Underground Space最新文献

{"title":"Enhanced safety assessment on tunnel excavation via refined rock mass parameter identification","authors":"Hongwei Huang ,&nbsp;Tongjun Yang ,&nbsp;Jiayao Chen ,&nbsp;Zhongkai Huang ,&nbsp;Chen Wu ,&nbsp;Jianhong Man","doi":"10.1016/j.undsp.2024.09.007","DOIUrl":"10.1016/j.undsp.2024.09.007","url":null,"abstract":"<div><div>This study employs computer vision and deep learning techniques to execute the refined extraction and quantification of rock mass information in tunnel faces. The integration of contact measurement data and surrounding environmental parameters leads to a proposal for rock mass quality prediction, utilizing integrated machine learning techniques. Subsequently, a 3D model is established by incorporating tunnel face features and environmental data. The safety factor of rock mass excavation is calculated through the utilization of the strength reduction method, and the analysis of rock mass stability on the continuous tunnel face is performed, considering factors such as rock stress and joint sliding. The investigation of variation patterns of excavation safety factors, influenced by multiple modelling factors, is conducted through the utilization of a response surface design method in 46 experimental studies. The research reveals the accurate characterization of complex fissure occurrence obtained in the field through a discrete fracture network. Furthermore, a negative correlation between the safety factor of tunnel excavation and the grade of surrounding rock is observed, with an increase in grade resulting in a decrease in the safety factor. The response surface method effectively discloses polynomial correlations between various parameters such as inclination angle, dip direction, spacing, density, number of groups, and the safety factor. This elucidates the impact patterns of these parameters and their coupling states on the safety factor. The study provides significant insights into the intelligent evaluation of safety for continuous tunnel excavation.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"24 ","pages":"Pages 142-161"},"PeriodicalIF":8.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725017","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":"TIM-FEM-ML synthetic technology for longitudinal optimization of tunnel excavated in the interlayered rock mass","authors":"Hui Li ,&nbsp;Weizhong Chen ,&nbsp;Xiaoyun Shu ,&nbsp;Xianjun Tan ,&nbsp;Qun Sui","doi":"10.1016/j.undsp.2025.03.001","DOIUrl":"10.1016/j.undsp.2025.03.001","url":null,"abstract":"<div><div>The layout of underground engineering objects significantly<!--> <!-->influences the stability of the surrounding rock mass and construction safety. Despite advancements toward intellectualization and informatization in design optimization and safety assessments, mechanical analysis-based engineering computations still face certain impediments. Consequently, this paper proposes a comprehensive framework integrating tunnel information modelling (TIM), finite element method (FEM) and machine learning (ML) technology to optimize the tunnel longitudinal orientation. It also delves into the specifics of addressing the challenges associated with each technology. The framework encompasses three phases: parametric modelling based on TIM, automatic numerical simulation based on FEM, and intelligent optimization leveraging ML. Initially, geometric models of the geological formations and engineering structures are constructed on the TIM platform. Subsequently, data conversion is facilitated through the proposed transformation interface. Python codes are programmed to realize automatic processing of numerical simulation and results are extracted to the ML algorithm for the prediction model. An optimization algorithm is implanted in the numerical stream file to retrieve the optimal relative intersection angle between the tunnel axis and the trend of rocks. A case study is conducted to evaluate the feasibility of the proposed framework. Results demonstrate a substantial improvement in design and optimization accuracy and efficiency. This framework holds<!--> <!-->immense<!--> <!-->potential to propel the intellectualization and informatization of underground engineering.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"23 ","pages":"Pages 327-342"},"PeriodicalIF":8.2,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518286","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":"Experimental study on the leakage-induced structural collapse of segmental tunnels","authors":"Qihao Sun ,&nbsp;Xian Liu ,&nbsp;Yihai Bao ,&nbsp;Wouter De Corte ,&nbsp;Luc Taerwe","doi":"10.1016/j.undsp.2024.09.006","DOIUrl":"10.1016/j.undsp.2024.09.006","url":null,"abstract":"<div><div>During the construction of segmental tunnels, unexpected leakage poses a significant safety hazard to the tunnel structures, potentially leading to collapse. Worldwide, accidents caused by leakage during the construction of shield tunnels have resulted in substantial losses. However, existing studies have not clearly elucidated the mechanism behind tunnel collapse induced by leakage, making it challenging to propose effective prevention or control measures. To address this issue, a series of model tests on tunnel collapse induced by leakage were designed and conducted. These tests replicated the tunnel collapse process and revealed three stages: seepage erosion, soil cave formation and destabilization, and soil impact. The soil caves develop upward, leading to a redistribution of external pressure on the tunnels. Ultimately, the structural collapse of the tunnel occurs due to soil impact from the unstable soil cave. Comparing tunnel entrance/exit accidents with connecting passage accidents highlights that both accident types share the same underlying mechanism but differ in boundary conditions.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"24 ","pages":"Pages 22-43"},"PeriodicalIF":8.2,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696419","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 of the spatial distribution of tunnel seepage under varying drainage capacities in water-abundant regions","authors":"Qing Xu ,&nbsp;Pengfei Li ,&nbsp;Chongbang Xu ,&nbsp;Siqing Wang ,&nbsp;Sulei Zhang","doi":"10.1016/j.undsp.2025.02.010","DOIUrl":"10.1016/j.undsp.2025.02.010","url":null,"abstract":"<div><div>Effective control of the tunnel seepage field is crucial in water-abundant regions to ensure the safety and stability of underground structures. Therefore, it is imperative to investigate the effects of the geological factors and tunnel permeability parameters on the drainage capacities of such structures. The Tongzi Tunnel was subjected to model tests using a self-developed testing apparatus to investigate the spatial distribution of tunnel seepage under varying conditions of sand permeability, number of primary support layers, and number of primary support openings. Subsequently, numerical models were developed to validate the observed tunnel seepage field based on experimental conditions. On this basis, an effective water pressure ratio <span><math><mrow><mi>η</mi></mrow></math></span> is proposed to evaluate the hydraulic safety of the tunnel spatial distribution. The results indicated a positive correlation between the tunnel water discharge and sand permeability, primary support layers, and primary support openings. Among these factors, the primary support openings exhibited the highest sensitivity to tunnel water discharge, whereas the impact of the primary support layers was relatively low. Additionally, the external water pressure in the tunnel exhibited a negative correlation with sand permeability, primary support layers, and primary support openings. The sensitivity ranking of the structural water pressure fluctuations to the parameters is as follows: primary support openings &gt; sand permeability &gt; primary support layers. Furthermore, the longitudinal water pressure values in the tunnel gradually increase from Section A (circular drainage section) to Section B (middle circular drainage section). Model tests and numerical simulations were performed to demonstrate the data reliability. Finally, with the increase of sand permeability and the number of primary support openings, the effective drainage area (<em>η</em> &lt; 0.6) around the tunnel spatial gradually expands. Besides, the tunnel longitudinal effective drainage interval progressively degrades from the vault (A1 area) to the tunnel bottom (A7 area), and even the tunnel bottom areas are not effectively drained (<em>η</em> &gt; 0.6).</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"23 ","pages":"Pages 343-361"},"PeriodicalIF":8.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523193","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":"Optimizing microseismic sensor networks in underground space using Cramér–Rao Lower Bound and improved genetic encoding","authors":"Yichao Rui ,&nbsp;Jie Chen ,&nbsp;Junsheng Du ,&nbsp;Xiang Peng ,&nbsp;Zelin Zhou ,&nbsp;Chun Zhu","doi":"10.1016/j.undsp.2025.02.009","DOIUrl":"10.1016/j.undsp.2025.02.009","url":null,"abstract":"<div><div>The layout of a sensor network is a critical determinant of the precision and reliability of microseismic source localization. Addressing the impact of sensor network configuration on positioning accuracy, this paper introduces an innovative approach to sensor network optimization in underground space. It utilizes the Cramér-Rao Lower Bound principle to formulate an optimization function for the sensor network layout, followed by the deployment of an enhanced genetic encoding to solve this function and determine the optimal layout. The efficacy of proposed method is rigorously tested through simulation experiments and pencil-lead break experiments, substantiating its superiority. Its practical utility is further demonstrated through its application in a mining process within underground spaces, where the optimized sensor network solved by the proposed method achieves remarkable localization accuracy of 15 m with an accuracy rate of 4.22% in on-site blasting experiments. Moreover, the study elucidates general principles for sensor network layout that can inform the strategic placement of sensors in standard monitoring systems.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"23 ","pages":"Pages 307-326"},"PeriodicalIF":8.2,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518285","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":"Multi-patch attention Transformer for multivariate long-term time series forecasting of TBM excavation parameters","authors":"Mingjun Liu ,&nbsp;Jianqin Liu ,&nbsp;Wei Guo ,&nbsp;Hongxu Liu ,&nbsp;Xiao Guo","doi":"10.1016/j.undsp.2025.02.007","DOIUrl":"10.1016/j.undsp.2025.02.007","url":null,"abstract":"<div><div>To address the research gap in multivariable long-term time series forecasting in the field of tunnel boring machine (TBM) and provide long-term insights for decision-making in TBM construction, this paper studies a novel Transformer-based forecasting model. Leveraging a multi-patch attention mechanism, the newly developed multi-patch attention Transformer (MPAT) model is designed to predict long-term trends of multiple TBM operation parameters. The innovation lies in finding the most relevant time delay series of the input series through autocorrelation calculation, and designing a multi-patch attention mechanism to replace the traditional attention mechanism of Transformer, so that the model can capture local and global information of the series and improve the accuracy of long-term prediction of high-frequency and weakly periodic TBM data. Experimental results have shown that MPAT model has a significant effect on capturing TBM data in terms of temporal dependencies. In a case study, we applied MPAT to the Rongjiang Guanbu Water Diversion Project in Guangdong Province and predicted four excavation parameters. The experimental results show that MPAT exhibits accurate predictive ability when the input length is 36 and the outputs are 12, 24, 48, and 72, respectively. In comparison with some state-of-the-art models, MPAT outperforms MSE by 19.1%, 23.6%, 36.4%, and 48.3%, respectively. We also discussed the impact of input length and the number of patches on performance, and found that each prediction length has the best input length corresponding to it, and longer inputs don’t represent more accurate predictions. The determination of the number of patches should also depend on the input length, as too many or too few patches can affect the capture of local information in the sequence.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"23 ","pages":"Pages 285-306"},"PeriodicalIF":8.2,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314593","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":"Heterogeneity of field cemented rockfill at a Canadian hard-rock mine","authors":"Chong Wei ,&nbsp;Derek B. Apel ,&nbsp;Huawei Xu ,&nbsp;Jun Wang ,&nbsp;Krzysztof Skrzypkowski","doi":"10.1016/j.undsp.2025.02.008","DOIUrl":"10.1016/j.undsp.2025.02.008","url":null,"abstract":"<div><div>This study presents uniaxial and triaxial compression tests on large-scale cemented rockfill (CRF) core samples from a Canadian hard-rock mine. Stress–strain curves indicate heterogeneity in strength and deformation properties at various depths. Segregation causes uneven cement and aggregate distribution, affecting uniaxial compressive strength, which decreases with proximity to the discharge point. Findings confirm CRF column strength variability, aiding stability assessment and optimization.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"23 ","pages":"Pages 279-284"},"PeriodicalIF":8.2,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314592","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":"Passive instability of longitudinally inclined shallowly-buried shield tunnel using physical model tests and DEM simulations","authors":"Zhiwang Lu ,&nbsp;Youlin Ye ,&nbsp;Pengpeng Ni ,&nbsp;Zijie Qian ,&nbsp;Ben Niu ,&nbsp;Shijian Shang","doi":"10.1016/j.undsp.2025.02.006","DOIUrl":"10.1016/j.undsp.2025.02.006","url":null,"abstract":"<div><div>Stability of tunnel face is crucial, but previous studies often overlooked the effect of longitudinal tunnel inclination, leading to inaccurate stability assessments. In this study, nine groups of 1<em>g</em> model tests were conducted to study the influence of longitudinal tunnel inclination on passive limit support pressure and passive failure mode of soil in front of the tunnel face under shallow burial conditions (i.e., cover depth ratio of 0.25, 0.50 and 0.75) in a sand stratum. In addition, discrete element method (DEM) analyses at the same scale were established and calibrated against the model test results. Accordingly, the micromechanical information of soil was derived from a microscopic perspective. The results indicate that upon the passive instability of tunnel face, the soil in front of the tunnel face firstly moved approximately perpendicular to the tunnel face, and then it deflected. The instability area of soil in front of the tunnel face increased with the decrease of longitudinal inclination, when the tunnel cover depth was fixed. Furthermore, microscopic analyses indicate that the longitudinal inclination could significantly affect the soil contact orientation in front of the tunnel face. This was more likely to cause the failure zone to rotate.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"23 ","pages":"Pages 258-278"},"PeriodicalIF":8.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306450","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":"AI-aided short-term decision making of rockburst damage scale in underground engineering","authors":"Chukwuemeka Daniel ,&nbsp;Shouye Cheng ,&nbsp;Xin Yin ,&nbsp;Zakaria Mohamed Barrie ,&nbsp;Yucong Pan ,&nbsp;Quansheng Liu ,&nbsp;Feng Gao ,&nbsp;Minsheng Li ,&nbsp;Xing Huang","doi":"10.1016/j.undsp.2025.02.005","DOIUrl":"10.1016/j.undsp.2025.02.005","url":null,"abstract":"<div><div>Rockbursts pose severe risks to underground engineering projects, including mining and tunnelling, where sudden rock failures can lead to substantial infrastructure damage and loss of human lives. An accurate assessment of rockburst damage is essential for safety and effective risk mitigation. This study investigates the effectiveness of ensemble machine learning models optimized through Bayesian optimization (BO) in predicting rockburst damage scales. Nine classifier algorithms, including random forest (RF), were evaluated using a dataset of 254 samples. The research considered factors such as stress conditions, support system capacity, excavation span, geological characteristics, seismic magnitude, peak particle velocity, and rock density as input variables. The rockburst damage scale, categorized into four severity levels based on displaced rock mass, served as the target variable. Among the models evaluated, BO-RF model demonstrated the highest predictive accuracy and generalization capability, achieving 92% testing accuracy. BO-RF model also ranked top in a multi-criteria evaluation framework. This devised ranking system underscores the importance of evaluating model performance on both training and unseen testing data to ensure robust generalization. The findings underscore the effectiveness of BO-RF in enhancing rockburst risk assessment and providing reliable predictive insights for underground engineering applications.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"23 ","pages":"Pages 362-378"},"PeriodicalIF":8.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563661","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 excavation-induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurements","authors":"Cheng Chen ,&nbsp;Guan-Nian Chen ,&nbsp;Song Feng ,&nbsp;Xiao-Zhen Fan ,&nbsp;Liang-Tong Zhan ,&nbsp;Yun-Min Chen","doi":"10.1016/j.undsp.2025.02.004","DOIUrl":"10.1016/j.undsp.2025.02.004","url":null,"abstract":"<div><div>Monitoring lateral displacement in deep excavation projects is crucial for structural stability and safety. Traditional methods, like manual inclinometers, are accurate but costly and labor-intensive. Automated systems provide real-time data but face challenges with dense sensor placement and high costs. This study presents a novel prediction method using an extreme learning machine (ELM) optimized by an improved particle swarm optimization (IPSO) algorithm. The IPSO-ELM approach utilizes sparse automated measurements to accurately predict lateral displacement profiles, minimizing the need for dense sensor deployment. A case study of a 30.2-m-deep excavation project in Hangzhou, China, demonstrates the method’s effectiveness. The results demonstrate that the IPSO-ELM model maintains high prediction accuracy, with low root mean square error (RMSE) and mean absolute error (MAE) values, even under conditions of sparse sensor placement. Across the entire test dataset, with a sensor spacing of 5.0 m, the model achieved maximum RMSE values ranging from 0.94 to 2.79 mm and maximum MAE values ranging from 0.77 to 2.18 mm, thereby showcasing its robustness and reliability in predicting lateral displacement. A detailed discussion was conducted on the errors associated with various sensor spacing intervals when implementing the proposed method. This study underscores the potential of IPSO-ELM as a cost-effective and reliable tool for automatic monitoring in increasingly complex urban excavation projects.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"23 ","pages":"Pages 125-145"},"PeriodicalIF":8.2,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221368","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}