A prefabricated subway station (PSS) represents an efficient and environmentally friendly construction approach; however, the complexity of its assembly process can induce structural disturbance responses within the base slab structure. This study systematically investigates the mechanical behavior and disturbance characteristics of the base slab during assembly. It establishes a high-precision, large-scale strain monitoring system for PSS construction by integrating distributed fiber optic sensing (DFOS) and fiber Bragg grating (FBG) technologies. Furthermore, a structural disturbance safety classification framework is developed using Principal Component Analysis (PCA) in combination with K-means clustering to quantitively evaluate disturbance severity. The results indicate that: (a) FBG-based time-series monitoring at critical supports reveals pronounced stress imbalance during the initial one-sided assembly stage. Rapid placement of prefabricated components introduces transient impact loads, which may lead to irreversible structural damage, whereas segmented and staged placement effectively reduces impact intensity and mitigates disturbance effects. (b) DFOS-based distributed monitoring demonstrates that the base slab undergoes pronounced compression-bending interaction during top slab assembly. One-sided wall installation induces a bending moment difference of 63%–188% between the slab ends, which is decreased to 9.6% after the top slab is installed, indicating improved structural integrity. (c) PCA-K-means analysis quantifies the average disturbance degrees for monitoring stages S2, S3, and S4 as 0.21, 0.29, and 0.34, respectively. Region exceeding the defined disturbance threshold are more prone to crack formation, demonstrating that the proposed method is effective in identifying structurally vulnerable zones during assembly.

With the rapid development of underground space, structural crack monitoring has become increasingly critical. This study proposes a unified framework integrating image preprocessing, feature extraction, model training, and safety assessment for crack analysis. An improved OTSU threshold segmentation algorithm based on sliding windows and local statistical analysis is developed to enhance noise suppression and detail preservation under complex backgrounds and varying resolutions. For crack identification and orientation classification, SVM, CNN, ResNet-18, and K-means clustering are systematically compared. The results show that the improved OTSU method outperforms the classical approach in both high- and low-resolution images. In classification tasks, SVM achieves the best performance under limited data conditions, with accuracy exceeding 96% and reaching 97% after outlier removal, outperforming CNN, K-means, and ResNet-18. Although ResNet-18 demonstrates strong overall performance with high prediction confidence across crack categories, it remains slightly inferior to SVM when training data are limited. Experimental validation using full-scale loading tests of metro shield tunnel segments further confirms the robustness of the proposed approach, with SVM achieving an accuracy of 95.45% in real-world conditions. This study provides an efficient and reliable solution for automated crack detection and classification in metro tunnel infrastructure and similar underground segment-based systems.

Prefabricated underground stations (PUSs) are widely used for their low-carbon, high-efficiency benefits. The complex construction of large-scale PUS components can lead to stress concentration and cracking. This study analyzes the mechanical performance and crack identification of these components during the construction process. A high-precision monitoring system using optical frequency domain reflectometry (OFDR) was developed to measure the internal strain field of large-scale components. Additionally, a crack identification method based on principal component analysis (PCA) and hierarchical clustering (HC) was established to detect crack initiation and growth during assembly. The main findings of this study are that the monitored data demonstrated the ability of fiber optic sensors to identify areas of highest strain concentration with sub-millimeter spatial resolution. The topsoil backfilling process contributed the most to the bending moment in the roof, accounting for an average of 39.3%, while the foundation trench backfill process had the greatest impact on the base plate and sidewalls, with average contributions of 43.5% and 30.9%, respectively. Additionally, the PCA-HC method proved effective for identifying potential cracks in large-scale assembly components through strain field data measured by OFDR, with cracks observed at the mid-span thin-walled cavities of the roof during hoisting and positioning.

The interface shearing behavior between the pile and seabed soil are crucial for the load-bearing capacity and stability of offshore platforms and wind turbines. This study investigated the interface shearing behavior by combining experimental analysis and micromechanical modeling, with a focus on the effects of relative density D-r, normal stress sigma(n), large interface roughness R-n, and mean particle size D-50. The experimental results show that both D-r and sigma(n) have significant effects on both the peak and residual shear strength. Increasing R-n enhances the interlocking effect between soil particles and the interface, thereby contributing to higher frictional resistance. Under conditions of high roughness, the interlocking action reaches a state of saturation such that further increases in R-n do not induce meaningful changes in the friction angle. At this stage, the interface friction angle stabilizes and approaches the value of pure sand. This behavioral trend, validated across an extended range of R-n (up to 6.96), not only corroborates prior findings regarding roughness effects but also expands the understanding of such mechanisms. However, the critical R-n threshold for the saturation of peak friction angles exhibits a strong dependence on D-50, with smaller D-50 corresponding to higher critical thresholds. Specifically, the critical thresholds are approximately 1.52 for a D-50 of 0.46 mm, 0.72 for a D-50 of 0.97 mm, and 0.36 for a D-50 of 1.39 mm. Furthermore, the proposed micromechanical model combining contact mechanics with statistical descriptions of rough interfaces shows that both sigma(n) and R-n significantly impact interface strength. The comparison between experimental data and model predictions demonstrates the model applicability and provides a deeper understanding of soil-pile interaction mechanisms.

Artificial ground freezing (AGF) is widely applied in tunneling projects in highly permeable strata to form impermeable frozen walls and ensure construction safety. However, accurately predicting frozen wall development under groundwater seepage remains challenging due to the complex coupling between seepage and thermal fields and limited field data. This study proposes a novel mesoscale identification-reconstruction-Monte Carlo simulation methodology to predict the development of frozen walls under seepage conditions based on the actual geological features and seepage characteristics. The method involves the identification of sand microstructural characteristics, including porosity, particle ellipticity, and roughness, based on computed tomography imaging, realistic reconstruction of sand particles, and the establishment of coupled seepage-temperature finite-element models. Monte Carlo simulations are incorporated to account for the stochastic variability of sand characteristics, providing statistically significant predictions. The numerical models were validated through laboratory model tests simulating frozen wall development under different seepage velocities. Parametric analyses were further conducted to investigate the effects of freezing temperature, seepage pressure, porosity, particle ellipticity, and particle roughness on the freezing rate. The results reveal four key mechanisms governing frozen wall formation: the throat effect, the wall effect, the reflux effect, and the frozen soil limitation effect. This study enhances the mechanistic understanding of frozen wall development under seepage conditions and offers practical insights for optimizing AGF design and construction in permeable ground conditions.

Prefabricated underground stations (PUSs) are widely used for their low-carbon, high-efficiency benefits. The complex construction of large-scale PUS components can lead to stress concentration and cracking. This study analyzes the mechanical performance and crack identification of these components during the construction process. A high-precision monitoring system using optical frequency domain reflectometry (OFDR) was developed to measure the internal strain field of large-scale components. Additionally, a crack identification method based on principal component analysis (PCA) and hierarchical clustering (HC) was established to detect crack initiation and growth during assembly. The main findings of this study are that the monitored data demonstrated the ability of fiber optic sensors to identify areas of highest strain concentration with sub-millimeter spatial resolution. The topsoil backfilling process contributed the most to the bending moment in the roof, accounting for an average of 39.3%, while the foundation trench backfill process had the greatest impact on the base plate and sidewalls, with average contributions of 43.5% and 30.9%, respectively. Additionally, the PCA-HC method proved effective for identifying potential cracks in large-scale assembly components through strain field data measured by OFDR, with cracks observed at the mid-span thin-walled cavities of the roof during hoisting and positioning.

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 Q. After normality tests confirm Q 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.

Accurate strain transfer prediction is critical for reliable performance of flexible graphene strain sensors, yet existing models are typically limited to a single deformation regime. This study proposes a unified dual-range interfacial shear-lag model that quantitatively describes strain transfer in vertically oriented graphene (VG) sensors across both microstrain and large-deformation domains. Theoretical formulation is coupled with substrate-dependent mechanics, and experimentally validated using two contrasting polymer platforms: thermoplastic polyurethane (TPU) for high-precision microstrain sensing and polydimethylsiloxane (PDMS) for large-strain monitoring. The TPU-VG sensor achieves a minimum detectable strain of similar to 20 mu epsilon (GF approximate to 2.8), while the PDMS-VG sensor operates up to 24 % strain (GF approximate to 60) with minimal hysteresis. Model-predicted results agree with measurements within 2 %. This work establishes the first generalized strain-transfer framework for multi-range VG-based strain sensors and demonstrates a dual-range sensing strategy bridging micro-and macro-scale deformation. The findings provide a mechanical foundation and practical design guidance for next-generation graphene SHM systems capable of monitoring both subtle structural responses and large-scale damage evolution.

This study develops a comprehensive framework for identifying geological characteristics (GC) during shield tunneling. Four unsupervised clustering methods are employed: Kmeans++, Fuzzy C-means (FCM), Gaussian Mixture Model (GMM), and Hierarchical Clustering (HIC). These clustering methods are applied to both the original and dimensionally reduced datasets, which are obtained through principal component analysis (PCA). The performance of each clustering method is emphasized by capturing the inherent variability of GC. Kmeans++ and FCM exhibit high recognition rates (89.54 % and 90.37 % respectively) and excellent stability. The mean Rand index values for both the original and PCA-processed data are close to 1. PCA significantly enhances the performance of GMM. The identification rate of GMM increases from 67.28 % to 87.68 %, and its mean Rand index improves towards 1. Conversely, HIC has low recognition rates (18.63 % for the original data and 20.60 % after PCA) and low mean Rand index values (0.93 for the original data and 0.91 after PCA), indicating poor stability. The proposed framework, integrating data preprocessing, comprehensive index calculation, and the application of clustering algorithms, provides a robust and effective approach to improve the identification and understanding of GC during the tunneling process. This, in turn, can enhance decision-making processes in similar engineering projects.

This paper investigates the use of the BOTDA (Brillouin Optical Time-Domain Analysis) technology to monitor a large-scale bored pile wall in the field. Distributed fiber optic sensors (DFOSs) were deployed to measure internal temperature and strain changes during cement grouting, hardening, and excavation-induced deformation of a secant pile wall. The study details the geological conditions and DFOS installation process. During grouting, the temperature increased by approximately 69 °C due to cement hydration 30 min post-grouting, while the strain decreased by 0.5% on average due to cement slurry shrinkage. During excavation, the temperature changes were minimal, but the excavation depth significantly influenced the strain distribution, with continuous compressive deformation observed in two monitored boreholes. Two analytical methods, the numerical integration method (NIM) and the finite difference method (FDM), were used to calculate the lateral pile displacement based on the monitored strain data. The results were compared with previous monitoring data, showing that the lateral displacement of the pile was minimal after excavation and was attributed to the high stiffness of the secant pile wall. This study demonstrates the effectiveness of DFOSs and BOTDA technology for monitoring complex pile wall behaviors during construction.

High-loading electrodes are crucial for attaining elevated high energy density in the industrial applications of lithium-ion batteries. However, a rise in electrode loading correlates with an elevation in electrode tortuosity. The elevated tortuosity of the transport pathway may result in a discrepancy between ion transport and electrode reaction, leading to excessive or incomplete reactions of localized particles, creating concentration gradient phenomena, and ultimately causing capacity loss. Research on high-loading electrodes mostly concentrates on the regulation of electrode structure and material modification, while investigations into electrolyte concentration predominantly emphasize solvation structures; however, the correlation between electrolyte concentration and high-loading electrodes has been inadequately explored. This study examines the effect of electrolyte concentration on the electrochemical performance of high-loading LiNi0.83Mn0.12Co0.05O2 (NMC83) electrode. Utilizing pore network modeling (PNM), high-resolution techniques, and pore equivalent diameters (EqD) analysis to compare ion transport pathways and abilities under different electrolyte concentrations. It was observed that a concentration of 1.5 M in the conventional electrolyte can establish a more efficient percolation channel and provide sufficient lithium ions to achieve a balance between ion transport and electrode reaction, thereby alleviating the inherent concentration polarization of high-loading electrodes.

The practical application of lithium metal batteries (LMBs) is largely hindered by the notorious lithium dendrite growth, low cycle efficiency associated with insufficient electrode-electrolyte interphase dynamics, and electrolyte combustion. Here, an advanced electrolyte using a combination of three kinds of salts dissolved in carbonate-based solvents is developed. The salt anions dominate a primary solvation sheath, resulting in weak interaction between Li+ and the solvents, as well as the in situ formation of an inorganic-rich bilayer solid electrolyte interface (SEI). The designed electrolyte enables high cycle stability in LMBs with a high-loading NMC cathode (approximately 20 mg cm(-2)), exhibiting 89.89% capacity retention after 200 cycles with the cutoff voltage of 4.5 V. Cryo-TEM characterization and density-functional theory (DFT) calculations reveal that the borate-based bilayer SEI, characterized by an exceptional dense structure, effectively suppresses lithium dendrite growth, and certify the crucial role of inorganic component continuity and density within the SEI, which surpasses the absorption and migration energy barrier in terms of significance. This profound understanding of SEI structure holds great potential for advancing the development of high-stable LMBs and can be expanded to other battery system.

High-loading electrode is a prerequisite for achieving high energy density in industrial applications of lithium-ion batteries. However, an increased loading leads to elevated battery polarization and reduced battery power density, which presents a significant technical bottleneck in the industry. The present study focuses on designing a rapid electrolyte diffusion pathway to diminish lithium concentration polarization for the high-loading LiNi0.83Mn0.12Co0.05O2 (NMC83) electrode by employing two layers of NMC83 materials with different sizes. This innovative architecture demonstrates exceptional rate performance even under challenging conditions with high-loading and high-rate. Additionally, the interrelationships between electrode structure, process route, porosity, and optimal thickness ratio between layers are discussed, providing valuable guidance for industrial promotion and application. The designed L-Dry-S electrode structure (coating large particles first and then small particles) effectively mitigates concentration polarization in the thick electrode, which is attributed to the fast electrolyte diffusion channel and the differential reaction speeds of NMC83 particles with varying sizes. The knowledge from this work is broadly applicable to other material systems. The present study focuses on minimizing the electrode concentration polarization for the high-loading LiNi0.83Mn0.12Co0.05O2 (NMC83) electrode by employing two layers of NMC83 materials with varying sizes. This innovative architecture demonstrates exceptional rate performance even under challenging conditions of high-loading and high-rate. image

Silicon (Si) has emerged as a promising anode material in the pursuit of higher energy-density lithium-ion batteries (LIBs). The large-scale applications of Si anode, however, are hindered by its significant swelling, severe pulverization, and continuous electrode-electrolyte reaction. Therefore, the development of an efficient approach to mitigate Si particle swelling and minimize interface parasitic reactions has emerged as a prominent research focus in both academia and industry. Here, a facile and scalable strategy is reported for the preparation of a double-layer coated submicron Si anode, comprising ceramic (silicon oxide) and graphene layers, denoted as Si@SiOx@G. In this approach, SiOx is in situ synthesized on the surface of Si and bonded with graphene through hydrogen bond interactions. The prepared Si electrode shows exceptional structural integration and demonstrates outstanding electrochemical stability, with a capacity retention of 92.58% after 540 cycles at 1 A g(-1), as well as remarkable rate capability, achieving a specific capacity of 875 mAh g(-1) at 2 A g(-1). This study presents a straightforward yet pragmatic approach for the widespread implementation of high-energy-density silicon-based batteries.

Sodium-ion batteries hold great promise as next-generation energy storage systems. However, the high instability of the electrode/electrolyte interphase during cycling has seriously hindered the development of SIBs. In particular, an unstable cathode–electrolyte interphase (CEI) leads to successive electrolyte side reactions, transition metal leaching and rapid capacity decay, which tends to be exacerbated under high-voltage conditions. Therefore, constructing dense and stable CEIs are crucial for high-performance SIBs. This work reports localized high-concentration electrolyte by incorporating a highly oxidation-resistant sulfolane solvent with non-solvent diluent 1H, 1H, 5H-octafluoropentyl-1, 1, 2, 2-tetrafluoroethyl ether, which exhibited excellent oxidative stability and was able to form thin, dense and homogeneous CEI. The excellent CEI enabled the O3-type layered oxide cathode NaNi1/3Mn1/3Fe1/3O2 (NaNMF) to achieve stable cycling, with a capacity retention of 79.48

In this study, a flexible vertical graphene (VG) strain sensor was developed for monitoring geogrids deformation. The VG material was fabricated using radio frequency plasma-enhanced chemical vapor deposition, followed by spin-coating a polydimethylsiloxane (PDMS) solution for film curing, resulting in a flexible sensor within a PDMS substrate. The VG sensor was integrated with a wireless Bluetooth data acquisition system for automated and remote strain measurement. The stability performance of VG sensors was examined and effectively improved through cyclic loading tests in the laboratory. The drift ratio of electrical resistance before cyclic loading tests is 37.01%, which is reduced to only 0.5% after cyclic loading tests. Calibration tests show that the maximum measurement resolution and maximum measurement range of VG sensors is 0.7 micro-strain and 40000 micro-strain, respectively, indicating that VG sensors are highly effective for both high-strain resolution identification and large-strain measurement. Pullout tests demonstrate an average error of 5.67% between VG sensors and fiber Bragg grating sensors, suggesting that VG sensors are a promising alternative for large strain, wireless, and long-term geogrid monitoring.

Mechanical performance of prefabricated structural components during construction is critical for ensuring long-term safety and durability of prefabricated underground stations (PUS). This study aims to address the issues of structure mechanical performance of large-scale prefabricated components during assembling process. First, a strain transfer model for five-layered armored fiber optic is proposed and validated experimentally through test. Second a PUS-based distributed fiber optic sensors (DFOS) monitoring system was established. Third, a PUS based hierarchical performance assessment framework was constructed. Finally, a PUS based theoretical Construction Intelligent Monitoring Assessment Model (CIMAM) was established to quantitively assess structure safety level. A case study of Shenzhen Metro PUS was performed, and the results showed that: (a) PUS-based DFOS monitoring system presents high accuracy and high resolution, (b) The monitoring system reflects the PUS reactions during the whole construction process with good adaptability and intelligence, (c) PUS-CIMAM results show that the assembly process of components is a significant control link, particularly the performance of large-span roof during assembling process is a key controlling component, and (d) PUS-CIMAM reveals the adjacent ring construction has a disturbing effect.

The practical application of lithium metal batteries (LMBs) is largely hindered by the notorious lithium dendrite growth, low cycle efficiency associated with insufficient electrode-electrolyte interphase dynamics, and electrolyte combustion. Here, an advanced electrolyte using a combination of three kinds of salts dissolved in carbonate-based solvents is developed. The salt anions dominate a primary solvation sheath, resulting in weak interaction between Li+ and the solvents, as well as the in situ formation of an inorganic-rich bilayer solid electrolyte interface (SEI). The designed electrolyte enables high cycle stability in LMBs with a high-loading NMC cathode (approximately 20 mg cm-2), exhibiting 89.89% capacity retention after 200 cycles with the cutoff voltage of 4.5 V. Cryo-TEM characterization and density-functional theory (DFT) calculations reveal that the borate-based bilayer SEI, characterized by an exceptional dense structure, effectively suppresses lithium dendrite growth, and certify the crucial role of inorganic component continuity and density within the SEI, which surpasses the absorption and migration energy barrier in terms of significance. This profound understanding of SEI structure holds great potential for advancing the development of high-stable LMBs and can be expanded to other battery system.