Pub Date : 2025-09-16DOI: 10.1016/j.undsp.2025.05.004
Chuangang Fan , Xiaoxian Fei , Maozhen Liu , Jiayi Ha , Linbo Du , Zhi Li , Yuhao Li , Dia Luan
The development of traffic networks in mountainous areas has led to an increasing number of tunnels being constructed in regions of high geothermal activity. This study examined the effects of geothermal temperature, heat release rate, and fire source location on temperature distribution and smoke movement in construction tunnel fires through a series of scaled-down experiments. Results showed that geothermal conditions heat the air, creating layered flow within construction tunnels. The temperature and velocity of the induced airflow along the tunnel length were characterized. The upper airflow caused by geothermal conditions hinders the spread of smoke toward the tunnel face, resulting in a complex thermal stratification phenomenon. A model for predicting the smoke diffusion length upstream of the fire source was developed, considering geothermal temperature, heat release rate, and fire source location. Additionally, the ceiling temperature distribution was analyzed, showing that downstream temperature decay is insensitive to fire location, while upstream temperature decay can be divided into geothermal-affected and non-affected zones based on the fire source position. Prediction models for the ceiling temperature distribution upstream and downstream were established, respectively. These findings enhance the understanding of smoke dynamics in construction tunnel fires under high geothermal conditions.
{"title":"Effects of geothermal temperature on smoke dynamics in construction tunnel fires","authors":"Chuangang Fan , Xiaoxian Fei , Maozhen Liu , Jiayi Ha , Linbo Du , Zhi Li , Yuhao Li , Dia Luan","doi":"10.1016/j.undsp.2025.05.004","DOIUrl":"10.1016/j.undsp.2025.05.004","url":null,"abstract":"<div><div>The development of traffic networks in mountainous areas has led to an increasing number of tunnels being constructed in regions of high geothermal activity. This study examined the effects of geothermal temperature, heat release rate, and fire source location on temperature distribution and smoke movement in construction tunnel fires through a series of scaled-down experiments. Results showed that geothermal conditions heat the air, creating layered flow within construction tunnels. The temperature and velocity of the induced airflow along the tunnel length were characterized. The upper airflow caused by geothermal conditions hinders the spread of smoke toward the tunnel face, resulting in a complex thermal stratification phenomenon. A model for predicting the smoke diffusion length upstream of the fire source was developed, considering geothermal temperature, heat release rate, and fire source location. Additionally, the ceiling temperature distribution was analyzed, showing that downstream temperature decay is insensitive to fire location, while upstream temperature decay can be divided into geothermal-affected and non-affected zones based on the fire source position. Prediction models for the ceiling temperature distribution upstream and downstream were established, respectively. These findings enhance the understanding of smoke dynamics in construction tunnel fires under high geothermal conditions.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"25 ","pages":"Pages 1-18"},"PeriodicalIF":8.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227709","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}
Earth pressure balance (EPB) shield tunneling in sandy cobble strata often encounters challenges such as muck stagnation, severe tool wear, difficulties in chamber pressure control, and low excavation efficiency. To address these issues, this study proposes a novel gradient stress construction strategy based on rigid wall boundaries by integrating the finite difference method (FDM) and the discrete element method (DEM), and establishes a refined FDM-DEM coupled shield tunneling model. Using this model, the pressure distribution and load transfer mechanisms at the excavation face and within the chamber, as well as the motion trajectories, velocities, and spatial distribution of muck particles, are analyzed in detail. The results indicate that: (1) The pressure at the cutterhead spokes is lower than that at the cutterhead openings; the muck pressure within the chamber exhibits significant radial gradient variations, with distinct differences between the left and right sides. (2) The average pressure in the upper regions of both the left and right sides of the chamber is nearly equal, with a stable pressure transmission coefficient of approximately 0.8. An under-pressure advancement strategy is recommended to avoid ground heave. (3) The muck particles follow spiral trajectories, forming dual-vortex stagnation zones in the central region of the cutterhead (0–0.2D, where D denotes the cutterhead diameter) and the support column region of the chamber (0–0.25D). The installation of radial mixing rods on the cutterhead shaft is suggested to improve muck flowability. This study provides new insights for optimizing cutterhead and chamber design, offering significant implications for enhancing the efficiency of shield tunneling construction.
{"title":"Spatial motion patterns and force transmission characteristics of muck particles in EPB shield tunneling: An FDM-DEM coupling analysis","authors":"Yuxiang Yao, Yong Fang, Chuan He, Gongyun Xu, Zhigang Yao, Xiongyu Hu","doi":"10.1016/j.undsp.2025.05.005","DOIUrl":"10.1016/j.undsp.2025.05.005","url":null,"abstract":"<div><div>Earth pressure balance (EPB) shield tunneling in sandy cobble strata often encounters challenges such as muck stagnation, severe tool wear, difficulties in chamber pressure control, and low excavation efficiency. To address these issues, this study proposes a novel gradient stress construction strategy based on rigid wall boundaries by integrating the finite difference method (FDM) and the discrete element method (DEM), and establishes a refined FDM-DEM coupled shield tunneling model. Using this model, the pressure distribution and load transfer mechanisms at the excavation face and within the chamber, as well as the motion trajectories, velocities, and spatial distribution of muck particles, are analyzed in detail. The results indicate that: (1) The pressure at the cutterhead spokes is lower than that at the cutterhead openings; the muck pressure within the chamber exhibits significant radial gradient variations, with distinct differences between the left and right sides. (2) The average pressure in the upper regions of both the left and right sides of the chamber is nearly equal, with a stable pressure transmission coefficient of approximately 0.8. An under-pressure advancement strategy is recommended to avoid ground heave. (3) The muck particles follow spiral trajectories, forming dual-vortex stagnation zones in the central region of the cutterhead (0–0.2<em>D</em>, where <em>D</em> denotes the cutterhead diameter) and the support column region of the chamber (0–0.25<em>D</em>). The installation of radial mixing rods on the cutterhead shaft is suggested to improve muck flowability. This study provides new insights for optimizing cutterhead and chamber design, offering significant implications for enhancing the efficiency of shield tunneling construction.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"25 ","pages":"Pages 132-155"},"PeriodicalIF":8.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334120","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}
Trace recognition is essential for rock discontinuity characterization of tunnel excavation faces. Traditional methods of trace identification based on three-dimensional (3D) point cloud curvatures require manual fine-tuning for curvature detection and lack consistency with orientation grouping results. This paper proposes a new automatic method for trace identification from 3D point cloud. An adaptive vector method based on neighbor assignment is proposed to accurately generate both normal vectors and directional vectors on sharp points. A principal component analysis-based oriented contraction (PWI-OC) method is presented to extract point cloud skeletons with good iterative conformality. A sparse growing method is proposed to generate extensive trace segments. Two rock excavation face cases, from a mining tunnel and a railway tunnel, are adopted for analysis. The significance of adaptive normal vectors is validated for improving the quality of orientation grouping, and the iterative conformality of PWI-OC is validated to generate more accurate and robust trace skeletons than the traditional method. The results show that the proposed method can achieve a more accurate trace identification than traditional methods, consistent with orientation grouping results, robust to overlapping traces, and automates curvature point detection.
{"title":"AOC: An adaptive oriented contraction method for automatic trace recognition of rock tunnel excavation face based on 3D point cloud","authors":"Keshen Zhang , Min Zhang , Lianyang Zhang , Wei Wu","doi":"10.1016/j.undsp.2024.11.005","DOIUrl":"10.1016/j.undsp.2024.11.005","url":null,"abstract":"<div><div>Trace recognition is essential for rock discontinuity characterization of tunnel excavation faces. Traditional methods of trace identification based on three-dimensional (3D) point cloud curvatures require manual fine-tuning for curvature detection and lack consistency with orientation grouping results. This paper proposes a new automatic method for trace identification from 3D point cloud. An adaptive vector method based on neighbor assignment is proposed to accurately generate both normal vectors and directional vectors on sharp points. A principal component analysis-based oriented contraction (PWI-OC) method is presented to extract point cloud skeletons with good iterative conformality. A sparse growing method is proposed to generate extensive trace segments. Two rock excavation face cases, from a mining tunnel and a railway tunnel, are adopted for analysis. The significance of adaptive normal vectors is validated for improving the quality of orientation grouping, and the iterative conformality of PWI-OC is validated to generate more accurate and robust trace skeletons than the traditional method. The results show that the proposed method can achieve a more accurate trace identification than traditional methods, consistent with orientation grouping results, robust to overlapping traces, and automates curvature point detection.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"25 ","pages":"Pages 218-238"},"PeriodicalIF":8.3,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363947","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 : 2025-09-10DOI: 10.1016/j.undsp.2025.05.003
Yu Tian , Hao Chen , Junquan Wen , Abdul Motalleb Qaytmas , Dechun Lu , Xiuli Du
During tunnel excavation, the surrounding soil experiences complex stress redistribution, which is the root cause of the ground deformation and other engineering disasters. Many researchers have studied this issue through numerical simulations, but the results depend on the soil constitutive model and simulation strategy for the excavation process. In this paper, a large-scale laboratory test is conducted using a scaled shield machine, and the three-dimensional stress state of the surrounding soil is measured by a special earth pressure cell. Test data shows that the normal stress components and principal stresses above the crown decrease, and the stress path on the normalized deviatoric plane reaches the failure envelope determined by Matsuoka–Nakai criterion. Due to the misalignment between the stress release direction and principal directions of the geostatic stresses, shear stress is generated in the physical space, which explains the principal stress rotation of the surrounding soil near the shoulder. Near the sidewall, the major principal stress σ1 is vertical and remains basically unchanged, the intermediate principal stress σ2 is along the longitudinal direction and increases when the cutterhead reaches the monitoring section, while the minor principal stress σ3 is along the transversal direction and decreases. On the deviatoric plane, stress paths near the foot and invert have similar development tendencies as those near the shoulder and crown, respectively. Therefore, the influence of the complex stress state on soil behaviours should be considered to provide a reasonable analysis for the tunnel excavation problem.
{"title":"Laboratory test on three-dimensional stress state of the surrounding soil during tunnel excavation","authors":"Yu Tian , Hao Chen , Junquan Wen , Abdul Motalleb Qaytmas , Dechun Lu , Xiuli Du","doi":"10.1016/j.undsp.2025.05.003","DOIUrl":"10.1016/j.undsp.2025.05.003","url":null,"abstract":"<div><div>During tunnel excavation, the surrounding soil experiences complex stress redistribution, which is the root cause of the ground deformation and other engineering disasters. Many researchers have studied this issue through numerical simulations, but the results depend on the soil constitutive model and simulation strategy for the excavation process. In this paper, a large-scale laboratory test is conducted using a scaled shield machine, and the three-dimensional stress state of the surrounding soil is measured by a special earth pressure cell. Test data shows that the normal stress components and principal stresses above the crown decrease, and the stress path on the normalized deviatoric plane reaches the failure envelope determined by Matsuoka–Nakai criterion. Due to the misalignment between the stress release direction and principal directions of the geostatic stresses, shear stress is generated in the physical space, which explains the principal stress rotation of the surrounding soil near the shoulder. Near the sidewall, the major principal stress <em>σ</em><sub>1</sub> is vertical and remains basically unchanged, the intermediate principal stress <em>σ</em><sub>2</sub> is along the longitudinal direction and increases when the cutterhead reaches the monitoring section, while the minor principal stress <em>σ</em><sub>3</sub> is along the transversal direction and decreases. On the deviatoric plane, stress paths near the foot and invert have similar development tendencies as those near the shoulder and crown, respectively. Therefore, the influence of the complex stress state on soil behaviours should be considered to provide a reasonable analysis for the tunnel excavation problem.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"25 ","pages":"Pages 19-32"},"PeriodicalIF":8.3,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334117","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 : 2025-09-05DOI: 10.1016/j.undsp.2025.05.002
Zhenyu Sun, Dingli Zhang, Muyang Li, Huiruo Wu
Tunnelling in layered rock with high geostress can cause large deformation disasters, and the reasonable countermeasures rely on a full understanding of the self-bearing capacity of the surrounding rock. In this article, the structural ring concept was introduced to represent the load-bearing capacity of the horizontal layered surrounding rock, whose formation mechanism and determination method were analyzed. Firstly, the mechanical response characteristics of the horizontal layered surrounding rock due to excavation were analyzed. Based on the stress transfer mechanism, the new concept of the structural ring which is a closed structure with a certain thickness was presented. Taking the stress element as the basic analytical model, the maximum increase ratio of the compressive stress was adopted to characterize the structural ring. Then the determination method and its implementation algorithm of the structural ring boundaries were proposed, based on which the beam-arch property of the layered rock was investigated. A series of model tests were carried out to validate the proposed structural ring concept and its determination method. Parametric studies were conducted to illustrate the effect of geological conditions and tunnel geometry on the position and shape of structural rings. Furthermore, the application of the structural ring concept in support design was discussed. It was found that the structural ring was usually oval-shaped with the major axis direction consistent with the dominant in-situ stress. Rock layers had a significant effect on the structural ring, and the beam-arch property was affected by the interlayers and bedding spacing. The support system was beneficial for the formation of the structural ring, which should be designed to balance the support capacity and the stability of the structural ring.
{"title":"Formation mechanism of the structural ring for tunnels in horizontal layered rock with high geostress","authors":"Zhenyu Sun, Dingli Zhang, Muyang Li, Huiruo Wu","doi":"10.1016/j.undsp.2025.05.002","DOIUrl":"10.1016/j.undsp.2025.05.002","url":null,"abstract":"<div><div>Tunnelling in layered rock with high geostress can cause large deformation disasters, and the reasonable countermeasures rely on a full understanding of the self-bearing capacity of the surrounding rock. In this article, the structural ring concept was introduced to represent the load-bearing capacity of the horizontal layered surrounding rock, whose formation mechanism and determination method were analyzed. Firstly, the mechanical response characteristics of the horizontal layered surrounding rock due to excavation were analyzed. Based on the stress transfer mechanism, the new concept of the structural ring which is a closed structure with a certain thickness was presented. Taking the stress element as the basic analytical model, the maximum increase ratio of the compressive stress was adopted to characterize the structural ring. Then the determination method and its implementation algorithm of the structural ring boundaries were proposed, based on which the beam-arch property of the layered rock was investigated. A series of model tests were carried out to validate the proposed structural ring concept and its determination method. Parametric studies were conducted to illustrate the effect of geological conditions and tunnel geometry on the position and shape of structural rings. Furthermore, the application of the structural ring concept in support design was discussed. It was found that the structural ring was usually oval-shaped with the major axis direction consistent with the dominant in-situ stress. Rock layers had a significant effect on the structural ring, and the beam-arch property was affected by the interlayers and bedding spacing. The support system was beneficial for the formation of the structural ring, which should be designed to balance the support capacity and the stability of the structural ring.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"24 ","pages":"Pages 387-411"},"PeriodicalIF":8.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104697","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 : 2025-09-05DOI: 10.1016/j.undsp.2025.06.001
Fang-Le Peng , Wei-Xi Wang , Yong-Kang Qiao , Chen-Xiao Ma , Yun-Hao Dong
Urban underground space (UUS) development, guided by prudent planning, has emerged as a vital solution to the increasingly complex issues of urban built environments globally. Driven by the growing needs for human-centric urban design, low-carbon development, enhanced urban resilience, and alignment with sustainable development goals, UUS planning is rapidly shifting from experience-based approaches to evidence-based and data-driven methodologies. Yet, the broader landscape of this research field remains ambiguous, with the characteristics and future trajectories of such emerging planning technologies still to be clearly delineated. To this end, this systematic review delves into the burgeoning field of data-informed planning technologies for underground space (DIPTUS), examining how data-driven methods are revolutionizing the planning, design, and management of underground environments. Through a comprehensive bibliometric analysis of 134 articles published from 2014 to 2024, we identified key trends and mapped research themes within DIPTUS. Our narrative synthesis evaluated DIPTUS advancements across three dimensions: sensing and measurement, pattern and model, and planning and governance. The results indicate that DIPTUS exploits diverse data streams to quantitatively analyze UUS development. Utilizing advanced analytical tools such as spatial statistics, machine learning, and causal inference, these technologies uncover utilization patterns and planning optimization strategies. The review also underscores the increasing integration of planning and governance within DIPTUS, merging resource evaluation and demand forecasting, layout planning optimization, development benefits and spatial performance evaluation into a cohesive framework. Enhancements in 3D cadastral systems, innovative management models, and digital twin technologies further bolster this integrated approach. Despite significant strides, challenges in data integration, model complexity, and practical application persist. Lastly, we proposed a visionary framework to address these issues through interdisciplinary research and robust model development, aiming to fully harness DIPTUS’s transformative potential for sustainable, resilient, and human-centered urban environments.
{"title":"Review on data-informed planning for underground space","authors":"Fang-Le Peng , Wei-Xi Wang , Yong-Kang Qiao , Chen-Xiao Ma , Yun-Hao Dong","doi":"10.1016/j.undsp.2025.06.001","DOIUrl":"10.1016/j.undsp.2025.06.001","url":null,"abstract":"<div><div>Urban underground space (UUS) development, guided by prudent planning, has emerged as a vital solution to the increasingly complex issues of urban built environments globally. Driven by the growing needs for human-centric urban design, low-carbon development, enhanced urban resilience, and alignment with sustainable development goals, UUS planning is rapidly shifting from experience-based approaches to evidence-based and data-driven methodologies. Yet, the broader landscape of this research field remains ambiguous, with the characteristics and future trajectories of such emerging planning technologies still to be clearly delineated. To this end, this systematic review delves into the burgeoning field of data-informed planning technologies for underground space (DIPTUS), examining how data-driven methods are revolutionizing the planning, design, and management of underground environments. Through a comprehensive bibliometric analysis of 134 articles published from 2014 to 2024, we identified key trends and mapped research themes within DIPTUS. Our narrative synthesis evaluated DIPTUS advancements across three dimensions: sensing and measurement, pattern and model, and planning and governance. The results indicate that DIPTUS exploits diverse data streams to quantitatively analyze UUS development. Utilizing advanced analytical tools such as spatial statistics, machine learning, and causal inference, these technologies uncover utilization patterns and planning optimization strategies. The review also underscores the increasing integration of planning and governance within DIPTUS, merging resource evaluation and demand forecasting, layout planning optimization, development benefits and spatial performance evaluation into a cohesive framework. Enhancements in 3D cadastral systems, innovative management models, and digital twin technologies further bolster this integrated approach. Despite significant strides, challenges in data integration, model complexity, and practical application persist. Lastly, we proposed a visionary framework to address these issues through interdisciplinary research and robust model development, aiming to fully harness DIPTUS’s transformative potential for sustainable, resilient, and human-centered urban environments.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"26 ","pages":"Pages 257-281"},"PeriodicalIF":8.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694738","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 : 2025-08-26DOI: 10.1016/j.undsp.2025.04.010
Xiao Yuan , Shuying Wang , Tongming Qu , Huanhuan Feng , Pengfei Liu , Junhao Zeng
Muck clogging during shield tunneling often leads to reduced construction efficiency, increased costs and potential safety hazards. Traditional methods for predicting muck clogging primarily rely on the operator’s experience and conventional risk maps, but have limitations in dealing with complex construction conditions. To address these issues, this study presents a Monte-Carlo dropout (MCD)-assisted multi-fidelity neural network (MFNN) framework for effective prediction of muck clogging risk. First, a low-fidelity model is trained based on synthesized data using clogging risk maps. Subsequently, in-situ tunneling data are used as high-fidelity data to train multi-fidelity models. MCD serves to evaluate the uncertainty of the MFNN’s inference, combined with an active learning strategy to refine the low-fidelity model via iterative training of the high-fidelity model. Experimental results show that the MCD-assisted MFNN framework captures clogging features more effectively than traditional machine learning models that use only single-fidelity data, especially in scenarios with imbalanced data. This study provides a viable solution for complex problems in shield tunneling by fully utilizing both experiential knowledge accumulated in engineering practice and field monitoring data, demonstrating the potential of integrating knowledge and data in tackling some challenges that were previously unresolved.
{"title":"Multi-fidelity knowledge inheritance with active querying for data-driven clogging prediction during mechanized tunneling","authors":"Xiao Yuan , Shuying Wang , Tongming Qu , Huanhuan Feng , Pengfei Liu , Junhao Zeng","doi":"10.1016/j.undsp.2025.04.010","DOIUrl":"10.1016/j.undsp.2025.04.010","url":null,"abstract":"<div><div>Muck clogging during shield tunneling often leads to reduced construction efficiency, increased costs and potential safety hazards. Traditional methods for predicting muck clogging primarily rely on the operator’s experience and conventional risk maps, but have limitations in dealing with complex construction conditions. To address these issues, this study presents a Monte-Carlo dropout (MCD)-assisted multi-fidelity neural network (MFNN) framework for effective prediction of muck clogging risk. First, a low-fidelity model is trained based on synthesized data using clogging risk maps. Subsequently, in-situ tunneling data are used as high-fidelity data to train multi-fidelity models. MCD serves to evaluate the uncertainty of the MFNN’s inference, combined with an active learning strategy to refine the low-fidelity model via iterative training of the high-fidelity model. Experimental results show that the MCD-assisted MFNN framework captures clogging features more effectively than traditional machine learning models that use only single-fidelity data, especially in scenarios with imbalanced data. This study provides a viable solution for complex problems in shield tunneling by fully utilizing both experiential knowledge accumulated in engineering practice and field monitoring data, demonstrating the potential of integrating knowledge and data in tackling some challenges that were previously unresolved.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"24 ","pages":"Pages 371-386"},"PeriodicalIF":8.3,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010329","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 : 2025-08-26DOI: 10.1016/j.undsp.2025.07.001
Jiahao Li , Hehua Zhu , Mei Yin
Ground penetrating radar (GPR) has been extensively applied in tunnel engineering for the non-destructive assessment of lining structures. However, the interpretation of GPR images remains a time-consuming and expertise-dependent task. To address this challenge, this study proposes tunnel ground-penetrating radar mask region-based convolutional neural network (T-GPRMask), a deep learning-based instance segmentation model designed for the automated detection of tunnel lining defects and components. By integrating a convolutional block attention module (CBAM) and feature pyramid network (FPN), T-GPRMask enhances multi-scale feature extraction, enabling the detection of small, low-contrast defects that are commonly encountered in GPR images. The model was pretrained on a domain-specific dataset containing a diverse set of GPR images related to underground structures and then fine-tuned on a dataset specifically designed for tunnel inspections. The model achieved recognition accuracies of 83.18%, 88.24%, 92.84%, and 91.56% for detecting poor compactness, voids, steel arch supports, and initial lining thickness, respectively. A comparative study further demonstrated T-GPRMask’s superior performance over traditional models, such as YOLOv7 and RetinaNet. Field experiments on real-world tunnel inspection data validated the model’s high spatial accuracy and highlighted its practical applicability in tunnel maintenance.
{"title":"Deep learning-based segmentation and detection of tunnel lining defects and components from GPR images using T-GPRMask","authors":"Jiahao Li , Hehua Zhu , Mei Yin","doi":"10.1016/j.undsp.2025.07.001","DOIUrl":"10.1016/j.undsp.2025.07.001","url":null,"abstract":"<div><div>Ground penetrating radar (GPR) has been extensively applied in tunnel engineering for the non-destructive assessment of lining structures. However, the interpretation of GPR images remains a time-consuming and expertise-dependent task. To address this challenge, this study proposes tunnel ground-penetrating radar mask region-based convolutional neural network (T-GPRMask), a deep learning-based instance segmentation model designed for the automated detection of tunnel lining defects and components. By integrating a convolutional block attention module (CBAM) and feature pyramid network (FPN), T-GPRMask enhances multi-scale feature extraction, enabling the detection of small, low-contrast defects that are commonly encountered in GPR images. The model was pretrained on a domain-specific dataset containing a diverse set of GPR images related to underground structures and then fine-tuned on a dataset specifically designed for tunnel inspections. The model achieved recognition accuracies of 83.18%, 88.24%, 92.84%, and 91.56% for detecting poor compactness, voids, steel arch supports, and initial lining thickness, respectively. A comparative study further demonstrated T-GPRMask’s superior performance over traditional models, such as YOLOv7 and RetinaNet. Field experiments on real-world tunnel inspection data validated the model’s high spatial accuracy and highlighted its practical applicability in tunnel maintenance.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"25 ","pages":"Pages 281-294"},"PeriodicalIF":8.3,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363944","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 : 2025-08-26DOI: 10.1016/j.undsp.2024.10.006
Chengzhi Xia , Zhenming Shi , Liu Liu , Guangyin Lu , Lin Zhou , Chuanyi Tao , Shaoqiang Meng
Tunnel construction in karst terrain is influenced by water-filled karst caves and stratigraphic layers, which involves failure characteristics of water-resistant structures and complex fluid–solid interaction (FSI) processes. To cope with this challenge, this paper used coupled discontinuous smoothed particle hydrodynamics (CDSPH) method for investigating water inrush of tunnels considering stratigraphic layers and karst cave positions. Hydraulic fracturing test and sliding‑induced impulsive wave test were carried out to verify the accuracy of the CDSPH method. Moreover, a comprehensive analysis of inrush events in the field-scale Qiyeshan (QYS) karst tunnel was conducted, considering different layer dip angles and cave positions on the evolution characteristics of inrush disasters, with quantitative parameters proposed for predicting water/mud inrush from local to overall disaster. The simulation results indicate that CDSPH karst model has been verified to faithfully capture the progressive failure of water-resistant structure during inrush in stratigraphic layers. Water/mud inrush in QYS tunnels can be divided into four stages based on vertical/horizontal stress characteristics, encompassing hydraulic fracturing of karst caves, local inrush, rock collapse, and overall inrush. The dip angle of the bedding planes affects the hydraulic failure characteristics of karst caves. When the cave is located at the top of the tunnel, the water-resistant structures with a dip angle (θ) of 45° poses the highest risk, while θ = 0° provides the most stability. Furthermore, the decrease in water pressure and the occurrence of the maximum flow velocity within the cave can serve as vital indexes to predict the transition from local inrush to overall inrush disaster. These findings emphasize the importance of the CDSPH tunnel model considering stratigraphic layers and karst cave positions when predicting water/mud inrush, and provide guidance for the prevention of inrush flow in karst tunnels.
{"title":"Progressive failure of water-filled karst cave of stratified tunnel using coupled discontinuous smoothed particle hydrodynamics method","authors":"Chengzhi Xia , Zhenming Shi , Liu Liu , Guangyin Lu , Lin Zhou , Chuanyi Tao , Shaoqiang Meng","doi":"10.1016/j.undsp.2024.10.006","DOIUrl":"10.1016/j.undsp.2024.10.006","url":null,"abstract":"<div><div>Tunnel construction in karst terrain is influenced by water-filled karst caves and stratigraphic layers, which involves failure characteristics of water-resistant structures and complex fluid–solid interaction (FSI) processes. To cope with this challenge, this paper used coupled discontinuous smoothed particle hydrodynamics (CDSPH) method for investigating water inrush of tunnels considering stratigraphic layers and karst cave positions. Hydraulic fracturing test and sliding‑induced impulsive wave test were carried out to verify the accuracy of the CDSPH method. Moreover, a comprehensive analysis of inrush events in the field-scale Qiyeshan (QYS) karst tunnel was conducted, considering different layer dip angles and cave positions on the evolution characteristics of inrush disasters, with quantitative parameters proposed for predicting water/mud inrush from local to overall disaster. The simulation results indicate that CDSPH karst model has been verified to faithfully capture the progressive failure of water-resistant structure during inrush in stratigraphic layers. Water/mud inrush in QYS tunnels can be divided into four stages based on vertical/horizontal stress characteristics, encompassing hydraulic fracturing of karst caves, local inrush, rock collapse, and overall inrush. The dip angle of the bedding planes affects the hydraulic failure characteristics of karst caves. When the cave is located at the top of the tunnel, the water-resistant structures with a dip angle (<em>θ</em>) of 45° poses the highest risk, while <em>θ</em> = 0° provides the most stability. Furthermore, the decrease in water pressure and the occurrence of the maximum flow velocity within the cave can serve as vital indexes to predict the transition from local inrush to overall inrush disaster. These findings emphasize the importance of the CDSPH tunnel model considering stratigraphic layers and karst cave positions when predicting water/mud inrush, and provide guidance for the prevention of inrush flow in karst tunnels.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"25 ","pages":"Pages 74-98"},"PeriodicalIF":8.3,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334079","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 : 2025-08-11DOI: 10.1016/j.undsp.2025.04.009
Boyu Jiang, Haibin Wei, Dongsheng Wei, Zipeng Ma, Fuyu Wang
Predicting surface settlement can identify potential risks associated in shield construction. However, in the construction of under-crossing existing structures, the surface settlement is minimal due to the high stiffness of the existing structure, making it unsuitable as a basis for risk assessment. Therefore, interlayer soil settlement was used as an evaluation index in this paper, which was predicted by the developed multi-parameter time series (MPTS) model. This model establishes new dataset, including time, effective stress ratio (ESR), mechanical fluctuation coefficient (MFC), and interlayer soil settlement, where ESR and MFC take into account the changing geological conditions. This study proposes a novel MPTS model, integrating grid search (GS), nonlinear particle swarm optimization (NPSO), and support vector regression (SVR) algorithms to predict interlayer soil settlement during under-crossing construction. It utilizes GS and NPSO to obtain the optimal hyperparameters for SVR. Sensitivity analysis based on MPTS model was used to identify important parameters and propose specific improvement measures. A real under-crossing tunnel project was adopted to verify the effectiveness of the MPTS. The results show that the new input parameters proposed in this paper reduce mean absolute error (MAE) by 20.3% and mean square error (MSE) by 46.7% of prediction results. Compared with the other three algorithms, GS-NPSO-SVR has better prediction performance. Through Sobol sensitivity analysis, previous settlement, ESR and MFC in fully weathered mudstone and moderately weathered mudstone are identified as the primary parameters affecting the interlayer soil settlement. The improvement measures based on analysis results reduce the accumulated settlement by 79.97%. The developed MPTS model can accurately predict the interlayer soil settlement and provide guidance for water stopping or reinforcement construction.
{"title":"Interlayer soil settlement prediction in the construction of under-crossing existing structures based on multi-parameter time series model","authors":"Boyu Jiang, Haibin Wei, Dongsheng Wei, Zipeng Ma, Fuyu Wang","doi":"10.1016/j.undsp.2025.04.009","DOIUrl":"10.1016/j.undsp.2025.04.009","url":null,"abstract":"<div><div>Predicting surface settlement can identify potential risks associated in shield construction. However, in the construction of under-crossing existing structures, the surface settlement is minimal due to the high stiffness of the existing structure, making it unsuitable as a basis for risk assessment. Therefore, interlayer soil settlement was used as an evaluation index in this paper, which was predicted by the developed multi-parameter time series (MPTS) model. This model establishes new dataset, including time, effective stress ratio (ESR), mechanical fluctuation coefficient (MFC), and interlayer soil settlement, where ESR and MFC take into account the changing geological conditions. This study proposes a novel MPTS model, integrating grid search (GS), nonlinear particle swarm optimization (NPSO), and support vector regression (SVR) algorithms to predict interlayer soil settlement during under-crossing construction. It utilizes GS and NPSO to obtain the optimal hyperparameters for SVR. Sensitivity analysis based on MPTS model was used to identify important parameters and propose specific improvement measures. A real under-crossing tunnel project was adopted to verify the effectiveness of the MPTS. The results show that the new input parameters proposed in this paper reduce mean absolute error (MAE) by 20.3% and mean square error (MSE) by 46.7% of prediction results. Compared with the other three algorithms, GS-NPSO-SVR has better prediction performance. Through Sobol sensitivity analysis, previous settlement, ESR and MFC in fully weathered mudstone and moderately weathered mudstone are identified as the primary parameters affecting the interlayer soil settlement. The improvement measures based on analysis results reduce the accumulated settlement by 79.97%. The developed MPTS model can accurately predict the interlayer soil settlement and provide guidance for water stopping or reinforcement construction.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"24 ","pages":"Pages 335-351"},"PeriodicalIF":8.3,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904475","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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