Pub Date : 2026-05-01Epub Date: 2026-01-09DOI: 10.1016/j.tust.2025.107440
Junjie Liu , Qing Ai , Lulu Zhang , Junyi Zhu , Hui Wang , Xingchun Huang , Yong Yuan
Monitoring data from underwater tunnels are critical for operations and maintenance. However, they are often corrupted by noise from water level fluctuations, and the degradation process within them is difficult to extract, which limits the utility of these data. To address this issue, this study proposes a data-physics integration model for predicting tunnel convergence considering water level fluctuations and lining structure degradation. In the data-driven part, an improved Seasonal and Trend decomposition using Loess (STL) is developed to separate seasonal and trend components while accounting for gradual stiffness degradation of the tunnel lining, thereby producing more realistic time-variant seasonal component. In the physics-based part, a probabilistic degradation model is constructed on the modified rigid ring model, with parameters dynamically updated via a dynamic Bayesian network. By embedding the physics-based degradation model into the STL framework, the proposed approach enhances the prediction accuracy of trend component and strengthens physical interpretability. Comparative analysis using convergence monitoring data from a real underwater tunnel shows that, the proposed integration model achieves higher prediction accuracy and better captures the underlying degradation mechanism.
{"title":"Data-physics integration model for predicting tunnel convergence subject to water level fluctuations and lining structure degradation","authors":"Junjie Liu , Qing Ai , Lulu Zhang , Junyi Zhu , Hui Wang , Xingchun Huang , Yong Yuan","doi":"10.1016/j.tust.2025.107440","DOIUrl":"10.1016/j.tust.2025.107440","url":null,"abstract":"<div><div>Monitoring data from underwater tunnels are critical for operations and maintenance. However, they are often corrupted by noise from water level fluctuations, and the degradation process within them is difficult to extract, which limits the utility of these data. To address this issue, this study proposes a data-physics integration model for predicting tunnel convergence considering water level fluctuations and lining structure degradation. In the data-driven part, an improved Seasonal and Trend decomposition using Loess (STL) is developed to separate seasonal and trend components while accounting for gradual stiffness degradation of the tunnel lining, thereby producing more realistic time-variant seasonal component. In the physics-based part, a probabilistic degradation model is constructed on the modified rigid ring model, with parameters dynamically updated via a dynamic Bayesian network. By embedding the physics-based degradation model into the STL framework, the proposed approach enhances the prediction accuracy of trend component and strengthens physical interpretability. Comparative analysis using convergence monitoring data from a real underwater tunnel shows that, the proposed integration model achieves higher prediction accuracy and better captures the underlying degradation mechanism.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107440"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928514","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 : 2026-05-01Epub Date: 2026-01-31DOI: 10.1016/j.tust.2025.107399
Jimeng Feng , Yifei Li , Jianchi Ma , Jiacheng Song , Bo Wang , Junru Zhang , Zhijian Yan , Hongtao Li
In soft rock tunnels under high in-situ stress, traditional rigid support structures are often incapable of accommodating the substantial, continuous deformation of the surrounding rock, which frequently leads to structural failure. To achieve effective stress release and ensure structural integrity, this study investigates the instability mechanisms of both circumferential and radial yielding structures using an integrated methodology that combines laboratory experiments, three-dimensional numerical simulations, and physical model tests. The results indicate that: (1) Polyurethane foam, as a typical highly compressible material, exhibits excellent energy absorption capacity and deformation compatibility, making it an ideal filler for yielding structures; (2) Regarding structural response, the failure of circumferential yielding structures is primarily caused by connection failure at the joints; deploying 5 to 7 nodes is recommended to ensure structural continuity and effective load transfer. In contrast, the failure of radial yielding structures is mainly due to the failure of the compressible layer, resulting in uncontrollable deformation; a compressible layer thickness of 25 to 30 cm is advised; (3) Depending on the burial depth and in-situ stress level, guidelines for selecting appropriate support structures are provided: circumferential yielding structures effectively control large deformations when the strength-to-stress ratio ranges from 1.2 to 0.4, while radial yielding structures are more suitable for a ratio between 0.7 and 0.25. The findings of this study provide valuable insights to guide the optimization of support design for soft rock tunnels under high in-situ stress conditions.
{"title":"Instability analysis of circumferential and radial yielding structures in high in-situ stress soft rock tunnels based on polyurethane foam","authors":"Jimeng Feng , Yifei Li , Jianchi Ma , Jiacheng Song , Bo Wang , Junru Zhang , Zhijian Yan , Hongtao Li","doi":"10.1016/j.tust.2025.107399","DOIUrl":"10.1016/j.tust.2025.107399","url":null,"abstract":"<div><div>In soft rock tunnels under high in-situ stress, traditional rigid support structures are often incapable of accommodating the substantial, continuous deformation of the surrounding rock, which frequently leads to structural failure. To achieve effective stress release and ensure structural integrity, this study investigates the instability mechanisms of both circumferential and radial yielding structures using an integrated methodology that combines laboratory experiments, three-dimensional numerical simulations, and physical model tests. The results indicate that: (1) Polyurethane foam, as a typical highly compressible material, exhibits excellent energy absorption capacity and deformation compatibility, making it an ideal filler for yielding structures; (2) Regarding structural response, the failure of circumferential yielding structures is primarily caused by connection failure at the joints; deploying 5 to 7 nodes is recommended to ensure structural continuity and effective load transfer. In contrast, the failure of radial yielding structures is mainly due to the failure of the compressible layer, resulting in uncontrollable deformation; a compressible layer thickness of 25 to 30 cm is advised; (3) Depending on the burial depth and in-situ stress level, guidelines for selecting appropriate support structures are provided: circumferential yielding structures effectively control large deformations when the strength-to-stress ratio ranges from 1.2 to 0.4, while radial yielding structures are more suitable for a ratio between 0.7 and 0.25. The findings of this study provide valuable insights to guide the optimization of support design for soft rock tunnels under high in-situ stress conditions.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107399"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095858","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 : 2026-05-01Epub Date: 2026-01-31DOI: 10.1016/j.tust.2026.107468
Jiajie Zhen , Fengwen Lai , Ming Huang , Jim S. Shiau , Junjie Zheng , Zhen Hua
To tackle the challenges of untimely position control in shield tunneling, this study proposes a novel offline reinforcement learning (RL) framework aimed at enabling intelligent decision-making for cylinder push thrust. The proposed framework comprises two key components: a state classification and assignment (SCA) method and a model-based offline policy conservative exploration optimization (MOPCEO) model. Specifically, the SCA method partitions the dataset into distinct clusters to accommodate the wide value range and time-varying distribution of shield tunneling data. MOPCEO incorporates an offline conservative exploration (OCE) strategy to enhance dataset exploration, mitigate error accumulation, and minimize the risk of high-consequence actions. Furthermore, an interpretability analysis combining Shapley Q values and particle swarm optimization (PSO) is employed to validate the reasonability and reliability of MOPCEO. The proposed framework is trained and tested using the dataset from the Xiamen Metro shield tunneling project. Results show that the MOPCEO not only outperforms baseline models but also significantly enhances the automation and intelligence of shield tunneling position control.
{"title":"Towards intelligent shield position control: A novel offline reinforcement learning framework with SCA-MOPCEO integration","authors":"Jiajie Zhen , Fengwen Lai , Ming Huang , Jim S. Shiau , Junjie Zheng , Zhen Hua","doi":"10.1016/j.tust.2026.107468","DOIUrl":"10.1016/j.tust.2026.107468","url":null,"abstract":"<div><div>To tackle the challenges of untimely position control in shield tunneling, this study proposes a novel offline reinforcement learning (RL) framework aimed at enabling intelligent decision-making for cylinder push thrust. The proposed framework comprises two key components: a state classification and assignment (SCA) method and a model-based offline policy conservative exploration optimization (MOPCEO) model. Specifically, the SCA method partitions the dataset into distinct clusters to accommodate the wide value range and time-varying distribution of shield tunneling data. MOPCEO incorporates an offline conservative exploration (OCE) strategy to enhance dataset exploration, mitigate error accumulation, and minimize the risk of high-consequence actions. Furthermore, an interpretability analysis combining Shapley Q values and particle swarm optimization (PSO) is employed to validate the reasonability and reliability of MOPCEO. The proposed framework is trained and tested using the dataset from the Xiamen Metro shield tunneling project. Results show that the MOPCEO not only outperforms baseline models but also significantly enhances the automation and intelligence of shield tunneling position control.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107468"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095855","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 : 2026-05-01Epub Date: 2026-01-15DOI: 10.1016/j.tust.2026.107443
Wei-Bin Chen , Hai-Tong Liu , Yue Chen , Xiang-Sheng Chen , Tao Xu , Jing-Song Bai , Lin-Shuang Zhao
The stratum disturbance caused by excavation will threaten the structural integrity and operational safety of the existing metro tunnels. The data-driven approach proposed in this study mainly focuses on the safety assessment and probabilistic deformation prediction of existing metro tunnels under adjacent excavation operations. In deformation prediction, comparison of the Elman neural network, extreme gradient boosting, support vector machine, and random forest model shows the extreme gradient boosting achieves excellent accuracy and captures convergence variation patterns robustly. For safety assessment, principal component analysis fuses three key deformation indices to generate a comprehensive parameter . After normality tests confirm approximates a normal distribution, the “68-95 rule” classifies tunnel safety into 4 levels. For the left tunnel line, the 180-day forecast shows that the deployment of monitoring points under slightly enhanced Level 3 frequency can be moderately expanded. For the right tunnel line, the proportion of high/enhanced-frequency monitoring points can be proportionally reduced. In probabilistic deformation prediction, K-means clustering identifies two optimal clusters for both tunnel lines. Larger Bootstrap sampling enhances the statistical stability of the expendance percentage distribution. Left-line Cluster 2 shows persistently high expendance percentages while right-line Cluster 1 carries higher risk, likely owing to greater burial depth and in-situ stress. Level 1 high-frequency monitoring supplemented by multi-source data is recommended for both high-risk clusters. The proposed risk assessment framework is expected to promote the transformation from empirical thresholds to statistical thresholds and from static risk mapping to dynamic risk mapping.
{"title":"Field data-based safety assessment and probabilistic deformation prediction of existing metro tunnels under adjacent excavation","authors":"Wei-Bin Chen , Hai-Tong Liu , Yue Chen , Xiang-Sheng Chen , Tao Xu , Jing-Song Bai , Lin-Shuang Zhao","doi":"10.1016/j.tust.2026.107443","DOIUrl":"10.1016/j.tust.2026.107443","url":null,"abstract":"<div><div>The stratum disturbance caused by excavation will threaten the structural integrity and operational safety of the existing metro tunnels. The data-driven approach proposed in this study mainly focuses on the safety assessment and probabilistic deformation prediction of existing metro tunnels under adjacent excavation operations. In deformation prediction, comparison of the Elman neural network, extreme gradient boosting, support vector machine, and random forest model shows the extreme gradient boosting achieves excellent accuracy and captures convergence variation patterns robustly. For safety assessment, principal component analysis fuses three key deformation indices to generate a comprehensive parameter <span><math><mrow><mi>Q</mi></mrow></math></span>. After normality tests confirm <span><math><mrow><mi>Q</mi></mrow></math></span> approximates a normal distribution, the “68-95 rule” classifies tunnel safety into 4 levels. For the left tunnel line, the 180-day forecast shows that the deployment of monitoring points under slightly enhanced Level 3 frequency can be moderately expanded. For the right tunnel line, the proportion of high/enhanced-frequency monitoring points can be proportionally reduced. In probabilistic deformation prediction, K-means clustering identifies two optimal clusters for both tunnel lines. Larger Bootstrap sampling enhances the statistical stability of the expendance percentage distribution. Left-line Cluster 2 shows persistently high expendance percentages while right-line Cluster 1 carries higher risk, likely owing to greater burial depth and in-situ stress. Level 1 high-frequency monitoring supplemented by multi-source data is recommended for both high-risk clusters. The proposed risk assessment framework is expected to promote the transformation from empirical thresholds to statistical thresholds and from static risk mapping to dynamic risk mapping.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107443"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979440","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 : 2026-05-01Epub Date: 2026-01-22DOI: 10.1016/j.tust.2026.107466
Jun Shen , Jiazhi Huang , Zhiheng Zhu , Xiaohua Bao , Junhong Li , Xiangsheng Chen , Hongzhi Cui
Accurate inspection of undersea tunnel linings is essential for ensuring the long-term structural integrity and safe operation of marine infrastructure. However, conventional methods struggle with multi-chamber configurations due to visual data isolation and limited spatial correlation across chambers. To address this challenge, we propose a novel 3D Reality and Deep Zoom Image (3DZI) inspection technique that integrates 3D reconstruction with panoramic image unfolding. This method establishes a formalized mapping between three-dimensional models and high-resolution surface imagery, enabling precise localization, cross-chamber defect tracking, and improved data fusion. The proposed approach is cost-effective, scalable, and adaptable to confined and complex environments. Demonstrated in a real-world large-diameter undersea tunnel project, the method achieved reconstruction accuracy within ± 10 mm using consumer-grade equipment, offering a practical and economical solution for intelligent tunnel monitoring. The paper also discusses limitations regarding environmental variability, automation potential, and scalability. The findings contribute to the advancement of engineering informatics by extending how spatial and visual knowledge can be formalized and operationalized in the built environment.
{"title":"3D reality and deep zoom image framework for inspection of an undersea multi-chamber tunnel","authors":"Jun Shen , Jiazhi Huang , Zhiheng Zhu , Xiaohua Bao , Junhong Li , Xiangsheng Chen , Hongzhi Cui","doi":"10.1016/j.tust.2026.107466","DOIUrl":"10.1016/j.tust.2026.107466","url":null,"abstract":"<div><div>Accurate inspection of undersea tunnel linings is essential for ensuring the long-term structural integrity and safe operation of marine infrastructure. However, conventional methods struggle with multi-chamber configurations due to visual data isolation and limited spatial correlation across chambers. To address this challenge, we propose a novel 3D Reality and Deep Zoom Image (3DZI) inspection technique that integrates 3D reconstruction with panoramic image unfolding. This method establishes a formalized mapping between three-dimensional models and high-resolution surface imagery, enabling precise localization, cross-chamber defect tracking, and improved data fusion. The proposed approach is cost-effective, scalable, and adaptable to confined and complex environments. Demonstrated in a real-world large-diameter undersea tunnel project, the method achieved reconstruction accuracy within ± 10 mm using consumer-grade equipment, offering a practical and economical solution for intelligent tunnel monitoring. The paper also discusses limitations regarding environmental variability, automation potential, and scalability. The findings contribute to the advancement of engineering informatics by extending how spatial and visual knowledge can be formalized and operationalized in the built environment.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107466"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033333","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 : 2026-05-01Epub Date: 2026-01-22DOI: 10.1016/j.tust.2026.107474
Biao Wang , Zaobao Liu , Liang Chen , Hongsu Ma , Bo Lu , Sun Jian , Xi Du
The long-term strength of rocks under high-temperature and high-pressure conditions is crucial for assessing the stability of high-level radioactive waste disposal facilities. This study investigates the thermomechanical behavior of Beishan granite, a candidate host rock for Chinese high-level radioactive waste repository, under coupled thermal–mechanical loading. A series of triaxial direct shear creep tests integrated with acoustic emission monitoring were conducted to analyze the damage evolution and failure mechanisms of the rock. Results reveal that as temperature increases from 30 °C to 150 °C, the proportion of tensile failure in the granite increases from 29.03 % to 51.36 %. A thermo-mechanically coupled viscoelastic damage constitutive model is developed to accurately capture the time-dependent mechanical response of granite under triaxial direct shear. After validation against experimental data, the model is implemented into a finite element framework via a user subroutine to predict the long-term stability of an high-level radioactive waste disposal unit. Furthermore, fuzzy set theory was applied to optimize the layout parameters of the disposal unit, leading to an enhanced safety and efficiency profile; the optimal layout is determined with a canister number (NC) to canister spacing (SC) to unit spacing (SI) ratio of 1:3:10. These results provide valuable insights into the thermo-mechanical behavior of granite and offer a practical tool supporting the design and safety evaluation of deep geological repositories, thereby facilitating the sustainable development of nuclear energy.
{"title":"Analysis and layout parameter optimization study for high-level radioactive waste disposal units in granite host rock","authors":"Biao Wang , Zaobao Liu , Liang Chen , Hongsu Ma , Bo Lu , Sun Jian , Xi Du","doi":"10.1016/j.tust.2026.107474","DOIUrl":"10.1016/j.tust.2026.107474","url":null,"abstract":"<div><div>The long-term strength of rocks under high-temperature and high-pressure conditions is crucial for assessing the stability of high-level radioactive waste disposal facilities. This study investigates the thermomechanical behavior of Beishan granite, a candidate host rock for Chinese high-level radioactive waste repository, under coupled thermal–mechanical loading. A series of triaxial direct shear creep tests integrated with acoustic emission monitoring were conducted to analyze the damage evolution and failure mechanisms of the rock. Results reveal that as temperature increases from 30 °C to 150 °C, the proportion of tensile failure in the granite increases from 29.03 % to 51.36 %. A thermo-mechanically coupled viscoelastic damage constitutive model is developed to accurately capture the time-dependent mechanical response of granite under triaxial direct shear. After validation against experimental data, the model is implemented into a finite element framework via a user subroutine to predict the long-term stability of an high-level radioactive waste disposal unit. Furthermore, fuzzy set theory was applied to optimize the layout parameters of the disposal unit, leading to an enhanced safety and efficiency profile; the optimal layout is determined with a canister number (<em>N<sub>C</sub></em>) to canister spacing (<em>S<sub>C</sub></em>) to unit spacing (<em>S<sub>I</sub></em>) ratio of 1:3:10. These results provide valuable insights into the thermo-mechanical behavior of granite and offer a practical tool supporting the design and safety evaluation of deep geological repositories, thereby facilitating the sustainable development of nuclear energy.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107474"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033342","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 : 2026-05-01Epub Date: 2026-01-12DOI: 10.1016/j.tust.2026.107459
Zhihan Zhou , Yanhong Xi , Jun Mao , Guilan Yu , Wanki Chow
This study systematically investigates the fluctuating characteristics and temperature field distribution of carriage fires through a series of scaled-down (1:8) modeling experiments. The fire development process was observed to exhibit two distinct stages: a fuel-controlled stage characterized by monotonically increasing maximum temperatures with heat release rate, and a ventilation-controlled phase showing a decreasing trend in maximum temperature. Parametric studies revealed that the maximum temperature during the fuel-controlled phase is primarily governed by heat release rate and ventilation factor, while demonstrating a weak correlation with gradient. A power law relationship was established between dimensionless maximum temperature, dimensionless heat release rate, and wall heat loss. Furthermore, a temperature decay prediction model incorporating slope-opening coupling effects was developed, featuring two key coefficients: Coefficient , dependent on gradient (θ), exhibited a 12.8 % variation rate as θ increased from 0 to 0.0764, reflecting the enhancement of buoyancy-driven flow. Coefficient , determined by opening geometry (proportional to the 0.15th power of the opening height-to-width ratio), was attributed to strong turbulent mixing effects. By integrating the maximum temperature and decay models, a comprehensive temperature field prediction model was developed for inclined railway carriage fires with lateral openings (W/H: 0.45–4.55) and gradients (θ: 0–0.0764). The model demonstrated high accuracy, with prediction errors consistently below 10 %. This work provides an improved predictive framework for temperature fields in inclined railway carriage fires and offers a valuable theoretical foundation for train fire safety design.
{"title":"Coupled ventilation-slope effects on flame dynamics and temperature distribution in high-speed train compartment fires","authors":"Zhihan Zhou , Yanhong Xi , Jun Mao , Guilan Yu , Wanki Chow","doi":"10.1016/j.tust.2026.107459","DOIUrl":"10.1016/j.tust.2026.107459","url":null,"abstract":"<div><div>This study systematically investigates the fluctuating characteristics and temperature field distribution of carriage fires through a series of scaled-down (1:8) modeling experiments. The fire development process was observed to exhibit two distinct stages: a fuel-controlled stage characterized by monotonically increasing maximum temperatures with heat release rate, and a ventilation-controlled phase showing a decreasing trend in maximum temperature. Parametric studies revealed that the maximum temperature during the fuel-controlled phase is primarily governed by heat release rate and ventilation factor, while demonstrating a weak correlation with gradient. A power law relationship was established between dimensionless maximum temperature, dimensionless heat release rate, and wall heat loss. Furthermore, a temperature decay prediction model incorporating slope-opening coupling effects was developed, featuring two key coefficients: Coefficient <span><math><mrow><mi>α</mi></mrow></math></span>, dependent on gradient (θ), exhibited a 12.8 % variation rate as θ increased from 0 to 0.0764, reflecting the enhancement of buoyancy-driven flow. Coefficient <span><math><mrow><mi>β</mi></mrow></math></span>, determined by opening geometry (proportional to the 0.15th power of the opening height-to-width ratio), was attributed to strong turbulent mixing effects. By integrating the maximum temperature and decay models, a comprehensive temperature field prediction model was developed for inclined railway carriage fires with lateral openings (W/H: 0.45–4.55) and gradients (θ: 0–0.0764). The model demonstrated high accuracy, with prediction errors consistently below 10 %. This work provides an improved predictive framework for temperature fields in inclined railway carriage fires and offers a valuable theoretical foundation for train fire safety design.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107459"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957104","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 : 2026-05-01Epub Date: 2026-01-12DOI: 10.1016/j.tust.2026.107458
Weisong Liu , Jun Zhang , Rui Ba , Weiguo Song
The guides can help pedestrians find exits, thereby improving evacuation efficiency and reducing casualties in emergency. This study investigates how guides affect crowd evacuations via controlled experiments and modeling. To investigate the influence of guide on crowd evacuation, a Social Force Model with Guidance (SFMG) has been established by embedding the guidance term into the social force framework. The interaction mechanism between a guide and pedestrians was studied by performing crowd movement experiments under different guidance modes (dynamic/static), movement speeds and crowd densities. The guidance attraction force formula involving the above variables has been proposed. It is revealed that the guidance attraction field is influenced by the guidance mode and speed. Subsequently, the simulations in a subway platform under varying visibility conditions were conducted and the influence of the initial layout of guides on crowd evacuation efficiency was studied. The results revealed that arranging guides in the areas far from the exit can facilitate the crowd evacuation. The distance between the guide’s initial position and exit was denoted as D. in the simulation analyses. A variable DR (distance ratio) calculated by the ratio of D to the length of the platform subzone was adopted to quantity guide’s initial position. Dynamic guide: The higher DR results in the shorter evacuation time under low visibility. But the optimal initial position of guide shifts slightly closer to the exit with increasing visibility. Static guide: The U-shaped relationship between evacuation time and DR is observed. The optimal position falls within the 40 %∼60 % DR. These findings are helpful to design indoor emergency guidance plan, and the optimal positioning rules are transferable to common building layouts.
{"title":"Mode-Dependent optimal positioning of evacuation Guides: An Experimental–Modeling study on static and dynamic guidance effect","authors":"Weisong Liu , Jun Zhang , Rui Ba , Weiguo Song","doi":"10.1016/j.tust.2026.107458","DOIUrl":"10.1016/j.tust.2026.107458","url":null,"abstract":"<div><div>The guides can help pedestrians find exits, thereby improving evacuation efficiency and reducing casualties in emergency. This study investigates how guides affect crowd evacuations via controlled experiments and modeling. To investigate the influence of guide on crowd evacuation, a Social Force Model with Guidance (SFMG) has been established by embedding the guidance term into the social force framework. The interaction mechanism between a guide and pedestrians was studied by performing crowd movement experiments under different guidance modes (dynamic/static), movement speeds and crowd densities. The guidance attraction force formula involving the above variables has been proposed. It is revealed that the guidance attraction field is influenced by the guidance mode and speed. Subsequently, the simulations in a subway platform under varying visibility conditions were conducted and the influence of the initial layout of guides on crowd evacuation efficiency was studied. The results revealed that arranging guides in the areas far from the exit can facilitate the crowd evacuation. The distance between the guide’s initial position and exit was denoted as <em>D.</em> in the simulation analyses. A variable <em>DR</em> (distance ratio) calculated by the ratio of <em>D</em> to the length of the platform subzone was adopted to quantity guide’s initial position. Dynamic guide: The higher <em>DR</em> results in the shorter evacuation time under low visibility. But the optimal initial position of guide shifts slightly closer to the exit with increasing visibility. Static guide: The U-shaped relationship between evacuation time and <em>DR</em> is observed. The optimal position falls within the 40 %∼60 % <em>DR</em>. These findings are helpful to design indoor emergency guidance plan, and the optimal positioning rules are transferable to common building layouts.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107458"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957105","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 : 2026-05-01Epub Date: 2026-01-21DOI: 10.1016/j.tust.2026.107463
Louis Ngai Yuen Wong , Sihao Yu , Kazaf Yuen Pan Wong
Pipelines play a significant role in transferring energies, materials and fulfilling public needs. However, conventional pipeline maintenance approaches predominantly depend on human inspection of captured closed circuit television (CCTV) records, a process that is particularly labor-intensive and time-consuming for lengthy pipelines. To address these limitations, this study proposes an autonomous framework based on the multi-object tracking (MOT) algorithm for efficient and accurate deposit detection and tracking within pipelines, significantly reducing the need for manual intervention. The proposed MOT model has been trained and validated on a customized pipe CCTV dataset, consisting of more than 12,000 video frames. The experimental results indicate that the combination of YOLOX (for detection) and BYTE (for tracking) achieves the highest MOTA, IDF1 and HOTA among all the tested models, with values of 87.4 %, 90.1 % and 78.7 %, respectively. Further testing conducted on a real-world sewer pipeline project demonstrates the robustness of our model. The estimation error of the deposit location predicted by the MOT model is less than ± 0.1 m, with a mean absolute error of only 0.06 m. These findings highlight the substantial advantages of the autonomous MOT system over manual methods, including improved efficiency, consistent accuracy and reduced labor demands, thus demonstrating its reliability and significant application potential for practical engineering practice.
{"title":"Intelligent and autonomous pipeline deposit tracking based on a multi-object tracking framework","authors":"Louis Ngai Yuen Wong , Sihao Yu , Kazaf Yuen Pan Wong","doi":"10.1016/j.tust.2026.107463","DOIUrl":"10.1016/j.tust.2026.107463","url":null,"abstract":"<div><div>Pipelines play a significant role in transferring energies, materials and fulfilling public needs. However, conventional pipeline maintenance approaches predominantly depend on human inspection of captured closed circuit television (CCTV) records, a process that is particularly labor-intensive and time-consuming for lengthy pipelines. To address these limitations, this study proposes an autonomous framework based on the multi-object tracking (MOT) algorithm for efficient and accurate deposit detection and tracking within pipelines, significantly reducing the need for manual intervention. The proposed MOT model has been trained and validated on a customized pipe CCTV dataset, consisting of more than 12,000 video frames. The experimental results indicate that the combination of YOLOX (for detection) and BYTE (for tracking) achieves the highest MOTA, IDF1 and HOTA among all the tested models, with values of 87.4 %, 90.1 % and 78.7 %, respectively. Further testing conducted on a real-world sewer pipeline project demonstrates the robustness of our model. The estimation error of the deposit location predicted by the MOT model is less than ± 0.1 m, with a mean absolute error of only 0.06 m. These findings highlight the substantial advantages of the autonomous MOT system over manual methods, including improved efficiency, consistent accuracy and reduced labor demands, thus demonstrating its reliability and significant application potential for practical engineering practice.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107463"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014887","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 : 2026-05-01Epub Date: 2026-01-20DOI: 10.1016/j.tust.2026.107465
Xuyang Wang , Dajun Yuan , Yong Fang , Dalong Jin , Yubo Wang
As the critical actuation subsystem, the shield thrust system performs dual essential functions: propelling the machine against ground resistance and enabling precise pose adjustments for alignment with the designed tunnel axis. This study develops a mechanical model for shield machines, integrating the principle of virtual work to derive the force Jacobian matrix of thrust mechanisms. Force and moment transmission ellipsoids that geometrically quantify the thrust system’s capability are further constructed to generate terminal effector forces. Finally, combined with a case analysis, the mechanical transmission performance of the shield thrust system in different pose (position and attitude) states is discussed. The model proposed in this study can provide some assistance in thrust system design based on the driving forces and moments required for shield tunneling.
{"title":"Mechanical transmission analysis of thrust systems of shield tunneling machines","authors":"Xuyang Wang , Dajun Yuan , Yong Fang , Dalong Jin , Yubo Wang","doi":"10.1016/j.tust.2026.107465","DOIUrl":"10.1016/j.tust.2026.107465","url":null,"abstract":"<div><div>As the critical actuation subsystem, the shield thrust system performs dual essential functions: propelling the machine against ground resistance and enabling precise pose adjustments for alignment with the designed tunnel axis. This study develops a mechanical model for shield machines, integrating the principle of virtual work to derive the force Jacobian matrix of thrust mechanisms. Force and moment transmission ellipsoids that geometrically quantify the thrust system’s capability are further constructed to generate terminal effector forces. Finally, combined with a case analysis, the mechanical transmission performance of the shield thrust system in different pose (position and attitude) states is discussed. The model proposed in this study can provide some assistance in thrust system design based on the driving forces and moments required for shield tunneling.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"171 ","pages":"Article 107465"},"PeriodicalIF":7.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014827","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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