Underground Space最新文献

{"title":"Review on data-informed planning for underground space","authors":"Fang-Le Peng ,&nbsp;Wei-Xi Wang ,&nbsp;Yong-Kang Qiao ,&nbsp;Chen-Xiao Ma ,&nbsp;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}

{"title":"Multi-fidelity knowledge inheritance with active querying for data-driven clogging prediction during mechanized tunneling","authors":"Xiao Yuan ,&nbsp;Shuying Wang ,&nbsp;Tongming Qu ,&nbsp;Huanhuan Feng ,&nbsp;Pengfei Liu ,&nbsp;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}

{"title":"Deep learning-based segmentation and detection of tunnel lining defects and components from GPR images using T-GPRMask","authors":"Jiahao Li ,&nbsp;Hehua Zhu ,&nbsp;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}

{"title":"Progressive failure of water-filled karst cave of stratified tunnel using coupled discontinuous smoothed particle hydrodynamics method","authors":"Chengzhi Xia ,&nbsp;Zhenming Shi ,&nbsp;Liu Liu ,&nbsp;Guangyin Lu ,&nbsp;Lin Zhou ,&nbsp;Chuanyi Tao ,&nbsp;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}

{"title":"Interlayer soil settlement prediction in the construction of under-crossing existing structures based on multi-parameter time series model","authors":"Boyu Jiang,&nbsp;Haibin Wei,&nbsp;Dongsheng Wei,&nbsp;Zipeng Ma,&nbsp;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}

{"title":"Artificial intelligence-optimized shield parameters for soft ground tunneling in urban environment: A case study of Bangkok MRT Blue Line","authors":"Sahatsawat Wainiphithapong ,&nbsp;Chana Phutthananon ,&nbsp;Sompote Youwai ,&nbsp;Pitthaya Jamsawang ,&nbsp;Phattarawan Malaisree ,&nbsp;Ochok Duangsano ,&nbsp;Pornkasem Jongpradist","doi":"10.1016/j.undsp.2025.04.008","DOIUrl":"10.1016/j.undsp.2025.04.008","url":null,"abstract":"<div><div>This paper presents a study on multi-objective optimization (MOO) of shield operational parameters (SOPs) for soft ground tunneling using a tunnel boring machine (TBM) in an urban environment, focusing on the case study of the MRT Blue Line in Bangkok. The investigation aims to determine the optimal combination of SOPs, consisting of face pressure (<span><math><msub><mi>F</mi><mtext>p</mtext></msub></math></span>), thrust force (<span><math><msub><mi>T</mi><mtext>f</mtext></msub></math></span>), grout pressure (<span><math><msub><mi>G</mi><mtext>p</mtext></msub></math></span>), and percent grout filling (<span><math><msub><mi>G</mi><mtext>f</mtext></msub></math></span>), along with relevant environmental factors, including tunnel depth (<span><math><msub><mi>T</mi><mtext>d</mtext></msub></math></span>), inverted groundwater level (<span><math><msub><mi>W</mi><mtext>i</mtext></msub></math></span>), and type of surrounding soil (<span><math><msub><mi>T</mi><mtext>s</mtext></msub></math></span>). The primary objective is to enhance the penetration rate (<span><math><msub><mi>P</mi><mtext>avg</mtext></msub></math></span>, in terms of average value), as cost consideration, while mitigating ground surface settlement (<span><math><mi>S</mi></math></span>), as safety (serviceability) consideration. Using long short-term memory (LSTM) neural networks as predictive models, the results yield coefficient of determination (<em>R</em>²) values of 0.81 and 0.96, root mean square error (RMSE) values of 5.91 mm/min and 3.09 mm, and average bias factor values of 0.99 and 0.88 for the <span><math><mi>P</mi></math></span> and <span><math><mi>S</mi></math></span> predictive models, respectively, based on validation datasets. This integrated framework, which combines the non-dominated sorting genetic algorithm (NSGA-II) with LSTM neural networks, is applied to MOO to identify the optimal SOPs, while accounting for their influence on <span><math><mi>S</mi></math></span> variation as a time-series over 11 timesteps, as considered in this study. For simplification and practical field implementation, the same set of SOP values is applied across all 11 timesteps during the optimization process. Using the proposed optimization framework, the optimal results demonstrate improvements in <span><math><msub><mi>P</mi><mtext>avg</mtext></msub></math></span>, increasing by up to 109.8% (from 13.99 to 29.35 mm) and in <span><math><mi>S</mi></math></span>, reducing up to 79.6% (from 34.55 to 7.06 mm) when MOO is conducted as a time series using the simplified method. This finding provides a valuable approach to effectively address the sequential uncertainties of relevant factors in soft ground tunneling for similar projects.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"24 ","pages":"Pages 311-334"},"PeriodicalIF":8.3,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of a large and shallow twin-tunnel excavation on a high-speed railway bridge and related protective measures: A case study","authors":"Wenhui Yang ,&nbsp;Dingwen Zhang ,&nbsp;Daniela Boldini","doi":"10.1016/j.undsp.2025.05.001","DOIUrl":"10.1016/j.undsp.2025.05.001","url":null,"abstract":"<div><div>This case study examines a landmark engineering project in Suzhou, China, involving the construction of two large-diameter (13.2 m) shield tunnels beneath an active high-speed railway (HSR) bridge. This pioneering project is the first of its kind in both China and the world. Advanced numerical simulations were conducted to rigorously assess construction risks. To ensure the operational safety of the existing HSR bridge, an innovative protective system, consisting primarily of segmental steel casing concrete pile barriers, was employed. A comprehensive network of monitoring sensors was strategically deployed to track soil, pile barrier, and pier displacements throughout both the protective and tunnelling phases. Simulation results indicated that tunnelling without protective measures could cause pier displacements of up to 3.1 mm along the bridge—exceeding the maximum allowable displacement of 2 mm in accordance with regulations. Monitoring data revealed that the maximum pier displacement during protective scheme installation was limited to 0.5 mm. With these protective measures, pier displacement during each tunnelling phase remained consistently below 0.5 mm, representing an approximate 80% reduction compared to the unprotected scenario, thereby ensuring the continued safety of the HSR bridge.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"24 ","pages":"Pages 216-237"},"PeriodicalIF":8.3,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated design framework for excavation retaining structures: Extending IFC standards and integrating BIM with geotechnical simulation","authors":"Qiwei Wan,&nbsp;Yuyuan Zhu,&nbsp;Haibin Ding,&nbsp;Wentao Hu,&nbsp;Changjie Xu","doi":"10.1016/j.undsp.2025.04.007","DOIUrl":"10.1016/j.undsp.2025.04.007","url":null,"abstract":"<div><div>Challenges arise in automate design with building information modeling (BIM) in underground space. Industry foundation classes (IFC) standard lacks detailed entity objects for describing excavation retaining structures and geological information, and automated design based on BIM models is not yet for practical application. This study presents a novel automated framework. It integrates the extended IFC standard with mechanical analysis and BIM modeling, significantly advancing structural optimization and rebar detailing. Direct 3D model generation streamlines complex excavation projects, aligning with the trend towards automated, precision-driven design. Key contributions include: (1) the extension of the IFC standard to support excavation retaining structures with objects like IfcBracedPit and IfcPitWall, improving interoperability between geotechnical models and BIM systems; (2) the integration of heuristic algorithms for automated optimization of deformation control parameters, reducing manual intervention; and (3) the promotion of design methodology that bypasses two-dimensional modeling and directly generates three-dimensional models, enhancing efficiency and allowing engineers to focus on high-level decision-making. However, the framework is primarily suited for standard cross-section projects like subway stations and tunnels. Future work will focus on refining the framework for more complex geotechnical projects, addressing software independence and improving design robustness and independence.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"24 ","pages":"Pages 261-282"},"PeriodicalIF":8.3,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental study on ECC-based unreinforced shield tunnel segmental joints for future resilient infrastructure","authors":"Minjin Cai ,&nbsp;Timon Rabczuk ,&nbsp;Xiaoying Zhuang","doi":"10.1016/j.undsp.2024.09.009","DOIUrl":"10.1016/j.undsp.2024.09.009","url":null,"abstract":"<div><div>To advance resilient infrastructure, this study explores unreinforced shield tunnel segment technologies, a critical but under-researched area. It conducted experiments on ECC-based unreinforced segments (ECCUS), comparing them with ECC-based reinforced segments (ECCRS) and reinforced concrete segments (RCS), focusing on their mechanical properties, including material characteristics, segmental deflection, joint behavior, bolt strain, damage propagation, failure modes, joint toughness, and ductility. Key findings include: (1) ECCUS joints exhibited significantly enhanced bearing capacity, with ultimate strength 34% higher than RCS and 29% higher than ECCRS. In terms of initial cracking strength, ECCUS outperformed RCS by 200% and ECCRS by 34%. (2) The absence of reinforcement cages in ECCUS reduced stiffness but improved overall segment coordination and deformation, leading to deflections 100% greater than RCS and 85% than ECCRS. (3) ECCUS and ECCRS displayed numerous, fine cracks under 200 µm wide, while RCS showed fewer, wider cracks over 3 mm, leading to significant spalling. Cracks in ECCUS were densely distributed across shear and compression zones, in contrast to RCS and ECCRS where they concentrated on compression areas. (4) ECCUS joints exhibited remarkable toughness, with elastic phase toughness 13.47 times that of RCS and 1.91 times that of ECCRS. In the normal serviceability phase, the toughness of ECCUS was 12.17 times that of RCS and 2.53 times that of ECCRS. (5) Considering multi-scale mechanical effects, ECCUS joints amplified the material advantages of ECC over RC more than 11 times during the elastic stage. These findings offer valuable insights for future resilient infrastructure development based on unreinforced construction technologies.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"24 ","pages":"Pages 283-310"},"PeriodicalIF":8.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmark study of three statistical methods for six intact rock failure criteria constrained by different rock strength data","authors":"Peng-fei He ,&nbsp;Xin Li ,&nbsp;Xu-long Yao ,&nbsp;Zhi-gang Tao ,&nbsp;Yan-ting Du","doi":"10.1016/j.undsp.2025.04.006","DOIUrl":"10.1016/j.undsp.2025.04.006","url":null,"abstract":"<div><div>To reduce the impact of potential strength outliers on parameter estimation, statistical methods based on the least median square and least absolute deviation have been proposed as alternatives to the traditional least square method. However, little research has been conducted to compare the performance of these different statistical methods. This study introduces a novel procedure for evaluating the three statistical approaches across six selected rock failure criteria, constrained by various rock strength datasets. The consistency of the best-fitting failure criterion and the robustness of the strength parameter estimations serve as the primary benchmarks for evaluation. Based on the benchmark analysis, the following conclusions are drawn. First, both the least square and least absolute deviation methods perform equivalently in identifying the best-fitting failure criterion for a given rock strength dataset, whereas the least median square method does not. Second, when estimating the strength parameters in a two-dimensional failure criterion with the conventional test data of low complexity, the least absolute deviation method is recommended for obtaining robust parameter estimations. Third, as the complexity of conventional test data increases or when true triaxial test data are used to estimate strength parameters for a three-dimensional failure criterion, it is essential to evaluate the outlier-proneness by analyzing the prediction error. If the kurtosis of the prediction error is less than 3, the least square method is preferred. Otherwise, the least absolute deviation method should be used to mitigate the influence of potential strength outliers. This benchmark study offers valuable insights for the future application of different statistical methods in rock mechanics.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"24 ","pages":"Pages 238-260"},"PeriodicalIF":8.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810524","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}