Bearing Failure Research Articles

Evaluation of the Use of the Angular Domain and Order Domain in a Bearing Fault Detection Framework using Deep Learning

Bearing failures are very common in the industrial environment, requiring effective fault detection methods, which can be categorized into physics-based, knowledge-based and data-driven types. Data-driven methods are efficient in differentiating healthy conditions from faulty conditions by characterizing machine signals, involving stages of data acquisition, feature extraction, and condition determination. Traditionally, feature extraction and condition determination were manual, but advances in artificial intelligence and machine learning, especially deep learning, have automated this process. Although deep learning automatically learns the best features from the input data, the signal domain can influence the model's performance. Time and frequency domain representations are widely used in fault detection methodologies using vibration signals, while angular and order domains are more common in variable operating conditions, but direct use of these domains with deep learning is still rare in the literature. Considering this, this study evaluates a bearing fault detection methodology using vibration signals in different domains (time, frequency, angular, and order) under various rotational conditions. Three distinct approaches were tested to assess the effectiveness of these representations. The results indicated that the frequency domain representation had the best overall performance and the study concluded that the angular and order domains do not offer significant advantages compared to the frequency domain. Nonetheless, it is recommended to conduct a more in-depth analysis with more diverse datasets, especially those containing early-stage bearing fault signals.

Mechanical Performance Analysis Method for Ribbed H-Section Aluminum Alloy Members with Initial Curvature and Torsion Angle

In this study, an experimental investigation was conducted on the axial compression performance of ribbed H-section aluminum alloy members with initial curvature and torsion angle under varying boundary conditions, including one end hinged with the other rigidly connected, and both ends rigidly connected. Ultimate bearing capacity and failure modes were identified under real loads and subsequently compared with previous findings from our research group on members with hinged ends. To account for initial imperfections introduced during processing and transportation, 3D scanning technology was utilized to capture the precise geometrical dimensions, constructing an accurate numerical simulation model. The experimental results were corroborated with numerical simulations, leading to the proposal of an analytical method for members with initial curvature and torsion angle. Furthermore, extensive parametric analysis elucidated the impact of initial curvature, torsion angle, and slenderness ratio on the ultimate bearing capacity, culminating in the formulation of the stability factor and calculated length factor based on numerical outcomes. The study discovered significant variances in bearing capacity under different boundary conditions, with one-end hinged and one-section rigidly connected, and two-end rigidly connected conditions exhibiting 1.4 and 2.1 times the capacity of the hinged-at-both-ends scenario. Under different boundary conditions, the axial compression members were subjected to flexural-torsional buckling failure. Moreover, when the ultimate bearing capacity was reached, the lower flange of the member and the web near the lower flange appeared obvious buckling phenomenon. The numerical analysis aligned well with experimental data, validating the simulation method's reliability and revealing the stress distribution and evolution during member failure. These findings offer vital theoretical insights and technical support for engineering design and practical applications.

Mechanical‐performance deterioration of RC segment of shield tunnel under high corrosion degree and the transformation of eccentric‐failure mode

AbstractReinforcement corrosion in shield tunnel segments has an important impact on the structure's bearing capacity and failure mode. Generally, a shield tunnel structure is under small eccentric compression, but if the reinforcement corrosion develops sufficiently, then a transition occurs from small to large eccentric load state, thereby lowering the structural bearing capacity and endangering the safety of tunnel operation. In the study reported here, based on the compressive bending load characteristics of the shield lining structure, corrosion test columns with small eccentric loading were designed to simulate the actual force state of the tunnel lining, and the whole evolution process of their mid‐span strain, ultimate bearing capacity, and cracks under different corrosion degrees was analyzed. With increasing corrosion, the bearing capacity, concrete strain, and depth of the compressive zone of a test column decrease more and more rapidly, and at the maximum corrosion degree of 68.32%, the maximum reduction of bearing capacity is 53.23%. The tests show that the failure mode of a test column with high corrosion degree (≥46.25%) is transformed from small to large eccentric failure. Based on theoretical analysis of the normal section bearing capacity of reinforced concrete flexural members, a theoretical method is proposed for calculating the critical corrosion degree for the tunnel lining structure to transform from small to large eccentric failure. The theoretical calculation method is verified against finite‐element calculations, and the present research results offer theoretical support for the durability design and safety evaluation of shield tunnel lining structures.

Comprehensive Analysis of Needle Bearing Failures in Fuel Transfer Pumps: Insights from Testing and Finite Element Analysis

<div>This study investigates the failure mechanisms of needle bearings within fuel transfer pump assemblies through a comprehensive approach combining endurance testing, detailed inspection, the Dykem blue method, proximity sensors, and finite element analysis (FEA). The findings reveal critical insights into the causes of failure, highlighting significant axial displacement, with a maximum of 0.37 mm measured by proximity sensors. The Dykem technique identified distinct wear patterns across various components, pinpointing areas of high stress and potential failure. Detailed bearing inspections uncovered trunnion damage and abrasive wear, corroborated by FEA, which quantified displacements of 0.144 mm in the x-direction, 0.030 mm in the y-direction, and 0.015 mm in the z-direction. The primary operational factors contributing to bearing failure were contamination and inadequate axial control. These insights are pivotal, as they align with and expand upon established literature on bearing failures, providing a deeper understanding of the interplay between mechanical wear and operational conditions. Despite the robustness of the methodology, challenges included ensuring the accuracy of axial displacement measurements and replicating real-world operational stresses in a controlled environment. The study proposes several recommendations to enhance axial support and optimize system design to mitigate the identified issues. The societal impact of this research is significant, offering potential improvements in machinery reliability, which can lead to enhanced industrial efficiency and safety standards. This work advances the current knowledge in the field and provides practical solutions for extending the lifespan and performance of critical mechanical components in fuel transfer systems.</div>

Wind Turbine Bearing Failure Diagnosis Using Multi-Scale Feature Extraction and Residual Neural Networks with Block Attention

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings.

Experimental study on the seismic performance of two-storey UHPC modular building structure

Ultra-high performance concrete modular building is a new structural system in which the module units made by the factory with UHPC materials are transported to the site for assembly. To study the seismic performance of UHPC module building structures, a two-story full-scale module building structure made by UHPC is designed in this study. Lateral cyclic loading tests are performed to investigate the seismic performance of the module building structures. The bearing capacity, failure mode, stiffness degradation, hysteretic performance, residual deformation, and strain skeleton curve are analyzed. Results indicate that the specimen undergoes three stress stages: elasticity, yield, and ultimate under lateral cyclic loading. At ultimate load capacity, the maximum inter-story drift ratio reached 1/57, and the load-bearing capacity was approximately 1.20 times the yield strength. The average ductility factor of the specimens was 3.42. The cracks mainly appear at the bottom and top of the walls of the module unit, and initial cracks occur at the top of the wall of the upper module unit. The specimen exhibits excellent seismic performance, full hysteretic curves, good dissipation capacity and ductility. The four corners between module units are connected by grouting sleeves in a reliable way to transfer force, which can ensure the overall deformation requirements of the structure.

Bearing performance and failure mechanisms of HSCM piles in marine soft soil under lateral loads

Helix Stiffened Cement Mixing (HSCM) pile represents a novel type of grouted composite pile known for its excellent compressive and tensile performance. Based on field tests, this study introduces a laboratory-based test design methodology and piling technique for HSCM piles. A comprehensive analysis of their horizontal bearing capacity, pile-soil failure mode, optimal piling technique, and reinforcement mechanism is performed. SEM-EDS technology is utilized at a microscopic scale to examine the reinforcement mechanism of HSCM piles. Through ABAQUS finite element simulation, a full-scale model simulating the HSCM pile-soil interaction is developed. The analysis includes pile cross-sectional stress, displacement, and pile-soil failure mode. The p−y curve of the HSCM pile is then plotted, elucidating the reinforcement mechanism of cemented soil. The results indicate that compared to ordinary helical piles, the horizontal bearing capacity of HSCM piles increases by 125.9%. SEM-EDS images reveal that cement and kaolin form a mutual bond, enhancing the strength of the soil around the pile. Additionally, unlike other composite piles, the direction of soil flow around HSCM piles encircles the first helix of the pile core. This research offers a design basis for understanding the horizontal bearing performance and failure mechanisms of HSCM piles in marine soft soil.

Adaptive extraction of characteristic ridges from time-frequency representation for wheelset bearings failure diagnosis under time-varying speed

Instantaneous frequencies-based order tracking is an excellent approach for bearing faults diagnosing at time-varying speeds. The key is to extract ridges from time-frequency representation. However, their precision seriously relies on the cost function and bandwidth selection. To address these issues, an adaptive extraction of characteristic ridges (AECR) method that integrates a variable-bandwidth ridge edge detection (VBRED) and an adaptive exponential cost function (AECF) is presented. VBRED provides a variable-bandwidth search area-based clustering algorithm to isolate neighboring components. AECF can be automatically adjusted based on the signal signature to ensure the smoothness of the ridge. The average frequency ratios of the extracted ridges are used to identify the health conditions of the bearings. Finally, the effectiveness of the proposed AECR is validated by simulations and experiments. The results prove that it performs excellently when the energy and relative position of the ridges are close, compared to other state-of-the-art methods.

The Behavior of Post-installed Rebar Connectors in Steel Frame Retrofitted Concrete Structures Under Combined Shear and Tension

To investigate the response of post-installed rebar connectors in steel frame retrofitted concrete structures under combined shear and tension, seven shear-and-tensile tests were conducted. The bearing capacity, stiffness, and hysteresis behavior of the connectors were investigated during the tests. Both Monotonic and cyclic load regimes were applied in the loading process. The results show that with the increase of the loading angle, the deformation capacity, the yield force, ultimate tensile force, and tensile stiffness of the specimen decrease, while the maximum shear force and shear stiffness of the specimen increase. The bearing capacity, stiffness, and energy dissipation performance of the specimen under a monotonic load is greater than that under the coupled shear-and-pull load. The bearing capacity, stiffness, hysteresis curve, and failure status of the specimens are not affected by different loading regimes. At the end, the design formula of anchor bolt for this type of connectors from the design code of China, United States, and Japan were compared. Formulas to calculate the shear strength under combined shear and tension is developed.

Operational excellence in total productive maintenance: statistical reliability as support for planned maintenance pillar

PurposeTotal productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.Design/methodology/approachIn this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.FindingsOur findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.Originality/valueIn terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.

Grouting sleeve/bird’s nest anchor cable anchoring capacity characteristics and numerical simulation study

With the deepening of coal mining resources, the safety issues of broken soft coal seams become increasingly prominent, severely affecting the safety of deep mining operations and workers. Traditional anchoring methods alone are ineffective in achieving secure anchorage. Therefore, the grouting anchor cable composite structure is explored to address the safety of roadway excavation in deep broken soft coal seams. Indoor grouting anchorage tests and pull-out tests for grouted anchorages are conducted to study their bearing characteristics and failure mechanisms. Discrete element numerical simulation methods are employed to verify the findings from indoor tests. Research indicates that the ultimate pull-out forces at grouting pressures of 0.5, 1.0, 1.5, and 2.0 MPa are 87, 104, 118, and 131 kN, respectively. The pull-out force of the grouted anchorage is positively correlated with the grouting pressure. At the loading end, shear stress and axial force reach their maximum values of 1.8 MPa and 106 kN, respectively, under 2.0 MPa grouting pressure. The distribution patterns of shear stress and axial force decrease gradually with increasing anchoring depth. Microscopic analysis reveals that the failure of the anchor body model is primarily due to the tensile cracks formed by the particle flow code in two dimensions. This finding is consistent with the failure mode observed in indoor tests, which involves the slip and debonding failure of the anchor cable. Crack evolution is categorized into three stages: initiation, continuous propagation, and anchor failure. As the inter-particle contact forces within the anchor body model decrease, the region of chain fracture expands. These results provide technical support for understanding the bearing characteristics of grouting anchor cable composite structures.

Multi-scale analysis of ultimate bearing capacity in composite hull joints: failure mechanism and numerical simulation

ABSTRACT Progressive failure analysis method is the main method to analyse the ultimate bearing capacity and failure mode of composite structures. Multi-scale method can effectively reduce the dependence of damage mode and stiffness degradation analysis on material parameters. Based on the multi-scale method, from micro-scale to mesoscale and then to macro-scale, the equivalent modulus and equivalent strength of fibre bundles, single-layer laminates and multi-directional laminates were predicted respectively, and the prediction method of ultimate bearing capacity of large-scale composite structures from material level to structure level was constructed. The mechanical properties of standard composite materials and the ultimate bearing capacity of full-scale composite joints were tested respectively, and the ultimate bearing capacity and failure mode of L-joints were predicted by numerical method. The results show that the numerical simulation method based on multi-scale can accurately predict the ultimate bearing capacity and failure mode of large-scale joints of the hull.

Mechanism analysis of burn-up failure for needle roller bearing at high-speed gear in manual transmission

Adequate lubrication conditions of needle roller bearings are crucial for the normal operation of transmission. Addressing the burn-up and adhesion failures of needle roller bearings during transmission bench trials, two rigid body rotation physical models were developed to analyze and model the lubrication effect on the inner and outer surfaces of needle roller bearing. Through theoretical analysis and lubrication simulation, it was found that under the critical condition of d/D=0.553, the centrifugal force F on the outer ring surface of the needle bearing was related to the density of lubricant, rotational speed, as well as the angle and size of oil hole, rather than the position of the oil holes; Moreover, the centrifugal force F on the needle roller bearing at input shaft was related to the speed of transmission gear and was also associated with the clearance size between needle roller and cage. Theoretical analysis, simulation, and experimental results have demonstrated that the lubrication performance of needle roller bearings during operation at lower rotary speed gears was inferior to that at higher speed gears, and the lubrication was also less effective when the cage clearance was narrower. Employing angled oil inlets (holes), appropriately enlarging the diameter of oil inlets (holes), and increasing the clearance width between needle roller and cage will contribute to the improvement of lubrication effect.

Research on the service life of bearings in the gearbox of rolling mill transmission system under non-steady lubrication state

A rolling bearing plays a key role in the gearbox of the cold rolling mill transmission system. As a result, the degradation and failure of bearings can lead to unplanned shutdowns of the entire rolling mill system. Since on-site cold rolling mills work usually in non-steady lubrication rolling conditions originating form variations of multiple parameters, the uncertainty affecting bearing performance in a cold rolling mill transmission system increases, making it more difficult to assess health status and predict the remaining service life of bearings, Therefore, by establishing a coupling model describing the relationship for the rolling mill and normal /faulty gearboxes under non steady rolling conditions, quantitatively studies the influence of various rolling parameters of a rolling mill on the fatigue service life of bearings in the gearbox of the rolling mill transmission system, and verify the effectiveness of linear cumulative damage theory for bearing fatigue service life through experiments and on-site bearing vibration data. The results indicate that as the thickness of the strip steel inlet, lubricant viscosity, and rolling speed increase, or the thickness of the strip steel outlet and rolling roll radius decrease, the service life of bearings gradually decreases. As the fluctuation amplitude of various rolling parameters in the cold rolling mill increases, the service life of bearings in the gearbox of the rolling mill transmission system gradually shortens. Moreover, when the rolling parameter value or fluctuation amplitude increases to a specific values, the remaining service life of bearings will sharply decrease. Under the same rolling conditions, the service life of bearings in gearboxes with faults decreases more significantly than that in normal gearboxes.

Research on cyclic behavior of precast bridge piers with ECC-socket connections

Precast bridge piers with socket connections (SCPs) have been widely studied and certainly applied in recent years. To reduce the punching failure of socket connections, decrease their construction difficulty, and improve their application in complex environment areas, the embedment depth of SCPs needs to be decreased and the cyclic behavior of SCPs needs to be improved. In this study, engineered cementitious composite (ECC) was firstly introduced to the SCPs, a novel precast bridge piers with ECC-socket connections (SCEs) were proposed. Subsequently, 3D finite element models (FEMs) were established and validated against experimental results. Then, a detailed parametric study was conducted to investigate the cyclic behaviors of SCEs, including the embedment depth, ECC height, axial compression ratio, reinforcement ratio, and concrete compressive strength. Finally, a simplified calculation model was proposed to evaluate the bearing capacity and failure mode of SCEs. The results show that the shear damage of the embedded section is more severe for traditional shallow socket connections. Compared to traditional SCPs, the cyclic behavior of SCEs is better and influenced by ECC height and embedment depth, as well as axial compression ratio, reinforcement ratio, and concrete compressive strength. The simplified calculation model is suitable to evaluate the bearing capacity and failure mode of SCEs.

Effect of soil spatial variability on the stability of pipelines under horizontal loading

Submarine pipeline has been utilized more and more widely in offshore oil and gas transportation engineering. Given that these pipelines frequently subject horizontal loading during operation, thus precise predicting their horizontal bearing capacity is essential. However, most previous studies employ deterministic analysis without considering soil spatialvariability. This article introduces a two-dimensional (2D) random finite element analysis to study the horizontal pullout behaviour and failure mechanisms of a pipeline in a spatial variability soil. It is observed that the effect of soil spatial variability on the horizontal bearing capacity factor and failure mechanisms is remarkably substantial, with the distribution of the former showing a close approximation to a log-normal distribution. Furthermore, the average horizontal bearing capacity typically falls below the corresponding deterministic value. Consequently, the deterministic estimation of the horizontal bearing capacity tends to be inflated. Eventually, a reliability-based approach is formulated, where a factor of safety is incorporated to account for the uncertainties arising from spatial variability and ensure a sufficient safety margin. This methodology enables engineers to determine appropriate factor of safety tailored to the specific requirements of each project.

Dual neural network for contact temperature and cooling state monitoring in water-lubricated bearings

Harsh operating conditions subject the water-lubricated stern bearings of a ship to a non-uniform stress field, leading to frequent boundary lubrication between the bearing and shaft. Poor lubrication results in local temperature rise due to friction heat, leading to heat aging or even carbonization of the bearings. This study proposes a novel universal monitoring approach based on the internal temperature of water-lubricated bearings (WLBs). Radial basis function neural networks (RBFNN) were established to evaluate the surface temperature and cooling statuses of the bearing surface during rubbing, in which simulation results obtained from a temperature model were used as a training set. The accuracy of the simulation results and prediction model was verified through experimental data. The results showed that the maximum error of predicted contact surface temperatures is only 0.84 °C, 0.02 °C, and 1.00 °C in MAE, MAPE, and RMSE, respectively. The accuracy of cooling conditions identification can be achieved at 91.7 % with 600 s of continuous temperature data. The scheme proposed in this study can effectively predict thermal failures of water-lubricated bearings without requiring extensive experimentation, which promotes the development of intelligent water-lubricated bearings.

Condition monitoring for planetary journal bearings in wind turbine gearboxes by means of acoustic measurements and machine learning

The use of journal bearings instead of rolling bearings as planetary bearings in wind turbine (WT) gearboxes is advantageous for the power density of the drive train. In addition, they can increase the reliability of the gearboxes as they can be operated wear-free in hydrodynamic operation, i.e. with fluid friction. The dynamic loading conditions in wind turbines, as well as special conditions such as insufficient lubrication and particle contamination, can lead to mixed friction operation and consequently to wear in the journal bearings. If mixed friction is not detected, damage and failure of the journal bearing and consequently of the WT gearbox may occur. Therefore, it is required to develop a Condition Monitoring System (CMS) to detect mixed friction in the journal bearings during operation of the WT. Preliminary investigations have shown that various friction conditions can be detected by acoustic measurements in combination with machine learning classifiers. Current investigations on CMS methods for journal bearings using acoustic measurement methods are limited to component level applications. A condition monitoring methodology for wind turbine gearbox journal bearings does not currently exist. A major challenge for CMS development for journal bearings in WT gearboxes is the transfer of methods already proven at component level to gearbox applications. In preparation for CMS application at the gearbox level, this paper presents an approach for monitoring different friction conditions of journal bearings based on acoustic measurements at a component test rig. For the classification of the friction state, different machine learning (ML)-based approaches trained on the acquired acoustic measurement data are compared with respect to the achieved classification accuracy. Knowledge of the robustness of the classification method, e.g. with respect to the distance of the sensor to the bearing, provides the necessary basis for the use of the CMS at the gearbox level. The investigations are carried out under operating conditions typical for planetary bearings in wind turbines. Classification performance is evaluated using a validated elasto-hydrodynamic simulation model. The aim of the work is to develop a method that detects friction classes in the journal bearing based on structure-borne sound measurements. Here, simulation results are used to train the algorithms. Finally, the demonstrated method will be successfully applied to a test rig for wind turbine gearbox journal bearing. Based on the results, an ML approach will be selected for application in gearboxes.

Free-Drop Experimental and Simulation Study on the Ultimate Bearing Capacity of Stiffened Plates with Different Stiffnesses under Slamming Loads

Differing from previous studies on free-drop tests, this study focuses on the ultimate bearing capacity and failure mechanism of the ship’s bow under slamming loads. A prototype ship’s bow is selected to design two simplified stiffened plates with different stiffeners, and the lateral slamming loads used are equivalent to flare slamming loads. Free-drop tests of the two simplified models are conducted, and the test setups and procedures are provided. The experimental results of slamming pressures and structural responses are obtained. By comparing with the simulation results obtained by Arbitrary Lagrangian-Eulerian (ALE) fluid–structure coupling, the convergence study, symmetry, and independence verifications are carried out. Finally, the dynamic ultimate bearing capacity of stiffened plates with different stiffnesses under lateral slamming loads is studied. The results show that stiffeners enhance the ability of stiffened plates to resist plastic deformation under slamming loads, and T-section stiffeners can provide greater resistance to plastic deformation than other types.

Global buckling of S35657 austenitic stainless steel welded box- and I-section long columns under axial compression

Due to its remarkable combination of high yield strength and cost-effectiveness, S35657 austenitic stainless steel has gained increasing attention in the field of construction engineering. This paper delves into the global stability performance of S35657 stainless steel welded long columns. Leveraging test results from 13 welded long columns, consisting of 4 welded box-section columns and 9 welded I-section columns, a numerical simulation analysis was conducted to scrutinize the bearing capacity and failure modes of each specimen. Utilizing validated numerical models, a parametric analysis was undertaken, exploring factors such as cross-sectional classification, slenderness ratio and initial imperfection. Subsequently, the buckling curves for S35657 austenitic stainless steel welded box-section and I-section long columns, both around the major and minor axes, were evaluated using existing test results and numerical outcomes. Through data fitting and reliability analysis, buckling curves tailored to S35657 austenitic stainless steel welded box-section and I-section long columns for global buckling were proposed for the first time, providing guidance for relevant designs based on the European code (prEN 1993-1-4), the American code (SEI/ASCE 8-22) and the Chinese specification (CECS 410: 2015).