Entropy (IRCMSE) model is adopted to calculate the feature entropy of each component, forming a dynamic sample database for the data-driven model. Based on this, an improved Convolutional Neural Network – Long Short-Term Memory (CNN-LSTM) model is developed to realize data-driven dynamic identification of TBM tunnel strata. Furthermore, a variation identification formula for surrounding rock grades was proposed based on the principle of geological continuity, enabling physics-driven dynamic identification of surrounding rock grades in TBM tunnels. On this basis, a fusion method combining the physical-driven model and the data-driven model is proposed. The constructed physics-data dual-driven model achieves average precision, recall, F1-score, and accuracy of 98.29 %, 97.98 %, 98.13 %, and 98.30 %, respectively, representing an average improvement of 2.17 % over the data-driven model and 15.07 % over the physical-driven model. Engineering validation results indicate that the overall performance of the model decreases by only 1.74 % and 5.29 % under similar and different geological conditions, respectively, demonstrating strong generalization and robustness, and meeting the requirements of intelligent TBM tunneling under complex geological conditions.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107311"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897494","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":"Optimized CNN-BiLSTM-Attention with hybrid signal denoising: a novel interpretable framework for prediction of shield tunneling advance speed","authors":"Wei Jin ,&nbsp;Kangping Gao ,&nbsp;Chengyao Liu","doi":"10.1016/j.tust.2026.107471","DOIUrl":"10.1016/j.tust.2026.107471","url":null,"abstract":"<div><div>To address the challenges of insufficient prediction accuracy and poor stability of shield tunneling advance speed (AS), this study proposes an intelligent prediction framework based on deep learning. First, a comprehensive data preprocessing strategy is applied, integrating boxplot-based outlier removal, sliding-window smoothing, and a hybrid denoising method combining ensemble empirical mode decomposition (EEMD) with sample entropy-weighted wavelet thresholding. This strategy effectively corrects raw monitoring data, enhances stationarity and signal-to-noise ratio, with its efficacy confirmed through ablation experiments. Subsequently, four key input features are selected from multi-source TBM operational parameters using Pearson correlation analysis. Building upon this, a novel CNN-BiLSTM-Attention model is constructed by synergistically integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM) networks, and an attention mechanism. This architecture facilitates the collaborative extraction of local spatial features and the modeling of long-term temporal dependencies. Furthermore, the Optuna framework is introduced for automated hyperparameter optimization to configure the model structure. Results demonstrate that the optimized model achieves significant performance improvements: the coefficient of determination (R²) and variance accounted for (VAF) increase from 0.84 to 0.94, while the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) decrease by 0.61, 0.49, and 1.42%, respectively. Compared to benchmark models such as GA-LightGBM, CNN-LSTM, and XGBoost, the proposed model demonstrates superior performance, with R² and VAF improving by at least 0.11, and RMSE, MAE, and MAPE decreasing by at least 0.63, 0.33, and 1.13%, respectively. The proposed model also slightly outperforms more advanced Transformer models. SHAP interpretability analysis confirms the validity of the feature selection and quantifies parameter contributions, identifying cutterhead penetration and torque as the most influential factors for advance speed prediction. Overall, the proposed model demonstrates stable and superior performance in terms of prediction accuracy and generalization capability.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107471"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071730","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}