Search Results (31)

The Vertical Shaft Machine (VSM) method is increasingly used in ultra-deep prefabricated shafts. However, as its application extends into hard ground, existing segment designs still largely follow soft soil experiences, resulting in insufficient material utilization and poor economic efficiency. Based on the first VSM shaft in South China, this study establishes a refined finite element model validated by field monitoring and subsequently constructs a structural response database. A GA-XGBoost surrogate model combined with the SHAP method quantifies the contributions of key parameters—concrete strength, rebar diameter, and steel plate thickness—to shaft structural stress. Following the optimization objective of reducing material consumption while maintaining the overall structural performance of the original design, an optimization scheme for Ring 0 reinforcement is proposed. Results show that SHAP analysis effectively identifies the contribution ranking of each parameter to the structural response: for Ring 0, concrete strength contributes the most while rebar diameter shows low sensitivity; for the cutting edge ring, steel plate thickness and concrete strength contribute significantly, whereas tie bars show the lowest sensitivity. After optimization of Ring 0, reinforcement consumption per linear meter of segment is reduced by 43.43 kg, and steel content decreases by 57.91 kg/m³. Verification confirms that the stress distribution remains largely unchanged and crack width meets specification limits. Tie bars in the cutting edge ring play an irreplaceable structural role during concrete pouring and should not be directly optimized. The proposed scheme reduces material consumption while ensuring structural safety, offering a reference for optimizing VSM shaft segment structures in hard ground conditions. Full article

With the increasing application of the Vertical Shaft Machine (VSM) method in ultra-deep shafts, accurate prediction of construction-induced structural stresses is vital for engineering safety. Currently, VSM is predominantly used in soft soils, where structural response analysis still relies on finite element (FE) simulations that are computationally intensive and complex to model. To improve analysis efficiency and understand the structural behavior of VSM shafts in granite composite strata, this study takes the first VSM shaft project in South China—the Guangzhou–Huadu Intercity Railway Shield Shaft—as a case study. A “monitoring-driven, large-sample data, machine learning substitution” framework is proposed for predicting structural stresses during construction. The framework calibrates an FE model using monitoring data. Through full factorial design, key design parameters—including main reinforcement diameter, stirrup diameter, concrete strength grade, and steel plate thickness—are systematically varied. Parametric FE simulations are then conducted to construct large-sample response databases (540 sets for ring 0 and 864 sets for the cutting edge ring). Genetic algorithm is introduced to optimize the hyperparameters of Random Forest, XGBoost, and Neural Network models, and their predictive performances are systematically compared. Results show that the proposed framework effectively substitutes traditional FE analysis and enables rapid multi-parameter comparison. Among the models, GA-XGBoost achieves the highest prediction accuracy across all stress indicators (R² > 0.999, where R² is the coefficient of determination, with values closer to 1 indicating better predictive performance), demonstrating the superiority of its gradient boosting and regularization mechanisms in handling tabular data with strong physical correlations. Moreover, the method exhibits good extensibility to other engineering response predictions beyond construction stresses. Full article

Large flange shafts are the core load-bearing and connecting components of high-end equipment, and their welding multi-physical fields directly affect the quality and service safety of the components. Traditional experiments and finite element methods suffer from long cycles and low efficiency, which can hardly meet the demand for rapid prediction. Aiming at the fast and accurate prediction of welding temperature, deformation and residual stress, this study combines thermal–mechanical coupled finite element simulation with machine learning to construct and compare a variety of prediction models. A dataset is built based on simulation data from 100 groups of process parameters. Overfitting is reduced through strategies including early stopping and dropout, and models such as MLP, RF, RBF-SVR, TabNet, XGBoost, and FT-Transformer are established and verified through 10-fold cross-validation. The results show that the MLP model performs best in the prediction of temperature, deformation and residual stress, and is in good agreement with the simulation values. The prediction errors of the peak temperature of the weld and base metal are below 5%, and the errors of deformation and residual stress are controlled within 10%. The average error of peak residual stress is about 6 MPa, and the deviation of most samples is less than 5 MPa. The RF model ranks second in accuracy, with an average error of about 6.5 MPa for peak residual stress, showing a satisfactory interpretability and engineering applicability. RBF-SVR and TabNet can meet basic prediction requirements. Under the small-sample condition in this work, XGBoost and FT-Transformer present relatively large errors and a weak generalization ability, making it difficult to achieve high-precision prediction. The MLP model established in this paper can effectively reproduce the evolution of welding multi-physical fields and supports the rapid prediction and process optimization of large flange shaft welding. The generalization ability and practical performance of the model can be further improved by expanding the dataset and experimental verification in the future. Full article

Vertical shafts are key channels for underground energy storage, mineral exploitation, and related engineering fields. Yet in deeply buried complex strata and high ground stress environments, traditional passive supports are prone to lining failure, while linear yield criteria cannot accurately characterize rock masses’ nonlinear mechanical behavior, limiting their use in shaft analysis. The core mechanical process of shaft construction aligns with the cavity contraction–expansion mechanism: excavation induces cavity unloading and contraction, causing shaft deformation and plastic zone expansion in surrounding rock; support enables cavity reverse expansion via preset shaft wall counter loads to actively control surrounding rock deformation. Based on this, this study integrates the Hoek–Brown nonlinear yield criterion, large-strain theory, and non-associated flow rules; couples cavity contraction–expansion semi-analytical solutions with the composite shaft wall mechanical model; and establishes a composite shaft wall–surrounding rock interaction analysis method. This research clarifies excavation-induced surrounding rock mechanical responses, reveals shaft wall counter loads’ regulatory effect on surrounding rock, and develops a systematic excavation support calculation workflow. Parameter analysis shows that increasing lining thickness is the most direct way to reduce inner wall tensile stress and improve safety; composite linings optimize stress distribution and enhance structural collaborative performance; and safety assessment confirms the lining inner wall as a structural weak zone. The proposed method and findings fill the gap in applying cavity contraction–expansion theory to shaft construction, providing reliable theoretical and practical guidance for deep shaft design, construction, and safety evaluation. Full article

Hair loss disorders, particularly androgenetic alopecia (AGA), are common conditions that carry significant psychosocial impact. Current standard therapies, including minoxidil, finasteride, and hair transplantation, primarily slow progression or re-distribute existing follicles and do not regenerate lost follicular structures. In recent years, regenerative medicine has been associated with a gradual shift toward approaches that aim to restore follicular function and architecture. Stem cell-derived conditioned media and exosomes have shown the ability to activate Wnt/β-catenin signaling, enhance angiogenesis, modulate inflammation, and promote dermal papilla cell survival, resulting in improved hair density and shaft thickness with favorable safety profiles. Autologous cell-based therapies, including adipose-derived stem cells and dermal sheath cup cells, have demonstrated the potential to rescue miniaturized follicles, although durability and standardization remain challenges. Adjunctive interventions such as microneedling and platelet-rich plasma (PRP) further augment follicular regeneration by inducing controlled micro-injury and releasing growth and neurotrophic factors. In parallel, machine learning-based diagnostic tools and deep hair phenotyping offer improved severity scoring, treatment monitoring, and personalized therapeutic planning, while robotic Follicular Unit Excision (FUE) platforms enhance surgical precision and graft preservation. Advances in tissue engineering and 3D follicle organoid culture suggest progress toward producing transplantable follicle units, though large-scale clinical translation is still in early development. Collectively, these emerging biological and technological strategies indicate movement beyond symptomatic management toward more targeted, multimodal approaches. Future progress will depend on standardized protocols, regulatory clarity, and long-term clinical trials to define which regenerative approaches can reliably achieve sustainable follicle renewal in routine cosmetic dermatology practice. Full article

Vertical axial-flow pumps with bidirectional passages are widely used in applications requiring flow reversal. However, their unique inlet geometry often leads to asymmetric impeller inflow conditions. This study investigates the internal flow behavior and pressure pulsation characteristics of a vertical bidirectional axial-flow pump under design, critical stall, and deep stall conditions using unsteady Reynolds-averaged Navier–Stokes simulations combined with Fast Fourier Transform and wavelet analysis. Results show that the pump reaches peak efficiency at the design point, with critical and deep stall occurring at 0.6 Q_des and 0.5 Q_des, respectively. The head at the deep stall condition shows a further drop of 7.51% compared to the critical stall condition. This progressive performance degradation is attributed to vortex-induced blockage: it initiates with the intensification of the tip leakage vortex and evolves into large-scale separation vortices covering the suction surface under deep stall—a mechanism distinctly influenced by the bidirectional inlet’s stagnant water zone. Inlet asymmetry, reflected by a normalized velocity coefficient (V_n) below 0.6 in the stagnant water zone under design flow, is partially mitigated during stall due to flow confinement. Pressure pulsations at the blade leading edge are dominated by the blade passing frequency (BPF), with amplitudes under critical stall about 3.2 times those at design conditions. At the impeller outlet, critical stall produces a mixed dominant frequency (shaft frequency and BPF), whereas deep stall yields the highest pulsation amplitude (BPF ≈ 4.8 × the design value) resulting from extreme passage blockage. These findings clarify how bidirectional-inlet-induced vortices modulate stall progression and provide theoretical guidance for enhancing the operational stability of such pumps under off-design conditions. Full article

This study investigates the excavation stability of vertical shafts using the Raise Boring Machine (RBM) method in karst strata with multiple cavities, based on the ventilation shaft project of the Zimuyan Tunnel along the Wudao Expressway. A three-dimensional numerical model was established using ABAQUS (version 6.14) to simulate the RBM excavation process and to analyze the effects of cavity positions and depths on the stability of the surrounding rock during excavation. The results show that (1) when the cavities are located at the same position and depth, the radial displacement of the surrounding rock during the reverse reaming stage is reduced by approximately 60% on average compared to that during the forward reaming stage, and the radial stress is also significantly lower during the reverse reaming process; (2) when the cavities are at the same depth, symmetrically distributed cavities cause the surrounding rock displacement to increase by 15–20% compared to vertically aligned cavities, and the stress distribution becomes more complex; and (3) when the cavities are at the same horizontal position but located on different planes, the stability of the surrounding rock improves as the distance between the two cavities increases. Full article

Shaft alignment is adversely affected by the increasingly severe coupled hull–raft deformation in deep-diving, highly integrated submersibles, thereby compromising operational safety and potentially amplifying vibration noise. To address to this issue, this paper investigates an intelligent alignment control method for the floating raft air spring mounting system (ASMS) applied to marine propulsion unit (MPU) under coupled hull–raft deformation conditions. A multi-objective alignment control algorithm was developed based on the NSGA-II optimization method within an N-step receding horizon optimal control framework, enabling simultaneous achievement of shaft alignment attitude adjustment, hull deformation compensation, raft deformation suppression, and pneumatic energy consumption. Experimental validation was conducted on two distinct ASMS prototypes to evaluate the control algorithm. Tests performed on the ASMS for MPU (MPU-ASMS) prototype demonstrated effective compensation of hull-induced deformations, maintaining shaft alignment offsets within ±0.3 mm and angularities within ±0.5 mm/m. Concurrently, experiments on the floating raft ASMS for the stern compartment (SC-FR-ASMS) achieved precise control of axial offsets within ±0.3 mm, angularities within ±0.5 mm/m, and vertical displacements of critical monitoring points within ±1 mm. The adaptive control strategy additionally proved effective in suppressing raft deformation while simultaneously optimizing pneumatic energy consumption. This research provides robust theoretical and technical foundations for intelligent vibration isolation systems in deep-sea equipment to accommodate extreme-depth-induced hull deformation and large-scale raft deformation. Full article

Circular deep excavations, characterized by their symmetrical geometry, are commonly employed in constructing foundations for large-span suspension bridges and as launching shafts for shield tunneling. However, the mechanical behavior of such excavations under asymmetric surface surcharge remains inadequately understood due to a paucity of relevant investigations. This study addresses this knowledge gap by establishing a three-dimensional finite element model (3D-FEA) based on the anchor deep excavation project of a specific bridge. The model is utilized to investigate the influence of asymmetric surcharge on the forces and deformations within the supporting structure. The results show that both the internal force and displacement cloud diagrams of the support structure exhibit asymmetric characteristics. The distribution of displacement and internal forces has spatial effects, and the maximum values all occur in the areas where asymmetric loads are applied. The maximum values of the displacement, axial force, and shear force of underground continuous walls increase with the increase in the excavation depth. The total displacement curves all show the feature of a “bulging belly”. The maximum displacement is 13.3 mm. The axial force is mainly compression, with a maximum value of −9514 kN/m. The maximum positive and negative values of the shear force are 333 kN/m and −705 kN/m, respectively. The bending moment diagram of different monitoring points shows the characteristics of “bow knot”. The maximum values of the positive bending moment and negative bending moment are 1509.4 kN·m/m and −2394.3 kN·m/m, respectively. The axial force of the ring beam is mainly compression, with a maximum value of −5360 kN, which occurs in ring beams 3, 4, and 5. The displacement cloud diagram of the support structure under symmetrical loads shows symmetrical characteristics. Under different load conditions, the displacement curve of the diaphragm wall shows the characteristics of “bulge belly”. The forms of loads with displacements from largest to smallest at the same position are as follows: asymmetric loads, symmetrical loads, and no loads. These findings provide valuable insights for optimizing the structural design of similar deep excavation projects and contribute to promoting sustainable urban underground development. Full article

Large turbine generators rely on shaft earthing brushes to safely divert harmful shaft currents to ground, protecting bearings from electrical damage. This paper presents a novel deep learning-based diagnostic framework to detect and classify faults in shaft earthing brushes of large turbine generators. A key innovation lies in the use of FFT-derived spectrograms from both voltage and current waveforms as dual-channel inputs to the CNN, enabling automatic feature extraction of time–frequency patterns associated with different SEB fault types. The proposed framework combines advanced signal processing and convolutional neural networks (CNNs) to automatically recognize fault-related patterns in shaft grounding current and voltage signals. In the approach, raw time-domain signals are converted into informative time–frequency representations, which serve as input to a CNN model trained to distinguish normal and faulty conditions. The framework was evaluated using data from a fleet of large-scale generators under various brush fault scenarios (e.g., increased brush contact resistance, loss of brush contact, worn out brushes, and brush contamination). Experimental results demonstrate high fault detection accuracy (exceeding 98%) and the reliable identification of different fault types, outperforming conventional threshold-based monitoring techniques. The proposed deep learning framework offers a novel intelligent monitoring solution for predictive maintenance of turbine generators. The contributions include the following: (1) the development of a specialized deep learning model for shaft earthing brush fault diagnosis, (2) a systematic methodology for feature extraction from shaft current signals, and (3) the validation of the framework on real-world fault data. This work enables the early detection of brush degradation, thereby reducing unplanned downtime and maintenance costs in power generation facilities. Full article

This study tackles the challenge of automatically estimating age from pelvis radiographs. Furthermore, we aim to develop a methodology for applying artificial intelligence to classify or regress medical imagery data. Our dataset comprises 684 pelvis X-ray images of patients, each accompanied by annotations and masks for various regions of interest (e.g., the femur shaft). Radiomic features, e.g., the co-occurrence matrix, were computed to characterize the image content. We assessed statistical analysis, machine learning, and deep learning methods for their effectiveness in this task. Correlation analysis indicated that using certain features in specific regions of interest is promising for accurate age estimation. Machine learning models demonstrated that when using uncorrelated features, the optimal mean absolute error (MAE) for age estimation is 5.20, whereas when employing convolutional networks on the texture feature maps yields the best result of 9.56. Automatically selecting radiomic features for machine learning models achieves a MAE of 7.99, whereas utilizing well-known convolutional architectures on the original image results in a system efficacy of 7.96. The use of artificial intelligence in medical data analysis produces comparable outcomes; however, when dealing with a large number of descriptors, selecting the most optimal ones through statistical analysis enables the identification of the best solution quickly. Full article

Amid escalating global climate crises and the urgent imperative to meet the Paris Agreement’s carbon neutrality targets, the steel industry—a leading contributor to global greenhouse gas emissions—confronts unprecedented challenges in driving sustainable industrial transformation through innovative low-carbon steelmaking technologies. This paper examines decarbonization technologies across three stages (source, process, and end-of-pipe) for two dominant steel production routes: the long process (BF-BOF) and the short process (EAF). For the BF-BOF route, carbon reduction at the source stage is achieved through high-proportion pellet charging in the blast furnace and high scrap ratio utilization; at the process stage, carbon control is optimized via bottom-blowing O₂-CO₂-CaO composite injection in the converter; and at the end-of-pipe stage, CO₂ recycling and carbon capture are employed to achieve deep decarbonization. In contrast, the EAF route establishes a low-carbon production system by relying on green and efficient electric arc furnaces and hydrogen-based shaft furnaces. At the source stage, energy consumption is reduced through the use of green electricity and advanced equipment; during the process stage, precision smelting is realized through intelligent control systems; and at the end-of-pipe stage, a closed-loop is achieved by combining cascade waste heat recovery and steel slag resource utilization. Across both process routes, hydrogen-based direct reduction and green power-driven EAF technology demonstrate significant emission reduction potential, providing key technical support for the low-carbon transformation of the steel industry. Comparative analysis of industrial applications reveals varying emission reduction efficiencies, economic viability, and implementation challenges across different technical pathways. The study concludes that deep decarbonization of the steel industry requires coordinated policy incentives, technological innovation, and industrial chain collaboration. Accelerating large-scale adoption of low-carbon metallurgical technologies through these synergistic efforts will drive the global steel sector toward sustainable development goals. This study provides a systematic evaluation of current low-carbon steelmaking technologies and outlines practical implementation strategies, contributing to the industry’s decarbonization efforts. Full article

The excavation of deep and large vertical shafts in karst areas can easily lead to sudden changes in the stress field of the surrounding rock and even cause disasters such as cave collapses. To investigate the influence of karst areas on the stability of deep and large vertical shaft excavation using the raise boring machine (RBM) method, based on the ventilation vertical shaft project of Zimuyan Tunnel, the influence of karst caves on the displacement and stress fields of the surrounding rock during the construction stage of the vertical shaft was analyzed using the finite element simulation method. Furthermore, the influence of the cave dimensions and the distance between the cave and the shaft on the stability of the surrounding rock was evaluated. The results indicate that the karst cave caused an increase in the radial displacement of the surrounding rock, and the radial displacements and stress in the surrounding rock increased linearly with depth. However, the radial displacement of the surrounding rock in the range of 20D to 21D (D is the well diameter) above the bottom of the well, and the radial stress of the surrounding rock in the range of 7D above and below the depth of the cave, are significantly affected by the cave. When the cavern size increased from 0 to 2.0D, the maximum radial displacement of the surrounding rock in each construction stage increased by 10.7, 16.6, 2.3, and 2.2 times, respectively. Moreover, when the distance between the cavern and the well was increased from 0.5D to 2.0D, the maximum radial displacements of the surrounding rock corresponding to each construction stage were reduced by 51.5%, 61.6%, 40.7%, and 18.4%, respectively. These findings can provide valuable references for the design, construction, and monitoring of deep and large vertical shafts in karst areas. Full article

In the process of tunnel construction, problems such as high-stress rockburst, large deformation of soft rock, water inrush and mud gushing, secondary cracking of linings, blasting interference, man-made damage, and mechanical damage are often encountered. These pose a great challenge to the installation of monitoring equipment and line protection. In order to solve these problems, the 2# inclined shaft of Muzhailing Tunnel in the Gansu Province of China, which exists under high stress, water bearing, and bias conditions, was taken as the research object in this paper. By assembling a string, drilling grouting and sealing, and introducing multiple modes of protection, new fiber grating sensor group installation and line protection methods were proposed. The automatic continuous monitoring of the deep deformation of surrounding rock and the automatic continuous monitoring of steel arch stress were realized. The field monitoring results showed that: (1) the fiber grating displacement sensor group could be used to verify the authenticity of the surface displacement results monitored by the total station; (2) the NPR anchor cable coupling support effectively limited the large deformation of soft rock and the expansion of surrounding rock in a loose circle, and the range of the loose circle was stable at about 1 m; and (3) the main influence range of blasting was at a depth of 0~5 m in surrounding rock, and about 25 m away from the working face. In addition, to secure weak links in the steel arch due to the hardening phenomenon, a locking tube was set at the arch foot. In the support design, the fatigue life of the steel was found to be useful as the selection index for the steel arch frame to ensure the stability of the surrounding rock and the long-term safety of the tunnel. The present research adopted a robust method and integrates a variety of sensor technologies to provide a multifaceted view of the stresses and deformations encountered during the tunneling process, and the effective application of the above results could have certain research and reference value for the design and monitoring of high stress, water-bearing, and surrounding rock supports in tunnels. Full article

Prefabricated reinforced concrete staircases (PC staircases) are prefabricated components that are widely used in prefabricated buildings and are used in large quantities. During the production and construction of a PC staircase, the lifting point setting directly affects the construction safety, construction efficiency, and construction quality. In this paper, we analyze the quality problems and safety risks in the design, production, and construction of PC staircases under the constraints of multidimensional spatial characteristics, clarify the key technical difficulties of prefabricated staircase lifting under the multidimensional spatial and temporal constraints, and analyze the factors that should be considered in the setting of lifting points. In this paper, a prefabricated staircase lifting point setting database is established and a thin-plate spline interpolation algorithm is introduced to expand it. Based on the support vector machine algorithm, the process of optimization is carried out for the kernel function scale parameter and penalty factor, and it is concluded that for every increase of two in the number of cross-validation folds, the percentage reduction in minimum RMSE is 9.4%, 17.8%, and 4.2%, respectively, the percentage increase in the optimization time is 39.7%, 61.8%, and 27.3%, respectively, and a PC staircase lifting point setup method based on the small-sample database is proposed. The number of lifting points and lifting point locations of the PC staircase satisfying the multidimensional spatial feature constraints can be obtained by inputting the five design parameters of the PC staircase, namely, the number of treads, the height of the treads, the width of the treads, the width of the staircase, and the weight of the staircase, into the lifting point setup method proposed in this paper. The reliability of the precast reinforced concrete staircase lifting point setting method proposed in this paper when considering the multidimensional spatial constraint characteristics is verified by the precast staircases in deep shafts for assembly construction at the Chongqing metro station. Full article