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Structural integrity assessment is essential in modern tunneling to ensure safe construction works. State-of-the-art monitoring approaches like displacement readings of geodetic prisms are often limited in the spatial as well as the temporal measurement resolution, which is why potential safety hazards might be overlooked. This paper introduces a large-scale distributed fiber optic sensing (DFOS) network inside the tunnel lining of a highway tunnel currently under construction in Austria. The tunnel construction site faces challenging geological conditions with loose rock excavation near to the surface with minimal covering. Fiber optic sensing cables were installed along both tunnel tubes to autonomously monitor 13 cross-sections of the primary shotcrete lining, about 220 m of the tunnel in longitudinal direction and 10 cross-sections of the secondary inner lining. Measurements are continuously evaluated and autonomously transferred to the geotechnical engineer on-site for further analysis. While the construction works are ongoing, alerts are additionally sent out automatically, if pre-defined thresholds are exceeded. The paper outcomes demonstrate that the innovative DFOS system immediately responds to structural modifications and, indeed, increases safety at the construction site.

The fully mechanized tunnelling method using an earth pressure balance tunnel boring machine (EPB-TBM) with a horseshoe-shaped cross section was first developed and applied to a loess mountain tunnel, along with the application of a horseshoe-shaped segmental tunnel lining. The mechanical behavior of this novel type of segmental tunnel lining still contained uncertainties, and full-scale ring tests were conducted for further investigation. During the loading process, the ring deformation, joint opening, and concrete strain were measured, and the occurrence and progression of structural damage were observed and documented. The experimental results demonstrate that the structural failure of the horseshoe-shaped segmental ring mainly occurred in the arch area, while the invert did not prove to be a weak area. The deformation and failure mechanisms of the horseshoe-shaped segmental ring were found to be similar to those of circular ones. Significantly, the specific characteristics of the ring convergence deformation and bending moment distribution were significantly affected by the distribution positions of segment joints. In addition, during the initial stages of TBM advancement, frequent segment damage was observed at inferior joints, and the elaboration on the causes and corresponding measures was provided. This study provides significant evidence for the design and optimization of horseshoe-shaped segmental tunnel linings.

Tunnel plays an easy functionality solution in complex conditions. The functionality role of the tunnel depends on the performance of tunnel lining segment. The structural and durable ability of the tunnel lining segment depends on the geometry of the lining segment. So, optimization of the various geometries of the lining has to be studied for understanding the lining segmental behaviour and its energy consumption criteria. This paper conducts a numerical investigation on tunnel lining segments with various geometries, such as rectangular, hexagonal and trapezoidal to replicate the flexural load test and thrust load test. In this study, the modelling of the metro tunnel lining segment is done using AutoCAD Software. The models are imported to the finite element software ANSYS 18.1 to determine the maximum principal stress, maximum principal strain and total displacement by the application of unit point load of 1 kN at the crown for flexural load test and lateral load of the same at the supports for thrust load test. The boundary conditions adopted are simply supported for all the models. The total displacement obtained in both the loading conditions is compared with theoretical displacement. The work suggests the optimized shape of the tunnel lining segment based on two different loading conditions and the best solution in terms of concrete consumption, energy intensity and carbon emission. The rectangular lining segment consumes less concrete of 1.36 m 3 , which is less compared to hexagonal segment and trapezoidal segment by 43.38% and 4.41%, respectively.

Precast segmental tunnel linings are structurally vulnerable due to the presence of mechanical joints between segments. However, the lack of detailed design guidelines for these joints has led to limited recognition of the need to evaluate their structural performance. Consequently, most previous studies have focused primarily on the behavior of single segments. Although some research has addressed jointed segments, the majority employed straight-type specimens that fail to capture the actual curvature of tunnel linings. As a result, the influence of joint and segment geometry on the structural response of tunnel linings remains insufficiently quantified, and ultimate failure modes—essential for ultimate limit state design—have not been adequately discussed. To fill this gap, this study conducted full-scale experiments on both single and jointed segments, including arch-type and straight-type geometries. The objective was to examine how segment joints and geometries affect structural responses and to identify the ultimate failure modes of jointed segments. The results revealed that jointed segments exhibited distinct behaviors compared with single segments, governed by joint rotation and local stress concentration. Their response progressed through three stages—bolt contact, joint surface contact, and concrete crushing. The ultimate failure pattern varied with moment direction, with cone-shaped failure developing under sagging, whereas only intrados crushing occurred under hogging. Axial force and geometry influenced stiffness and post-cracking performance, with arch-type segments showing stiffness recovery after cracking and demonstrating superior performance to straight-type segments. These findings contribute to a more comprehensive understanding of tunnel segment behavior.

Cracks in concrete tunnel linings are inevitable during service life. It is necessary to keep abreast of the cracking condition of the lining and formulate reasonable inspection and maintenance measures to ensure operational safety. Considering the influence of train loads on the safety and service performance of cracked linings, the expansion process of lining cracks and the maintenance strategy of tunnels during the service period was investigated. The impact of detection probability and maintenance measures on the service life of tunnel lining and the cost of detection and maintenance of cracked lining in the whole life cycle was analyzed; the optimization calculation model of tunnel lining crack detection and maintenance strategy based on genetic algorithm was established with the multi-objective optimization function of maximizing the service life of detection and maintenance and minimizing the total cost of detection and maintenance of fatigue cracks. The optimization analysis of lining crack expansion, detection, and maintenance was carried out for an operational railroad tunnel. Finally, an optimization analysis of lining crack expansion and maintenance was carried out in a railway tunnel. The results show that the stress intensity factor at the tip of the lining cracks is the same as the train load waveform; the magnitude of the stress intensity factor approximately satisfies the exponential function relationship with the depth of cracks; the fatigue service life of cracked lining is positively correlated with the cost of inspection and maintenance; the adoption of the necessary maintenance and the increase in the number of inspections and maintenance have a better economy while meeting the expectation of the service life. According to the Pareto solution set, the management can formulate the inspection and maintenance strategy based on the tunnel’s expected life and maintenance budget.

This paper presents the effect of impact load and weathering of surrounding rockmass on the deformation behavior of urban underground structures. The FEM based numerical simulation is carried out by varying overburden depths, impact energy, and rock weathering grade. Based on this study, it is suggested that subsidence in tunnel under dynamic loading conditions are basically the function of impact energies, engineering characteristics of surrounding rock mass, depth and diameter of U/G structure, and induced stresses. Overburden depth and weathering grade are found to be the critical parameters in the study. It is observed that the tunnel crown deformation increases with increase in weathering grade. It is also found that the weathering of rock affects the depth of penetration of hammer. The depth of penetration of hammer is large in case of highly weathered Basalt rock as compared to Fresh rock. It is noticed that the increase in cover depth decreases the tunnel deformation and mises stresses around the tunnel periphery. Hence, the tunnels at deeper depth are more stable as compare to the tunnels with lesser overburden depth under dynamic loading conditions. In present work, parametric study is carried out for different rockmasses, subjected to impact loading. The findings of this work suggests that the synthetic rockmass, which is prepared in the laboratory, can be used to replicate in-situ conditions by representing weathered rockmass. Finally, the investigation is performed for the prototype model, and the dynamic response of tunnel lining is computed. The tunnel reinforcement is safe against impact loading, whereas the concrete lining is failed completely at the crown of tunnel and the significant amount of failure is observed along the tunnel length. Hence, the improvement in the tunnel lining material and thickness is required.

During tunnel construction by the drilling and blasting method, the blasting stress waves may affect the mechanical response of existing tunnel lining structure. Based on the similarity theory, a mechanical response of existing tunnel lining structure by blasting model test is designed. The similar material of surrounding rock, explosives, and tunnel lining structure are determined by the similarity of physical mechanical parameters. This study investigates the mechanical response of existing tunnel lining concrete and reinforcing steel‌ by the action of adjacent tunnel blasting. The experimental results reveal the maximum strain distribution and stress peak value of existing tunnel lining concrete and reinforcing steel‌ affected by blasting load. Meanwhile, the equations are established between the peak particle velocity (PPV) and the strain (or stress) in tunnel lining concrete and reinforcing steel‌ affected by adjacent tunnel blasting as well. These Eqs. ( 13 ) and ( 14 ) can be used to predict the maximum stress value of existing tunnel lining concrete and reinforcing steel‌ by the action of blasting load. Simultaneously, these equations have been validated and adjusted by an actual engineering project field test. Thus, evaluating the stress states of tunnel lining caused by adjacent tunnel blasting is more effective. This finding is of great significance for evaluating the stress state of existing tunnel lining structure, as well as providing theoretical guidance and reference for the design of blasting parameters on adjacent tunnel construction.

Current semantic segmentation models have limitations in addressing tunnel lining crack, such as high complexity, misidentification, or inability to detect tiny cracks in specific practical scenarios, which is crucial for precise assessment of tunnel lining health. We developed a novel approach called EDeepLab, aiming to achieve a higher precision detection and segmentation of lining surface crack. EDeepLab improves upon the original DeepLabV3+ framework by replacing its backbone network with an optimized lightweight EfficientNetV2. The amount of EfficientNetV2 block computation is reduced and a self-designed shallow feature fusion module is used to merge the layers to enhance parameter utilization efficiency. Furthermore, the normalization-based attention module and convolutional block attention module attention mechanisms are integrated to classify and process both high and low dimensional information features. This allows for comprehensive utilization of global semantic information and channel information, thereby enhancing the model’s feature extraction capability. Results in constructed metro-tunnel crack dataset demonstrate that the number of parameters is reduced from 144.45 M in the DeepLabV3+ to 99.80 M in the EDeepLab. EDeepLab achieves a mean intersection over union of 84.77%, mean pixel accuracy of 94.96%, and frames per second of 18.52 f/s. The proposed EDeepLab outperforms other models including U-Net, ResNet and fully convolutional networks in the quantitative analysis of tiny cracks and noise interference.

Reliability-based design (RBD) of tunnel concrete linings in weak rocks is challenging due to the uncertainty of geomechanical parameters and the ground–support interaction. This study introduces a transparent and efficient framework that integrates three key components: (1) implicit ground–support equilibrium analysis based on the Convergence–Confinement Method (CCM), (2) probabilistic evaluation of the safety factor ( ), reliability index ( ), and failure probability ( ) and (3) a data-driven surrogate model based on artificial intelligence for rapid parametric analysis. The uncertainties of the rock mass and concrete linings are treated through Monte Carlo sampling, and the outcomes are benchmarked against the First-Order Reliability Method (FORM) to verify accuracy and potential bias. The results are transformed into decision-making charts linking the thickness and compressive strength of concrete linings to target levels, thus providing risk-consistent objectives instead of a fixed safety factor criterion. Two types of linings are considered: conventional concrete (with an average uniaxial compressive strength of 20 MPa) and fiber-reinforced reactive powder concrete (FRPC, with an average uniaxial compressive strength of 65 MPa, developed by the research team). The findings demonstrate that increasing thickness and material quality significantly reduce , achieving the reliability thresholds required for final tunnel support (2E-5). This effect is more critical at smaller thicknesses and with lower-quality materials. The use of FRPC also leads to a considerable reduction in at intermediate thicknesses, making it an efficient option when construction constraints limit thickness increase. The surrogate model successfully reproduces probabilistic trends with high consistency and slight conservatism. Ultimately, the integrated CCM–reliability–artificial intelligence framework bridges the gap between deterministic design and risk management, delivering economical and resilient tunnel lining designs in weak rocks.

Deformation monitoring and structural reliability assessment are key components in modern conventional tunneling. The state-of-the-art monitoring design is usually based on displacement measurements of geodetic targets using total stations paired with pointwise geotechnical sensors inside the tunnel lining. In recent years, distributed fiber optic sensing (DFOS) has become more popular in tunneling applications. DFOS measurements basically deliver internal strain and temperature distributions, but no direct relation to the tunnel shape’s behavior. This paper introduces a novel sensing and evaluation concept, which combines DFOS strain measurements and geodetic displacement readings for distributed shape assessment along curved structures, such as tunnel cross-sections. The designed system was implemented into shotcrete tunnel cross-sections as well as shaft linings and enables the determination of displacement profiles with high spatial resolution in the range of centimeters. Evaluations of continuous monitoring campaigns over several weeks as well as epoch-wise measurements performed by different DFOS sensing units in combination with stochastic analysis demonstrate the high potential of the developed approach and its capability to extend traditional monitoring methods in tunneling.