Finite Element Modeling Approach Research Articles

Finite element modelling approaches for sheet metal joining: A review of techniques and application

Joining for sheet metal is a widely used process in product manufacturing in diverse industries such as automotive, aerospace, electronics, constructions and others. Finite element has become a vital tool when it comes to simulating the structural integrity and performance of many types of joining that are being used such as mechanical fastener, welding, adhesives and many more. This review paper provides a comprehensive overview of finite element modelling techniques applied to sheet metal joining based on existing studies from other researchers. The paper discusses the methods, advantages and limitations for each modelling approaches and highlights their key applications. This paper also discusses the role of finite element analysis when studying the performance of these models for joints. This will enable researchers to predict failure behaviours and enhance design efficiency. The strengths and limitations of various modelling approaches such as simplified and detailed methods and identified key areas for future research and development were also examined. By summarising the current knowledge and methodologies, this paper aims to contribute to the advancement of joint modelling techniques, which can lead to more accurate and reliable modelling method for joining in sheet metal.

Analysis the impact of ground conductivity source current speed and source current rise time on peak induced voltage on overhead conductors

The paper presents a finite element modeling approach to analyze lightning-induced voltages (LIV) on overhead conductors, focusing on factors such as the distance from the strike point, ground conductivity, source current speed, and current rise time. Firstly, the return stroke current (RSC) at various altitudes throughout the lightning path and the electromagnetic field radiation surrounding the strike point have been thoroughly examined. Secondly, the influence of ground conductivity on peak-induced LIV on overhead conductor, with specific numerical findings (e.g., peak-induced voltages reaching 109.9 kV at 40 m horizontal distance from the strike point for finite ground conductivity; at the same horizontal distance, peak-induced voltages reaching 95.9 kV for infinite ground conductivity). Thirdly, it is observed that at different horizontal distances from the strike point, the peak-induced voltages on overhead conductors increase if the source current speed increases. Additionally, the impact of source current rise time on peak-induced voltages of an overhead conductor has been noted and found that if the source current rise time increases, peak-induced voltages decrease. When developing an overhead conductor protection strategy and creating a mechanism of failure, the suggested model and analysis would be helpful.

Process capability analysis of additive manufacturing process: a machine learning−based predictive model

Purpose Predicting dimensional and geometrical errors in 3D printing parts during the design stage can significantly enhance the product’s quality. This study aims to predict the form deviation and process capability in additive manufacturing (AM) specimens considering layer thickness, laser power and scan speed parameters in the laser powder bed fusion (LPBF) method. Various machine learning (ML) techniques are implemented to estimate the form deviation and process capability with the highest accuracy in 3D-printed cylindrical parts as a case study. Design/methodology/approach The workflow started by simulating the LPBF AM process using a finite element modeling approach. Then, different ML algorithms like artificial neural networks are used to predict the form deviation. The process capability value is forecasted using some classification ML models and process capability indices (PCIs) for cylindrical parts. Finally, concentricity tolerance classification is performed for cylindrical parts, which can ensure quality control issues in the production stage. Findings Results present an accuracy of about 93% for predicting form deviations and 95% accuracy for predicting PCI C_pm in PCI classification based on random forest model as an ML algorithm. Originality/value The noteworthy point of the research is accessing the form deviation due to AM and process capability evaluation in the AM process before the production stage, which has not been studied before based on the author’s knowledge. So that the product quality is evaluated based on the shape deviation and its tolerances in the AM process digital chain.

Elasticity solution of fixed beams under linear temperature variation: An experimental and numerical study

This article presents the Airy stress function method for predicting the structural response of a fixed beam subjected to a linear temperature distribution, using the superposition principle. In this approach, the fixed-end beam is decomposed into three simply supported beams with unknown reactions and a linear temperature variation. For each loading condition, the suitable Airy’s stress functions were formulated to satisfy the stress equilibrium and strain compatibility equations. The results obtained using this method are subsequently contrasted with those derived from finite element modeling. To validate the analytical and experimental results, two finite element modeling approaches, namely two-dimensional and three-dimensional modeling, are implemented using the ANSYS finite element software. This numerical modeling approach utilizes a sequentially coupled steady-state thermal and static structural analysis. In addition to employing Airy’s stress function method and finite element analysis, experiments were conducted on SS304 rectangular specimens under proper insulation to investigate their thermal bowing response. Samples were tested in a simple thermal bending experimental setup under fixed support conditions, and subjected to conduction-type heating to achieve linear temperature changes along their depth. The analytical method predicts that the beam will not deflect under linear temperature variation in a fixed-beam situation. The numerical analysis results confirm this, showing a minimal deflection, that aligns well with the Airy’s stress function method. However, the deflection measured in the experimental program is significantly larger than the predictions from the analytical method. This method eliminates the complexity involved in applying boundary conditions along with thermal loading on a fixed beam.

Investigation of surface roughness induced attenuation of reflected waves using a quasi-Monte Carlo method

Abstract An understanding of the influence of surface roughness on wave scattering and accurate predictions of wave amplitudes are crucial for quantitative ultrasonic nondestructive testing and evaluation. In this work, the effects of surface roughness on the reflection coefficient are investigated using a quasi-Monte Carlo (QMC) method. The wave fields reflected from smooth and rough interfaces with an immersion transducer are modeled using the Rayleigh integral method, and the solutions are efficiently calculated using the QMC method for interfaces constructed using pseudo-random samples. The reflected wave fields are simulated and presented, and the properties of coherent and incoherent waves affected by interface roughness are discussed. The surface roughness induced attenuation of reflected waves is calculated using the ratio of received pressures for waves reflected from rough and smooth interfaces, and the predicted results are compared with those obtained using other recognized methods. It is shown that at low levels of roughness, excellent agreement is obtained between the results from the QMC method and the well-known Kirchhoff approximation, while for high levels of roughness, where the Kirchhoff theory gives pessimistic results, the predicted values agree well with those simulated using a finite element modeling approach, thus verifying the effectiveness of the proposed method.

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery.

Quantifying the mechanical properties of coronary arterial walls could provide meaningful information for the diagnosis, management, and treatment of coronary artery diseases. Since patient-specific coronary samples are not available for patients requiring continuous monitoring, direct experimental testing of vessel material properties becomes impossible. Current coronary models typically use material parameters from available literature, leading to significant mechanical stress/strain calculation errors. Here, we would introduce a finite element model-based updating approach (FEMBUA) to quantify patient-specific in vivo material properties of coronary arteries based on medical images. In vivo cine intravascular ultrasound (IVUS) and virtual histology (VH)-IVUS images of coronary arteries were acquired from a patient with coronary artery disease. Cine IVUS images showing the vascular movement over one cardiac cycle were segmented, and two IVUS frames with maximum and minimum lumen circumferences were selected to represent the coronary geometry under systolic and diastolic pressure conditions, respectively. VH-IVUS image was also segmented to obtain the vessel contours, and a layer thickness of 0.05 cm was added to the VH-IVUS contours to reconstruct the coronary geometry. A computational finite element model was created with an anisotropic Mooney-Rivlin material model used to describe the vessel's mechanical properties and pulsatile blood pressure conditions prescribed to the coronary luminal surface to make it contract and expand. Then, an iterative updating approach was employed to determine the material parameters of the anisotropic Mooney-Rivlin model by matching minimum and maximum lumen circumferences from the computational finite element model with those from cine IVUS images. This image-based finite element model-based updating approach could be successfully extended to determine the material properties of arterial walls in various vascular beds and holds the potential for risk assessment of cardiovascular diseases.

Numerical Modelling of the Punching Shear Response of Shearhead Connections between a CFT Column and a Flat Reinforced Concrete Slab

In this article, the punching shear response of shearhead connections between a Reinforced Concrete (RC) flat slab and interior Concrete-Filled steel Tubular (CFT) columns under static load is investigated by using a 3D nonlinear finite element modelling approach. The accuracy of the numerical model is verified against recent experimental results by comparing the results of the load–displacement relationship, failure models and cracking patterns. Based on validated numerical models, parametric investigations are conducted to examine the effects of slab thickness, concrete compressive strength, length of shearhead arm, size of shearhead cross section and flexural reinforcement ratio on the punching shear response of the connections. Then, some recommendations are given for the design of the effective parameters of shearhead connections, particularly focusing on the effective length and cross-section size of shearhear arms. Finally, a design equation considering the effect of the effective length of shearhead arms and flexural reinforcement ratio is proposed to predict the punching shear strength of shearhead connections. The comparison of punching shear strengths predicted by the proposed equation and some design codes shows that the proposed equation can predict the punching shear strength of shearhead connections more accurately than those from ACI 318-14 and Eurocode 2.

Seismic Fragility Analysis of Offshore Wind Turbines Considering Site-Specific Ground Responses

This study investigated the seismic performance and assessed the seismic fragility of an existing pentapod suction-bucket-supported offshore wind turbine, focusing on the amplification of earthquake ground motions. A simplified suction bucket–soil interaction model with nonlinear spring elements was employed within a finite element framework, linking the suction bucket and soil to hypothetical points on the OWT structures at the mudline. Unlike conventional approaches using bedrock earthquake records, this study utilized free-field surface motions as input, derived from bedrock ground motions through one-dimensional wave theory propagation to estimate soil-layer-induced amplification effects. The validity of the simplified model was confirmed, enabling effective assessment of seismic vulnerability through fragility curves. These curves revealed that the amplification effect increases the vulnerability of the OWT system, raising the probability of exceeding damage limit states such as horizontal displacement of the tower top, tower stress, and horizontal displacement at the mudline during small to moderate earthquakes, while decreasing this likelihood during strong earthquakes. Comparisons between the Full Model and the simplified Spring Model reveal that the simplified model reduces computational time by approximately 75%, with similar seismic response accuracy, making it a valuable tool for rapid seismic assessments. This research contributes to enhancing seismic design practices for suction-bucket-supported offshore wind turbines by employing a minimalist finite element model approach.

Design and validation of finite element models for the assessment of post-fixation distal femur fracture motion.

Fracture site motion is thought to play an important role in the healing of complex fractures of the distal femur via mechanotransduction. Measuring this motion in vivo is challenging, and this has led researchers to turn to finite element modeling approaches to gain insights into the mechanical environment at the fracture site. Developing a systematic understanding of the effect of different model choices for distal femur fractures may allow more accurate prediction of fracture site motion from these types of simulations. In this study, we aim to assess the effect of four different modeling choices and parameters. We looked at the effect of using bone specific density distributions vs generic values, employing landmark-based geometry generation, varying fracture alignment within clinically relevant ranges, and determining whether direct apposition of the fracture to the plate was achieved. For validation, five cadaveric femurs had fractures created and repaired with plated constructs, and these were then loaded and fracture site motion was directly measured. We found that using landmark based bone geometry and patient-specific bone density distributions had a minimal effect on the overall model predictions. Changing the alignment, particularly into varus and procurvatum could have a large (>50%) effect on predicted shear motion, as could direct apposition of the bone to the plate. These findings demonstrate that modeling choices can play an important role in simulating distal femur fracture mechanics, and it is particularly critical that patient customized models attempt to accurately represent alignment of the bone fragments and lateral plate apposition.

Utilization finite element and machine learning methods to investigation the axial compressive behavior of elliptical FRP-confined concrete columns

Nowadays, elliptical sections are among the geometric shapes that are becoming more and more popular in the architecture world. Finite element analysis (FEA) software, such as ABAQUS, is used to simulate elliptical concrete columns confined with fiber reinforced polymer (FRP) jackets based on experimental data published in the literature. The validity of the finite element modeling (FEM) approach was confirmed by comparing it to experimental data obtained from 45 specimens in existing studies, demonstrating a favorable agreement. Additionally, a parametric study was also carried out, which included simulating 40 more specimens, resulting in a total of 85 specimens for analysis. The effect of various test variables such as sectional aspect ratio, the amount of FRP layers, and concrete strength on the elliptical column’s behavior is investigated. The axial compressive strength is predicted using current models from the literature. The outcomes revealed that the higher unconfined concrete strength decreased FRP confinement efficacy. Insufficient confinement with post-peak softening is more likely in FRP-confined high strength concrete columns, especially if the confinement was not stiff enough. Also, by adding more FRP layers, the stress and strain capabilities of elliptical concrete columns confined with FRP composites are improved. Physical experiments require a significant investment of time and resources, but they can produce valuable results. Finite element analysis, on the other hand, depends heavily on the modeler's competence and can minimize the number of experiments required by employing computer simulation, but it also requires high computer settings. In recent years, there has been a major advancement in the usage of machine learning (ML) algorithms in the applications of FRP composites with different concrete components. Taking this into consideration, this investigation is extended to estimate the compressive strength of elliptical FRP-confined columns using four tree-based ML algorithms including Decision Tree, Random Forest, Gradient Boosting and XGBoost. The XGBoost model achieved the highest predictive accuracy with the R2 values of 0.95 for both the compressive strength and axial strain targets.

Numerical Investigation of the Cohesive Strength Regime of the Bilobated Arrokoth after the Sky-crater-forming Impact Event

In 2019, NASA’s New Horizons mission, using the Long Range Reconnaissance Imager, revealed Arrokoth’s bilobated shape and a large impact-crater-like region (“Sky”) on the small lobe, which is ∼7 km wide and ∼1 km deep. Given that this depression takes up ∼7% of the entire volume of the small lobe, Arrokoth’s neck, the most structurally sensitive area to failure, might have been subject to substantial structural modification if the Sky-crater-forming event occurred after the bilobate shape had formed. Using the π-scaling law, we quantified the linear momentum imparted to the small lobe by the Sky-crater-forming event, which was in the range of (2.4–4.0) × 1013 kg m s−1, depending on Arrokoth’s bulk density of 250–500 kg m−3 and impact speeds of 100 m s−1, 300 m s−1, and 1 km s−1. If the linear momentum was fully transferred to Arrokoth’s small lobe, it would have given the small lobe an impulse velocity of approximately 0.1 m s−1 relative to the large lobe. To assess the structural impact of this event, we used a finite-element modeling approach to simulate post-impact stress fields driven by the estimated impulse velocity on the small lobe and constrained the critical cohesive strength required to prevent structural failure. Based on the current parameter space, our results suggest that the Sky-crater-forming event could have required the critical cohesive strength of up to ∼20 kPa for Arrokoth’s neck to avoid structural failure, which is higher than the typical cohesive strength estimated for small bodies (usually less than 1 kPa for asteroids and comets).

Effect of patient specificity on predicting knee cartilage degeneration in obese adults: Musculoskeletal finite-element modeling of data from the CAROT trial.

Obesity is a known risk factor for development of osteoarthritis (OA). Numerical tools like finite-element (FE) models combined with degenerative algorithms have been developed to understand the interplay between OA and obesity. Inthisstudy,we aimed to predict knee cartilage degeneration in a cohort of obese adults to investigate the importance of patient-specific information on degeneration predictions. We used a validated FE modeling approach and three different age-dependent functions (step-wise, exponential, and linear) to simulate cartilage degradation under overloading in the knee joint. Gait motion analysis and magnetic resonance imaging data from 115 obese individuals with knee OA were used for musculoskeletal and FE modeling. Cartilage degeneration predictions were contrasted with Kellgren-Lawrence (KL)and Boston-Leeds Osteoarthritis Knee Score (BLOKS) grades. The findings show that overall, the similarities between numerical predictions and clinical measures were better for the medial (average area under the curve(AUC) = 0.62) compared to the lateral compartment (average AUC = 0.52) of the knee. Classification results for KL grades, full patient-specific models and patient-specific geometry with generic gait data showed higher AUC values (AUC = 0.71 and AUC = 0.68, respectively) compared to generic geometry and patient-specific gait (AUC = 0.48). For BLOKS grades, AUC values for both full patient-specific models and for patient-specific geometry with generic gait locomotion were higher (AUC = 0.66 and AUC = 0.64, respectively) compared to when the generic geometry and patient-specific gait were used (AUC = 0.53). In summary, our study highlights the importance of considering individual information in knee OA prediction. Nevertheless, our findings suggest that personalized gait play a smaller role in the OA prediction and classification capacity than personalized joint geometry.

Research on Coordinated Relationship Between Deformation and Force in Shaft Foundation Pit Support Structures

In order to investigate the coordinated relationship between lateral deformation of the diaphragm wall and axial force of the internal strut, this paper first carried out a scaled model test on the mechanical features of a foundation pit support system based on a novel axial force servo device. Then, a finite element model was established to simulate the scaled model test, and the correctness of the finite element modeling approach was validated by comparing test results. After that, the same finite element modeling method was used to analyze the coordinated relationship between axial force and lateral deformation in the prototype foundation pit support structure. The results show that the axial force of the inner strut is negatively correlated with the lateral deformation in the diaphragm wall. The initial maximum lateral deformation in the diaphragm wall of the shaft foundation pit occurs at the bottom of the foundation pit, so changing the length of bottom strut simultaneously is the most effective way to adjust the mechanical behavior of the support structure. Under various support conditions, the maximum lateral deformation of the diaphragm wall in the prototype project is 0.59~0.66‰ of the total excavation depth of the foundation pit, and the maximum axial force of internal support is 11~30% of the yield load of a single steel strut.

Axial compression behaviour of square concrete-filled titanium-clad bimetallic steel tubular stub columns

This paper aims to study the axial compression behaviour of square concrete-filled titanium-clad bimetallic steel tubular (CFTCBST) stub columns. A total of five specimens with varying width-to-thickness ratios are manufactured and are tested by axial compression loading herein. A three-dimensional finite element (FE) model is developed and validated against both dependent and independent test data to further study the axial compression behaviour of square CFTCBST stub columns in this paper, and the cold-formed effect of the titanium-clad (TC) bimetallic steel tube on the axial behaviour of the CFTCBST stub columns is discussed especially. The parametric investigation considering five major parameters, including the concrete strength, the substrate steel grade, the clad ratio, and the width-to-thickness ratio, are carried out using the validated FE modelling approach. Finally, the applicability of existing design methods for predicting the axial compression capacity of square CFTCBST stub columns is further analysed through comparisons with the FE results, and it is found that the Chinese standard DBJ/T 13–51-2020 has the most accurate prediction. Overall, a modified design method adapted from DBJ/T 13–51-2020 was proposed for square CFTCBST stub columns.

Biomechanical significance of intervertebral discs on growthplate stresses in scoliotic trunks following unilateral muscle weakening: A hybrid approach of finite element and musculoskeletal modeling.

This study aimed to ascertain the relevance of intervertebral discs (IVD) in the stress distribution on growthplates (GPs) of a trunk model with adolescent idiopathic scoliosis (AIS) following a unilateral weakening of muscles. A thoracolumbar spine finite element (FE) model of a young female healthy and an AIS spine comprising GPs linked to the T12 through sacrum vertebrae. Two scenarios of including (FEI) and excluding (FEE) IVDs were considered. Then, using optimization-driven musculoskeletal models of the AIS and healthy trunks, the FE models were examined under subject-specific muscle forces and gravity loads. Results of this study demonstrate that when IVDs included in the FE model, an increase, ranging from 0.2 to 1.7 MPa, with the highest value occurring at the apex of the AIS model, in the von Mises stresses in the GPs. The ratio of 1.5 was found for the maximum von-Mises stress value on the most tilted GP in the FEI over the FEE model. Unilateral paralysis of muscles caused a reduction of 50% and 63% in the von Mises stress ratio of the concave-over-convex side of the most tilted GP in the FEI and FEE models of the AIS spine with healthy muscles, respectively. The intradiscal pressures, found for FEE and FEI models, assented to recent in-vivo investigations. Nonetheless, employing IVDs in the simulations provides an indispensable tool to anticipate the effects of neuromuscular disorders on GP stresses in an AIS spine and predict deformity progression during growth.

Preliminary Study on Multi-Scale Modeling of Asphalt Materials: Evaluation of Material Behavior through an RVE-Based Approach

This study employs a microstructure-based finite element modeling approach to understand the mechanical behavior of asphalt mixtures across different length scales. Specifically, this work aims to develop a multi-scale modeling approach employing representative volume elements (RVEs) of optimal size; this is a key issue in asphalt modeling for high-fidelity fracture modeling of heterogeneous asphalt mixtures. To determine the optimal RVE size, a convergence analysis of homogenized elastic properties is conducted using two types of RVEs, one made with polydisperse spherical inclusions, and another made with polydisperse truncated cylindrical inclusions, each aligned with the American Association of State Highway and Transportation Official’s maximum density gradation curve for a 12.5 mm Nominal Maximum Aggregate Size (NMAS). The minimum RVE lengths for this NMAS were found to be in the range of 32–34 mm. After the optimal RVE size for each inclusion shape is obtained, computational models of heterogeneous Indirect Tensile Asphalt Cracking Test samples are then generated. These models include the components of viscoelastic mastic, linear elastic aggregates, and cohesive zone modeling to simulate the rate-dependent failure evolution from micro- to macro-cracking. Examination of load-displacement responses at multiple loading rates shows that both heterogeneous models replicate experimentally measured data satisfactorily. Through micro- and macro-level analyses, this study enhances our understanding of the composition-performance relationships in asphalt pavement materials. The procedure proposed in this study allows us to identify the optimal RVE sizes that preserve computational efficiency without significantly compromising their ability to capture the asphalt material behavior under specific operational conditions.

Hybrid Continuum and Multi‐Particle Simulation of RDX Powder Subjected to Drop Impact

AbstractA variety of energetic material (EM) devices could be designed more efficiently with a more rapid protocol of modeling and testing if computational tools are more predictive. Drop weight impact tester (DWIT) is widely used to experimentally study energetic material response to impact. Drop weight impact on a thin layer of RDX powder is studied here and computationally modeled to compute the likelihood of EM powder ignition from low‐speed impact. The overall objective is to develop a thermomechanical‐based FEM methodology that can qualitatively predict ignition‐likelihood to study iginition mechanics in EM powders and understand how anvil properties and striker impact conditions influence it. The mechanical confinement conditions offered by an anvil and striker type, influence energy transmission and heat localization within the energetic powder. Efficiently modeling both the bulk behavior and the mesoscopic behavior, such as, localized shear and particle‐to‐particle frictional heating, is necessary to design insensitive EM‐devices. A 3D finite element modeling (FEM) approach is described that more accurately models the impact conditions than previously developed 2D models. The results are compared with the experimental results and the 2D simulations. A continuum‐based explicit FEM can only simulate the bulk‐behavior of the powder. To accurately predict hot‐spot ignition mechanisms, meso‐scale multi‐particle models of the EM is needed. For computational efficiency, hybrid models that combines continuum and multi‐particle FEM (MPFEM) were utilized. The computed temperature profiles are compared for two anvil types and demonstrates that an assessment of ignition likelihood is viable with the hybrid methodologies proposed.

Prediction of Ductile Damage in Composite Material Used in Type IV Hydrogen Tanks by Artificial Neural Network and Machine Learning with Finite Element Modeling Approach

This study investigates the degradation process of composite materials used in high‐pressure hydrogen storage vessels by employing advanced computational techniques. A recurrent neural network, specifically a bidirectional long short‐term memory (Bi‐LSTM) network, is utilized to predict the temporal evolution of ductile damage. The key degradation features are extracted from finite element modeling (FEM) computations using group method of data handling algorithms and treated as time‐series data. Results demonstrate that the Bi‐LSTM network can accurately undergo both elastic and plastic behaviors of the composite under tensile strength. Additionally, traditional machine learning (ML) algorithms such as extreme gradient boosting and random forest are employed to forecast strain degradation, showing promising results. This hybrid approach combining FEM, ML, and deep learning provides a comprehensive method for predicting the degradation of composite materials, offering significant potential for optimizing the design and durability of hydrogen storage vessels.

Tough Monolayer Silver Nanowire-Reinforced Double-Layer Graphene.

Mixed-dimensional nanomaterials composed of one-dimensional (1D) and two-dimensional (2D) nanomaterials, such as graphene-silver nanowire (AgNW) composite sandwiched structures, are promising candidates as building blocks for multifunctional structures and materials. However, their mechanical behavior and failure mechanism have not yet been fully understood. In this work, we have performed integrated experimental, theoretical, and numerical studies to explore the performance and failure modes of graphene-AgNW composite under tensile and impact loading conditions. In situ tensile tests using a nanoindenter, implemented with a push-to-pull device and a laser-induced projectile impact test system, are used to shed light on load-bearing mechanisms in graphene-AgNW composites. Multiple failure modes have been observed in both experimental setups and analyzed with numerical and theoretical models. Results show that in the tensile loading the distribution of AgNW, as characterized by the effective free length, is the key parameter determining the failure mode. As for the impact failure scenarios, compared with failure modes observed in pure graphene cases, the mechanical reinforcing effect of AgNW will transform the failure mode from a scattered tensile fracture along radial directions to a shear failure that is constrained in a relatively local domain. Theoretical analysis using shear lag modeling, Timoshenko plate theory, molecular dynamics modeling, and finite element modeling approaches are adopted to further establish the failure modes.

Finite Element Modeling and Calibration of a Three-Span Continuous Suspension Bridge Based on Loop Adjustment and Temperature Correction.

Precise finite element modeling is critically important for the construction and maintenance of long-span suspension bridges. During the process of modeling, shape-finding and model calibration directly impact the accuracy and reliability. Scholars have provided numerous alternative proposals for the shape-finding of main cables in suspension bridges from both theoretical and finite element analysis perspectives. However, it is difficult to apply these solutions to suspension bridges with special components. Seeking a viable solution for such suspension bridges holds practical significance. The Nanjing Qixiashan Yangtze River Bridge is the first three-span suspension bridge in China. To maintain the configuration of the main cable, the suspension bridge is equipped with specialized suspenders near the anchors, referred to as displacement-limiting suspenders. It is the first suspension bridge in China to use displacement-limiting suspenders and their anchorage system. Taking the suspension bridge as a research background, this paper introduces a refined finite element modeling approach considering the effect of geometric nonlinearity. Firstly, based on the loop adjustment and temperature correction, the shape-finding and force assessment of the main cables are carried out. On this basis, a nonlinear finite element model of the bridge was established and calibrated, taking into account factors such as pylon settlement and cable saddle precession. Finally, the static and dynamic characteristics of the suspension bridge were thoroughly investigated. This study aims to provide a reference for the design, construction and operation of the three-span continuous suspension bridge.