Search Results (344)

The exploitation of China’s oil and gas resources has advanced into the “ten-thousand-meter” ultra-deep realm. Downhole tubulars and well control equipment materials face severe corrosion challenges under the extreme high-temperature, high-pressure, and highly acidic environments prevalent in such formations. However, the corrosion mechanisms and patterns of materials under these harsh conditions remain insufficiently elucidated, necessitating systematic research. This study focuses on the typical casing material P110SS, investigating its corrosion behavior in high-temperature, high-pressure H₂S/CO₂ environments. The results show that at a partial pressure of H₂S of 0.5 MPa, the corrosion rate of P110SS increases with temperature. A significant increase in the corrosion rate and the occurrence of pitting corrosion were observed between 100 °C and 140 °C. The corrosion product transformed from mackinawite to pyrrhotite. At 60 °C, increasing the partial pressure of H₂S led to a slight increase in the corrosion rate, while at 160 °C, the corrosion rate slightly decreased. However, temperature changes did not cause any alteration in the corrosion products. Full article

Fossil fuels are crucial to the global energy supply, with pipelines being a vital transportation method. However, these vital assets are highly susceptible to pitting corrosion, an insidious form of degradation that can lead to catastrophic failures. Unlike uniform corrosion, which represents a symmetric form of material loss, pitting corrosion is a highly asymmetric and localized phenomenon. The inherent complexity and asymmetry of this process make its prediction a significant challenge. To address this, this study presents SSA-CNN-Attention, a deep learning model specifically designed to analyze the complex, nonlinear interactions among environmental factors. The model employs a Convolutional Neural Network (CNN) to extract local features, while a crucial attention mechanism allows it to asymmetrically weight the importance of these features, enhancing its ability to recognize intricate interactions. Additionally, the Sparrow Search Algorithm (SSA) optimizes the model’s hyperparameters for improved accuracy and stability. Furthermore, a post hoc interpretability analysis using the LIME framework validates that the model’s learned feature relationships are consistent with established corrosion science, revealing how the model accounts for the asymmetric influence of key variables. The experimental results demonstrate that the proposed model reduces mean squared error (MSE) by 61.3% and mean absolute error (MAE) by 26.6%, while improving the coefficient of determination (R²) by 28.2% compared to traditional CNNs. These findings highlight the model’s superior performance in predicting a fundamentally asymmetric process and provide valuable insights into the underlying corrosion mechanisms. Full article

In deep excavation dewatering engineering, fully enclosed cutoff walls are widely implemented to improve the efficiency of dewatering in the pit and prevent adverse environmental impacts such as land subsidence and damage to adjacent infrastructure. However, the presence of such impermeable barriers fundamentally alters flow dynamics, rendering conventional aquifer test interpretation methods inadequate. This study presents a novel closed-form analytical solution for transient drawdown in a leaky confined aquifer bounded by a rectangular, fully enclosed cutoff wall under constant-rate pumping. The solution is rigorously derived by applying the mirror image method within a superposition framework, explicitly accounting for the barrier effect of the curtain. A type-curve matching methodology is developed to inversely estimate key aquifer parameters—transmissivity, storativity, and vertical leakage coefficient—while incorporating the geometric and boundary effects of the curtain. The approach is validated against field data from a pumping test conducted at a deep excavation site in Wuhan, China. Excellent agreement is observed between predicted and measured drawdowns across multiple observation points, confirming the model’s fidelity. The proposed solution and parameter estimation technique provide a physically consistent, analytically tractable, and computationally efficient framework for interpreting pumping tests in constrained aquifer systems, thereby improving predictive reliability in dewatering design and supporting sustainable groundwater management in urban underground construction. Full article

In the context of deep foundation pit excavation, the settlement of surrounding buildings is a critical indicator for safety assessment and early warning. Due to the non-stationary and nonlinear characteristics of settlement data, traditional prediction approaches often fail to achieve satisfactory accuracy. To address this challenge, this study proposes a hybrid prediction model integrating the Grey Wolf Optimizer (GWO), Variational Mode Decomposition (VMD), and Long Short-Term Memory (LSTM) networks, referred to as the GWO-VMD-LSTM model. In the proposed framework, GWO is employed to optimize the key hyperparameters of VMD as well as LSTM, thereby ensuring robust decomposition and prediction performance. Experimental results based on settlement monitoring data from four typical points around the Yongning Hospital foundation pit in Taizhou, China, demonstrate that the proposed model achieves superior predictive accuracy compared with five benchmark models. Specifically, the GWO-VMD-LSTM model attained an average coefficient of determination (R²) of 0.951, mean squared error (MSE) of 0.002, root mean square error (RMSE) of 0.033 mm, mean absolute error (MAE) of 0.031 mm, and mean absolute percentage error (MAPE) of 1.324%, outperforming all alternatives. For instance, compared with the VMD-LSTM model, the proposed method improved R² by 26.56% and reduced MAPE by 45.87%. These findings confirm that the GWO-VMD-LSTM model not only enhances the accuracy and generalization of settlement prediction but also provides a reliable and practical tool for real-time monitoring and risk assessment of buildings adjacent to deep foundation pits in soft soil regions. Full article

Urban deep excavations conducted near operational tunnels necessitate stringent deformation control. This study investigates the Baiyun Station excavation by employing a three-dimensional finite-element model based on the Hardening Soil Small-strain (HSS) constitutive law, calibrated using Phase I field monitoring data on wall deflection, ground settlement, and tunnel displacement. Material parameters for the HSS model derived from the prior Phase I numerical simulation were held fixed and used to simulate the Phase II excavation, with peak errors of less than 5.8% for wall deflection and less than 2.9% for ground settlement. The model was subsequently applied to evaluate the impacts of Phase II excavation. The key contribution of this study is a monitoring-driven HSS modeling framework that integrates staged excavation simulation with field-based calibration, enabling quantitative assessment of tunnel responses—including settlement troughs, bow-shaped wall deflection patterns, and the distance-decay characteristics of lining displacement—to support structural safety evaluations and protective design measures. The results demonstrate that the predicted deformations and lining stresses in adjacent power and metro tunnels remain within permissible limits, offering practical guidance for excavation control in densely populated urban areas. Full article

This study aims to evaluate the corrosion behavior of and morphological changes in S235JR steel exposed to 0.5 M hydrochloric acid solution over a period of 24 weeks. Corrosion resistance was assessed through weight loss measurements and electrochemical techniques (such as open circuit potential (OCP), polarization resistance (R_p), and corrosion rate (V_corr)), while surface morphology, elemental analysis, roughness, and Vickers hardness were also analyzed. All evaluations were performed at the same immersion intervals: 2, 4, 8, 12, and 24 weeks. The corrosion rate started at 0.9 mm/year after the first hour of immersion, then decreased due to the formation of corrosion products on the steel surface, and fluctuated during prolonged exposure, reaching a maximum of 8.5 mm/year after 24 weeks. Weight loss increased gradually during the first 8 weeks, followed by a more pronounced rise. Polarization resistance and corrosion rate exhibited dynamic variations. SEM analysis revealed severe surface degradation, including cracks and deep pits. Surface roughness increased significantly from an initial value of 0.91 μm to 9.03 μm at 24 weeks. Vickers hardness dropped from 148.7 HV0.5 to 87.3 HV0.5, due to non-uniform corrosion product formation. These findings highlight the progressive deterioration of S235JR steel in acidic environments and provide valuable insight into its long-term corrosion resistance. Full article

The aim of the present study is to investigate the mechanism behind corrosion destruction in laser-melted layers (LMLs) of AISI 321 austenitic stainless steel after electrochemical corrosion in Ringer’s solution. Surface morphology, microstructure, chemical composition, grain sizes, and orientation are studied using OM, SEM, EDS, and EBSD. It was confirmed that (1) the main mechanism behind corrosion destruction is identical between untreated and laser-melted steel, i.e., the selective destruction of the lower corrosion resistance phase (δ-ferrite) in the form of pits, and (2) the morphology and size of corrosion pits are different, as determined via δ-ferrite morphology, with narrow deep pits of uneven shape observed on the surface of wrought steel and rounded shallower pits seen in LML. The following mechanism is proposed with regard to corrosion destruction in LML: (1) the initial destruction of δ-ferrite; (2) the formation of an austenitic dendrite network; (3) the mechanical fracture of austenitic dendrites and pit formation; and (4) the growth of pits inside the grain. The following relationship between corrosion pit development and dendrite orientation in the LML is observed: (1) In the melted zone, with dendrite axes perpendicular to or inclined toward the surface, the corrosion pit grows within the grain. (2) At the melted zone/base metal (MZ/BM) boundary, with dendrite axes parallel to the surface, the corrosion pit develops in the heat-affected zone, along the MZ/BM boundary. Full article

Selecting appropriate materials is crucial for mitigating the severe economic and safety challenges posed by microbiologically induced corrosion (MIC) in marine and industrial settings. This study focuses on the MIC behavior of 316L austenitic stainless steel and 2205 duplex stainless steel that is caused by the metabolic activities of D. vulgaris during a life span of 7 days. Cell counts, weight loss, electrochemical measurements, and surface characterization were employed to evaluate the materials’ resistance to MIC. Specifically, 2205 DSS exhibited a 60% lower weight loss (0.02 vs. 0.05 mg/cm²), a 42% lower maximum pit depth (2.11 vs. 3.64 μm), and an orders-of-magnitude lower corrosion current density (0.094 vs. 2.0 μA cm⁻²) compared to 316L SS, demonstrating its superior resistance to D. vulgaris MIC. XRD and XPS analyses revealed that although FeS formed on both materials, FeS₂—a thermodynamically stable deep-sulfidation product—was only present on 316L, indicating a more advanced corrosion stage. The absence of FeS₂ on 2205 suggests limited sulfide corrosion progression. These findings confirm the advantage of duplex stainless steel in mitigating D. vulgaris-induced corrosion and provide insights into the selection of materials for MIC-prone environments. Full article

Dental caries remains one of the most prevalent global diseases, and the early detection of occlusal lesions is critical because demineralization often begins deep within pits and fissures where conventional visual–tactile or radiographic inspection cannot detect it. SmarTooth, a newly introduced fluorescence device that irradiates enamel with a 655 nm laser and records the reflected intensity, may provide more objective, quantitative diagnoses. This study assessed its diagnostic performance against the International Caries Detection and Assessment System (ICDAS). We examined 1421 occlusal surfaces from 153 adults, scored each surface with ICDAS codes 0–4, and recorded SmarTooth peak values. Spearman’s rank correlation was used to test the association between codes and peak values; one-way ANOVA with Tukey’s post hoc was used to compare mean values across codes; and sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) were calculated at three diagnostic thresholds: D1 (0 vs. 1–4), D2 (0–2 vs. 3–4), and D3 (0–3 vs. 4). The SmarTooth values rose with lesion severity and correlated moderately with ICDAS (r = 0.495, p < 0.001). The AUROC ranged from 0.69 to 0.82, with the best accuracy observed at D2 (cut-off: 7.0; AUC: 0.82; sensitivity: 78.3%; specificity: 77.4%). These findings suggest that SmarTooth can complement ICDAS scoring as an adjunctive tool, potentially enhancing diagnostic accuracy and supporting early intervention for occlusal caries in general dental practice. Full article

With rapid urbanization, deep foundation pit clusters (DFPCs) have become increasingly common, introducing complex and significant construction risks. To improve risk evaluation under such complexity and uncertainty, this study proposes a hierarchical assessment framework. First, fault tree analysis is used to systematically identify and decompose DFPC-related risks. Second, a Bayesian network (BN) is constructed based on the fault tree to model interactions among risks, and structural learning techniques are applied to optimize the BN structure. An analytic hierarchy process (AHP) is then used to assign prior probabilities, enabling the identification of critical risk factors. To validate the framework, numerical simulations are used to analyze the impact of support failures on pit stability. The results show that mid-span support failures have the greatest influence. Two DFPC layouts are simulated to assess the effects of failure location and pit spacing. When the spacing is 0.10H (H = excavation depth), failures in a subpit’s mid-support cause the most severe impact on adjacent pits. These results confirm the framework’s effectiveness in evaluating DFPC risk. Full article

Continuum robots with outstanding compliance, dexterity, and lean bodies are successfully applied in medicine, aerospace engineering, the nuclear industry, rescue operations, construction, service, and manipulation. However, the inherent low stiffness characteristics of continuum bodies make it challenging to develop ultra-long and small-diameter continuum robots. To address this size–scale challenge of continuum robots, we developed an 8 m long continuum robot with a diameter of 23 mm by a tip actuation and growth mechanism. Meanwhile, we also realized the untethered design of the continuum robot, which greatly increased its usable space range, portability, and mobility. Demonstration experiments prove that the developed growing continuum robot has good flexibility and manipulability, as well as the ability to cross obstacles and search for targets. Its continuum body can transport liquids over long distances, providing water, medicine, and other rescue items for trapped individuals. The functionality of an untethered growing continuum robot (UGCR) can be expanded by installing multiple tools, such as a grasping tool at its tip to pick up objects in deep wells, pits, and other scenarios. In addition, we established a static model to predict the deformation of UGCR, and the prediction error of its tip position was within 2.6% of its length. We verified the motion performance of the continuum robot through a series of tests involving workspace, disturbance resistance, collision with obstacles, and load performance, thus proving its good anti-interference ability and collision stability. The main contribution of this work is to provide a technical reference for the development of ultra-long continuum robots based on the tip actuation and steering principle. Full article

The stability of open-pit mines under low-temperature conditions is critical for safe and efficient coal extraction. However, the mechanisms of rock damage and fracture under combined temperature and stress effects remain unclear, particularly regarding the evolution of mechanical properties under repeated freeze–thaw cycles and varying peripheral pressures. This study investigates the damage and rupture behavior of coal-bearing sandstone in cold-region open-pit mines through experimental testing and theoretical modeling. The research was conducted in three stages: (1) freeze–thaw and peripheral pressure experiments to evaluate mechanical property evolution; (2) acoustic emission monitoring to analyze internal fracture initiation, propagation, and coalescence under temperature–stress coupling; (3) development of a local deterioration model to quantify post-damage strength decay considering low-temperature erosion and freeze–thaw effects. Results show that increasing freeze–thaw cycles leads to a transition from brittle to ductile behavior, while higher peripheral pressures significantly enhance ductility. Mechanical parameters are highly sensitive to peripheral pressure but largely independent of freeze–thaw cycle count. Acoustic emission signals respond strongly to temperature, and temperature–stress coupling governs the three-stage evolution of fracture germination, extension, and penetration. The local deterioration model effectively captures post-peak residual strength and damage evolution. These findings indicate that in regions with higher microcrack density, fault propagation is driven by rapid coalescence under stress concentration, whereas in lower-density regions, it is dominated by gradual fracture growth and temperature-induced expansion. The results provide theoretical guidance for stability assessment and support design in open-pit coal mines in cold environments. Full article

Asymmetric structures are widespread in deep excavation engineering and place heightened demands on the deformation control and safety of retaining systems. This study focuses on an asymmetric deep foundation pit project in a soft soil area, using PLAXIS 3D to model the entire excavation process, with model accuracy confirmed by measured values. The study systematically explores the impact of multiple factors—including surcharge loading, external groundwater level, soil internal friction angle and cohesion, and the elastic modulus and embedment ratio of the retaining structure—on the lateral displacement of retaining piles. Orthogonal experimental design is utilized to calculate lateral displacements for various factor combinations, with sensitivity analyzed using the range method and verified by grey relational analysis. The results demonstrate that all factors influence the maximum lateral displacement of retaining piles to varying degrees. Both the orthogonal tests and range analysis consistently identify the influence ranking as soil internal friction angle > soil cohesion > retaining structure elastic modulus > embedment ratio > external groundwater level > surcharge loading. The grey relational analysis yields identical rankings. These results offer theoretical support and practical guidance for the design and monitoring of retaining structures in asymmetric deep excavations within soft soil environments. Full article

To address the challenges in multimodal information integration and the inefficiency of knowledge transfer in the construction technology disclosure of deep foundation pit projects, an intelligent generation method based on graph rule reasoning and template mapping was proposed. First, a multi-level domain knowledge structure model was constructed by designing domain concepts and relationship types using the Work Breakdown Structure (WBS). Second, entity and attribute extraction was performed using regular expressions and the BERT-BiLSTM-CRF model, while relationship extraction was conducted based on text structure combined with the BERT-CNN model. For image and video data, cross-modal data chains were built by adding keyword tags and generating URLs, utilizing semantic association rules to form a multimodal knowledge graph of the domain. Finally, based on graph reasoning and template mapping technology, the intelligent generation of construction disclosure schemes was realized. The case verification results showed that the proposed method significantly improved the structural integrity, procedural logical consistency, parameter traceability, knowledge reuse rate, environmental compliance, and parameter compliance of the schemes. This method not only promoted the standardization and efficiency of construction technology disclosure activities for deep foundation pit projects but also enhanced the visualization and intelligence level of the schemes. Full article

Understanding the interaction between submerged water jets and cohesive deep-sea sediment is critical for optimizing deep-sea polymetallic nodule hydraulic mining techniques. This research investigated the distinct erosion behavior of cohesive sediments through laboratory experiments and numerical simulations. Cohesive deep-sea sediments were simulated using bentonite–kaolinite mixtures. A series of laboratory experiments, including vane shear tests and viscosity tests under varying moisture content, were conducted to assess the sediments’ mechanical properties. Experimental submerged water jet erosion tests provided basic data for validating the numerical simulations. A Eulerian multi-fluid (EMF) model was implemented to capture sediment–water jet interactions under varying operational parameters, including jet velocities and nozzle heights. The erosion process was found to comprise three distinct stages, including rapid erosion, steady erosion, and stabilization. Two distinct erosion mechanisms were identified, depending on the jet intensity, which affected the depth and shape of the erosion pits. Quantitative analysis revealed that erosion depth exhibits an approximately linear relationship with jet velocity and nozzle height, whereas the erosion diameter shows nonlinear characteristics. These findings enhance the fundamental understanding of cohesive sediment responses under hydraulic disturbances, providing crucial insights for the design and optimization of efficient deep-sea mining systems. Full article