Pub Date : 2025-06-08DOI: 10.1016/j.undsp.2025.02.009
Yichao Rui , Jie Chen , Junsheng Du , Xiang Peng , Zelin Zhou , Chun Zhu
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.
{"title":"Optimizing microseismic sensor networks in underground space using Cramér–Rao Lower Bound and improved genetic encoding","authors":"Yichao Rui , Jie Chen , Junsheng Du , Xiang Peng , Zelin Zhou , 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}
Pub Date : 2025-06-03DOI: 10.1016/j.undsp.2025.02.007
Mingjun Liu , Jianqin Liu , Wei Guo , Hongxu Liu , Xiao Guo
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.
{"title":"Multi-patch attention Transformer for multivariate long-term time series forecasting of TBM excavation parameters","authors":"Mingjun Liu , Jianqin Liu , Wei Guo , Hongxu Liu , 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}
Pub Date : 2025-06-02DOI: 10.1016/j.undsp.2025.02.008
Chong Wei , Derek B. Apel , Huawei Xu , Jun Wang , Krzysztof Skrzypkowski
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.
{"title":"Heterogeneity of field cemented rockfill at a Canadian hard-rock mine","authors":"Chong Wei , Derek B. Apel , Huawei Xu , Jun Wang , 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}
Pub Date : 2025-05-30DOI: 10.1016/j.undsp.2025.02.006
Zhiwang Lu , Youlin Ye , Pengpeng Ni , Zijie Qian , Ben Niu , Shijian Shang
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 1g 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.
{"title":"Passive instability of longitudinally inclined shallowly-buried shield tunnel using physical model tests and DEM simulations","authors":"Zhiwang Lu , Youlin Ye , Pengpeng Ni , Zijie Qian , Ben Niu , 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}
Pub Date : 2025-05-29DOI: 10.1016/j.undsp.2025.02.005
Chukwuemeka Daniel , Shouye Cheng , Xin Yin , Zakaria Mohamed Barrie , Yucong Pan , Quansheng Liu , Feng Gao , Minsheng Li , Xing Huang
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.
{"title":"AI-aided short-term decision making of rockburst damage scale in underground engineering","authors":"Chukwuemeka Daniel , Shouye Cheng , Xin Yin , Zakaria Mohamed Barrie , Yucong Pan , Quansheng Liu , Feng Gao , Minsheng Li , 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}
Pub Date : 2025-05-19DOI: 10.1016/j.undsp.2025.02.004
Cheng Chen , Guan-Nian Chen , Song Feng , Xiao-Zhen Fan , Liang-Tong Zhan , Yun-Min Chen
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.
{"title":"Predicting excavation-induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurements","authors":"Cheng Chen , Guan-Nian Chen , Song Feng , Xiao-Zhen Fan , Liang-Tong Zhan , 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}
Vast amounts of valuable historical tunnelling site investigation data remain underutilized due to inefficient content-based archiving and searching tools. This study introduces a novel data-driven framework that integrates transfer learning with reverse image search to revolutionize the utilization of historical data in tunnelling projects. The method indexes excavated tunnel sections with corresponding tunnel face images and identifies similarities between projects based on geological features. Transfer learning with pre-trained deep learning models is employed to compress tunnel face images into compact, lower-dimensional vectors, enabling efficient similarity searches. This transformation converts geological information into comparable vectors, enhancing the efficiency and speed of data searches. An online cloud service is developed to allow engineers to access similar historical projects in real-time. To enhance the quality of the compressed vectors, this study developed a multi-level feature extraction method. This method markedly improves the deep learning models’ ability to accurately identify major features from rock images. When applied to a diverse range of tunnel excavation projects in China, the model exhibited an impressive accuracy of over 90% in retrieving projects with similar geological features. This underscores the model’s potential as a robust tool for enhancing data management and decision-making in tunnelling engineering.
{"title":"Enhancing data reuse in tunnelling site investigation through transfer learning-based historical data mining","authors":"Jiawei Xie , Baolin Chen , Shui-Hua Jiang , Hongyu Guo , Si Xie , Jinsong Huang","doi":"10.1016/j.undsp.2025.02.003","DOIUrl":"10.1016/j.undsp.2025.02.003","url":null,"abstract":"<div><div>Vast amounts of valuable historical tunnelling site investigation data remain underutilized due to inefficient content-based archiving and searching tools. This study introduces a novel data-driven framework that integrates transfer learning with reverse image search to revolutionize the utilization of historical data in tunnelling projects. The method indexes excavated tunnel sections with corresponding tunnel face images and identifies similarities between projects based on geological features. Transfer learning with pre-trained deep learning models is employed to compress tunnel face images into compact, lower-dimensional vectors, enabling efficient similarity searches. This transformation converts geological information into comparable vectors, enhancing the efficiency and speed of data searches. An online cloud service is developed to allow engineers to access similar historical projects in real-time. To enhance the quality of the compressed vectors, this study developed a multi-level feature extraction method. This method markedly improves the deep learning models’ ability to accurately identify major features from rock images. When applied to a diverse range of tunnel excavation projects in China, the model exhibited an impressive accuracy of over 90% in retrieving projects with similar geological features. This underscores the model’s potential as a robust tool for enhancing data management and decision-making in tunnelling engineering.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"23 ","pages":"Pages 161-174"},"PeriodicalIF":8.2,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231792","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}
Pub Date : 2025-05-11DOI: 10.1016/j.undsp.2025.02.002
Honggan Yu , Yin Bo , Quansheng Liu , Xuhui Yang , Shuzhan Xu , Xing Huang
Accurately estimating the monthly advance rate of hard rock tunnel boring machine is of great significance for construction method selection, machine type determination, and project planning. However, current researches mainly focus on estimating the advance rate during construction, and few studies can estimate the advance rate from the entire tunnel scale. To overcome above shortcomings, a monthly advance rate estimation method based on rock mass classification and data augmentation is proposed. Firstly, 56 cases of tunnel boring machine are collected, and proportions of all rock mass grades in basic quality system of the entire tunnel are selected as main inputs of the model. Then, a two-stage data augmentation method based on synthetic minority over-sampling technique and modified auxiliary classifier generative adversarial network is developed. Finally, monthly advance rate estimation models based on machine learning and augmented datasets are established. The results show that the proposed method can accurately estimate the monthly advance rate and the data augmentation method can significantly augment the dataset. The average accuracy of the models is improved by 44.82% after data augmentation. Extreme gradient boosting model performs the best, with an accuracy of 90.31%. Therefore, the proposed method can accurately estimate the monthly advance rate of tunnel boring machine from the tunnel scale and has essential academic and engineering value.
{"title":"Monthly advance rate estimation of hard rock tunnel boring machine based on rock mass classification and data augmentation","authors":"Honggan Yu , Yin Bo , Quansheng Liu , Xuhui Yang , Shuzhan Xu , Xing Huang","doi":"10.1016/j.undsp.2025.02.002","DOIUrl":"10.1016/j.undsp.2025.02.002","url":null,"abstract":"<div><div>Accurately estimating the monthly advance rate of hard rock tunnel boring machine is of great significance for construction method selection, machine type determination, and project planning. However, current researches mainly focus on estimating the advance rate during construction, and few studies can estimate the advance rate from the entire tunnel scale. To overcome above shortcomings, a monthly advance rate estimation method based on rock mass classification and data augmentation is proposed. Firstly, 56 cases of tunnel boring machine are collected, and proportions of all rock mass grades in basic quality system of the entire tunnel are selected as main inputs of the model. Then, a two-stage data augmentation method based on synthetic minority over-sampling technique and modified auxiliary classifier generative adversarial network is developed. Finally, monthly advance rate estimation models based on machine learning and augmented datasets are established. The results show that the proposed method can accurately estimate the monthly advance rate and the data augmentation method can significantly augment the dataset. The average accuracy of the models is improved by 44.82% after data augmentation. Extreme gradient boosting model performs the best, with an accuracy of 90.31%. Therefore, the proposed method can accurately estimate the monthly advance rate of tunnel boring machine from the tunnel scale and has essential academic and engineering value.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"23 ","pages":"Pages 175-192"},"PeriodicalIF":8.2,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240618","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}
Pub Date : 2025-05-08DOI: 10.1016/j.undsp.2025.02.001
Raymond L. Sterling
For thousands of years, humans have used the underground for many purposes and we are now in an era when such uses are becoming more important to support our living patterns, our material needs and to improve the sustainability of our way of life. Many underground facilities serve their intended function well and have proven to have long lifetimes. Some have not been so successful for a variety of reasons or have been retired as no longer meeting the original purpose and not being suitable for conversion to another purpose. While the difference between success and failure is often tied to the specifics of a particular project, this paper seeks to extract some of the general principles that underlie the benefits or drawbacks of different types of underground space uses and how to maximize “success”. The paper is a mixture of the general and the specific because both play a role in success. The paper draws significantly from a recent study of the “lessons learned” from 42 worldwide underground facilities with an average of over 37 years of service mixed with other observations by the author from a career of studying underground space use and underground construction technologies.
{"title":"Minimizing problems and maximizing benefits from underground space use","authors":"Raymond L. Sterling","doi":"10.1016/j.undsp.2025.02.001","DOIUrl":"10.1016/j.undsp.2025.02.001","url":null,"abstract":"<div><div>For thousands of years, humans have used the underground for many purposes and we are now in an era when such uses are becoming more important to support our living patterns, our material needs and to improve the sustainability of our way of life. Many underground facilities serve their intended function well and have proven to have long lifetimes. Some have not been so successful for a variety of reasons or have been retired as no longer meeting the original purpose and not being suitable for conversion to another purpose. While the difference between success and failure is often tied to the specifics of a particular project, this paper seeks to extract some of the general principles that underlie the benefits or drawbacks of different types of underground space uses and how to maximize “success”. The paper is a mixture of the general and the specific because both play a role in success. The paper draws significantly from a recent study of the “lessons learned” from 42 worldwide underground facilities with an average of over 37 years of service mixed with other observations by the author from a career of studying underground space use and underground construction technologies.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"23 ","pages":"Pages 3-22"},"PeriodicalIF":8.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147359","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}
Pub Date : 2025-05-03DOI: 10.1016/j.undsp.2025.04.001
Hehua Zhu
{"title":"Introduction to the second Jeme Tien Yow Lecture","authors":"Hehua Zhu","doi":"10.1016/j.undsp.2025.04.001","DOIUrl":"10.1016/j.undsp.2025.04.001","url":null,"abstract":"","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"23 ","pages":"Pages 1-2"},"PeriodicalIF":8.2,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068422","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}
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