Nodal Displacements Research Articles

Precision Orthodontic Force Simulation Using Nodal Displacement-Based Archwire Loading Approach.

Precision in force simulationis critical for forecasting tooth movement and optimizing orthodontic treatment strategies. While traditional techniques have provided valuable insights, there remains a need for improved methodologies that can seamlessly integrate with fixed orthodontic practices. This study aims to refine orthodontic force simulation techniques by integrating a nodal displacement approach within finite element analysis, specifically designed to enhance prediction accuracy in tooth movement and optimize orthodontic treatment planning. Three-dimensional patient-specific models of the Tooth, Periodontal Ligament, and Bone Complex (TPBC) of five volunteers were created, along with models of brackets and wires. The simulation involved an initial step of estimating node displacements to align the archwire with the brackets, followed by a subsequent step to attain the required tooth movement and determine the orthodontic force. Experimental validation of the simulation results was performed using an orthodontic force tester (OFT). Utilizing the nodal displacement approach, the simulation successfully positioned the archwire onto the brackets. When benchmarked against the OFT, 80% of the simulated force directions exhibited angular discrepancies of less than 5°. Additionally, the absolute differences in force magnitude reached 20.06 cN, and in moments, up to 71.76 cN mm. The relative differences were as high as 9.55% for force and 13.83% for moments. These findings represent an improvement of up to 10.45% in force accuracy and 8.87% in moment accuracy compared to median values reported in most recent literature. In this research, a nodal displacement methodology was employed to simulate orthodontic forces with precision across the dental arch. The results demonstrate the approache's potential to enhance the accuracy of force prediction in orthodontic treatment planning, thereby advancing our understanding of orthodontic biomechanics.

Improvements for the solution of crack evolution using extended finite element method

It is demonstrated that the eXtended Finite Element Method (XFEM) is of remarkable efficiency in simulating crack evolution by eliminating the need for remeshing and refinement. In this paper, it is shown how to enhance the solution efficiency through a comprehensive mathematical investigation of the solution process using XFEM. A typical example is presented to illustrate the disparities in nodal displacements along the two symmetric faces of the crack resulting from the approximation of XFEM. By analysing the structure and components of the global stiffness matrix, the underlying causes of these discrepancies are identified. Building upon these findings, two improvements of the solution are proposed to gain an acceptable accuracy in computing the nodal displacements. The first improvement consists of the subdivision of the enriched elements depending on the characteristic of the distribution of Gauss points. The second improvement is set by determining the optimal number of Gauss points in each sub-element near the crack tip. To calculate the stress intensity factor of the crack under surface pressure, such improvements are applied in conjunction with the interaction integral method, which significantly reduces computational time and eliminates the influence of surface tractions. The numerical solution is validated by comparing it with the analytical solution and the standard XFEM solution. The proposed improvements can enhance both the accuracy of the solution and the computational efficiency of XFEM.

A Combined Strategy for Path Planning of Tensegrity Manipulators Considering Structural Stability

Abstract A strategy combining an improved rapidly exploring random tree (RRT) method with the artificial potential field (APF) method is proposed for the path planning of flexible tensegrity manipulators considering typical complex constraints, such as structural stability, obstacle avoidance, and cable no-slackening. The relationship between the node displacements and the elongations of active members is established for the kinematic analysis of tensegrity manipulators. The rest lengths of active members are taken as the design variables for the generation of a random tree. The guide node is utilized for goal-biased samplings to expedite the exploration toward the final configuration, while the APF method is introduced to ensure that the obtained elongations of active members can satisfy the constraint of obstacle avoidance. The proportion of random and goal-biased samplings is adaptively adjusted based on the sampling success rate. Futile random samplings can be significantly reduced by dynamically modifying the random sampling range according to the obtained configuration nearest to the final configuration. The computational procedure of the proposed method is presented. An illustrative flexible tensegrity manipulator is employed to demonstrate the adaptability of the proposed path planning method to complex constraints, especially structural stability.

Six-degrees-of-freedom pelvic bone monitoring on 2D kV intrafraction images to enable multi-target tracking for locally advanced prostate cancer.

Patients with locally advanced prostate cancer require the prostate and pelvic lymph nodes to be irradiated simultaneously during radiation therapy treatment. However, relative motion between treatment targets decreases dosimetric conformity. Current treatment methods mitigate this error by having large treatment margins and often prioritize the prostate at patient setup at the cost of lymph node coverage. Treatment accuracy can be improved through real-time multi-target adaptation which requires simultaneous motion monitoring of both the prostate and lymph node targets. This study developed and evaluated an intrafraction pelvic bone motion monitoring method as a surrogate for pelvic lymph node displacement to be combined with prostate motion monitoring to enable multi-target six-degrees-of-freedom (6DoF) tracking using 2D kV projections acquired during treatment. A method to monitor pelvic bone translation and rotation was developed and retrospectively applied to images from 20 patients treated in the TROG 15.01 Stereotactic Prostate Ablative Radiotherapy with Kilovoltage Intrafraction Monitoring (KIM) trial. The pelvic motion monitoring method performed template matching to calculate the 6DoF position of the pelvis from 2D kV images. The method first generated a library of digitally reconstructed radiographs (DRRs) for a range of imaging angles and pelvic rotations. The normalized 2D cross-correlations were then calculated for each incoming kV image and a subset of DRRs and the DRR with the maximum correlation coefficient was used to estimate the pelvis translation and rotation. Translation of the pelvis in the unresolved direction was calculated using a 3D Gaussian probability estimation method. Prostate motion was measured using the KIM marker tracking method. The pelvic motion monitoring method was compared to the ground truth obtained from a 6DoF rigid registration of the CBCT and CT. The geometric errors of the pelvic motion monitoring method demonstrated sub-mm and sub-degree accuracy and precision in the translational directions ( , , ) and rotational directions ( , , ). The 3D relative displacement between the prostate and pelvic bones exceeded 2, 3, 5, and 7mm for approximately 66%, 44%, 12%, and 7% of the images. Accurate intrafraction pelvic bone motion monitoring in 6DoF was demonstrated on 2D kV images, providing a necessary tool for real-time multi-target motion-adapted treatment.

A novel optimization‐based modal pushover analysis procedure for estimating the response of structures

AbstractIn recent years, studies have focused on the seismic behavior of various structural systems, including single‐ and double‐layered spatial structures. While nonlinear dynamic analyses are common for these studies, they are time‐consuming and complex. To address this, many pushover procedures have been developed, but their effectiveness in spatial structures remains underexplored. This study introduces an optimized lateral loading profile for evaluating the seismic response of walled bilayer spatial structures. By combining dominant inertial forces and optimizing their corresponding coefficients with the particle swarm optimization (PSO) algorithm, an optimal loading profile is derived. The method's accuracy was tested on models with varying arch height‐to‐span ratios and a constant wall height‐to‐span ratio, as well as a 15‐story frame structure. The results obtained showed that the proposed method closely matches incremental dynamic analyses (IDAs) in predicting base shear, initial stiffness, and displacements, especially for higher arch height‐to‐span ratios. Additionally, the method accurately estimates drift profiles and nodal displacements on roofs and walls, and performs better than similar mode‐based methods for frame structures.

Prediction of bone ingrowth into a porous novel hip-stem: A finite element analysis integrated with mechanoregulatory algorithm.

Bone ingrowth into a porous implant is necessary for its long-term fixation. Although attempts have been made to quantify the peri-implant bone growth using finite element (FE) analysis integrated with mechanoregulatory algorithms, bone ingrowth into a porous cellular hip stem has scarcely been investigated. Using a three-dimensional (3D) FE model and mechanobiology-based numerical framework, the objective of this study was to predict the spatial distribution of evolutionary bone ingrowth into an uncemented novel porous hip stem proposed earlier by the authors. A CT-based FE macromodel of the implant-bone structure was developed. The bone material properties were assigned based on CT grey value. Peak musculoskeletal loading conditions, corresponding to level walking and stair climbing, were applied. The geometry of the implant-bone macromodel was divided into multiple submodels. A suitable mapping framework was used to transfer maximum nodal displacements from the FE macromodel to the cut boundaries of the FE submodels. CT grey value-based bone materials properties were assigned to the submodels. Thereafter, the submodels were solved and simulations of bone ingrowth were carried out using mechanoregulatory principle. A gradual increase in the average Young's modulus, from 1200 to 1500 MPa, of the bone tissue layer was observed considering all the submodels. The distal submodel exhibited 82% of bone ingrowth, whereas the proximal submodel experienced 65% bone ingrowth. Equilibrium in the bone ingrowth process was achieved in 7 weeks postoperatively, with a notable amount of bone ingrowth that should lead to biological fixation of the novel hip stem.

Evaluation of the Spatial Variability of the Mechanical Properties of Rocks Using Non‐Iterative Green's Function Approach and the FOSM Method

ABSTRACTThe present work proposes a new version of the Green‐FOSM (first‐order second moment) method, which eliminates the iterative calculation process of the original version and, simultaneously, solves the convergence problems related to the mechanical properties of rocks that form the geological formation. In this calculation scheme, the iterative process is eliminated by using a matrix that correlates the nodal displacement vector with the strain vector. Considering the same computational resources, this non‐iterative version of the Green‐FOSM method is up to 200 times faster than the original iterative process. In addition, it allows analyzing problems with more than 10,000 random variables, value that in the original method is less than 3000. To demonstrate its validity, the proposed method is applied to two hypothetical models subjected to different fluid extraction processes. For all the different levels of correlation and spatial variability, the statistical results obtained by the proposed method agree well with the results obtained via Monte Carlo Simulation (MCS). The relationship between CPU times demonstrates that the proposed method is at least 50 times faster than MCS. In the end, the non‐iterative Green‐FOSM method is used to obtain the displacement, strain, and stress fields of a geological section constructed from a seismic image of Brazilian pre‐salt oil region. The results found show that, depending on the levels of spatial variability, the analyzed fields can assume values up to 30.6% higher or lower than the values obtained deterministically.

A novel iterative algorithm for nonlinear inelastic dynamic analysis of truss structures; MCB-Spline+Sp time integration method

The focus of this work is on the development of a novel algorithm for the nonlinear dynamic analysis of structures. In the sake of a general and easy-to-implement solution method, the tangential stiffness matrix is calculated by combining the Cauchy-Riemann equations derived from the differentiation of complex functions and using the nodal displacement perturbation vector. An implicit direct time integration method based on a cubic B-Spline is integrated with the incremental-iterative method using a quadrature rule based on the interpolation of spline functions, which is referred to as the MCB-Spline+Sp time integration method. A step-by-step algorithm for developing a computer code to implement this method is provided. The effectiveness of the proposed approach is evaluated through several nonlinear time history analyses, which demonstrate its accuracy and efficiency by observing less computational efforts and fast convergence rate. Moreover, a comparison is made between the developed method and well-established techniques such as the Newmark and Standard-Bathe time integration methods in combination with the iterative Newton-Raphson method.

Grid Optimization of Free-Form Spatial Structures Considering the Mechanical Properties

In recent years, the application of free-form surface spatial grid structures in large public buildings has become increasingly common. The layouts of grids are important factors that affect both the mechanical performance and aesthetic appeal of such structures. To achieve a triangular grid with good mechanical performance and uniformity on free-form surfaces, this study proposes a new method called the “strain energy gradient optimization method”. The grid topology is optimized to maximize the overall stiffness, by analyzing the sensitivity of nodal coordinates to the overall strain energy. The results indicate that the overall strain energy of the optimized grid has decreased, indicating an improvement in the structural stiffness. Specifically, compared to the initial grid, the optimized grid has a 30% decrease in strain energy and a 43.3% decrease in maximum nodal displacement. To optimize the smoothness of the grid, the study further applies the Laplacian grid smoothing method. Compared to the mechanically adjusted grid, the structural mechanical performance does not significantly change after smoothing, while the geometric indicators are noticeably improved, with smoother lines and regular shapes. On the other hand, compared to the initial grid, the smoothed grid has a 21.4% decrease in strain energy and a 28.3% decrease in maximum nodal displacement.

A fast search method of buckling load for telescopic boom structure

The telescopic boom structures are extensively utilized in engineering applications involving large mobile cranes, aerial work platforms of significant height, and similar mechanical devices. These are predominantly fabricated using solid-webbed box types, latticed truss designs, or a combination thereof. Under load, they exhibit pronounced geometric nonlinear effects, with the relationship between load and displacement demonstrating marked nonlinear characteristics. This paper focuses on the geometric nonlinear modeling of slender multi flexible beam structures. The overall structural system is divided into several substructures, and the concept of multi flexible system modeling is borrowed to establish a follow-up connected body foundation on each substructure. By decomposing the node displacement and rotation in the substructure, the large displacement and large rotation of the node are decomposed into rigid body motion of the connected base and small displacement and small rotation relative to the connected base, effectively representing the large displacement and large rotation of the substructure as rigid body motion of the connected base. A modeling method for the geometric nonlinear analysis of slender and flexible multibody beam structures is proposed. By decomposing the nodal displacement and rotation within the substructures, the large displacement and rotation of the nodes are broken down into the rigid body motion of the body-fixed coordinate and small displacement and rotation relative to the body-fixed coordinate. This approach establishes the application conditions for calculating the structure’s virtual power of deformation using traditional beam elements with linear strain. A method for solving the instability load of slender and flexible multibody structures is proposed. This method integrates the characteristics of the system’s nonlinear equilibrium equations with respect to load, by deriving the system equations with respect to a load increment control parameter. This paper converts nonlinear equilibrium equations into first-order ordinary differential equation (ODE). The method proposed in this paper provides a more efficient solution for the buckling load of the telescopic boom. It can be used to systematically and quickly calculate the structure of the telescopic boom.

Boosting tree with bootstrap technique for pre-stress design in cable dome structures

Tensegrity structures, known for their rigidity derived from feasible pre-stresses, present unique challenges in structural engineering. Traditional force-finding methods, though comprehensive, rely heavily on intricate matrix computations, making them computationally intensive and often uncomfortable for considering external loads in practical engineering scenarios. This paper introduces a novel approach to compute pre-stresses in cable dome structures by integrating machine learning and probability theory, collectively termed the boosting tree with bootstrap technique (BTWBT). This method reduces the sample size to as few as 100 per iteration, while improving computational efficiency by randomly generating internal forces. By reframing the force determination as an inverse problem, it ensures that structural displacement converges to zero under feasible pre-stresses. The effectiveness of BTWBT is demonstrated across three distinct cable dome structures: the Geiger dome, Kiewitt dome, and rotating hyperboloid cable dome. Results show that BTWBT achieves the preset displacement requirement (maximum nodal displacement below 0.01 mm) with fewer iterations and reduced computational cost compared to traditional machine learning methods. BTWBT's capability to manage complex structural configurations with minimal data, while incorporating random internal force generation ranges, highlights its potential as a superior alternative for force determination in tensegrity structures.

Eulerian approach for erosion induced by particle-laden impinging jets

Particle dispersion and erosion play an important role in various engineering applications, particularly in impinging jet flows. This study presents a new Eulerian method for characterizing these phenomena in axisymmetric, laminar, and turbulent jets, impinging and transiently eroding a flat wall. The Eulerian mass and momentum conservation equations are solved assuming a one-way coupling between the flow and the particles. The carrier flow boundary layer velocity profiles in the laminar case are validated with analytical solutions. The particle calculation involves two stages, incident and reflected particles, connected via a restitution model. The reflected particles’ results offer insights into secondary erosion areas but are not used for the main erosion calculations. Subsequently, erosion at the eroded surface is computed using an empirical erosion model, followed by the displacement of computational nodes using the linear-elastic, small-strain deformation equations, all of which are solved transiently. The solutions reveal the spatial particle concentrations and momentum for different Stokes numbers: smaller Stokes particles follow the carrier flow streamlines, medium Stokes particles deviate, forming a W-shaped erosion profile, while the largest Stokes particles create a U-shaped profile. This numerical model offers a computationally efficient framework for CFD-based transient erosion calculation due to its Eulerian implementation.

FRACTURE MECHANISM ANALYSIS OF HIGH-DENSITY FIBREBOARD BASED ON DIGITAL IMAGE CORRELATION TECHNOLOGY

This paper analyses the scattering images of the bending deformation of high-density fibreboards based on the digital image correlation (DIC) technique, so as to study its mechanical deformation law. Three-point bending tests were carried out on fibreboards using a mechanical testing machine with a non-contact measuring system. The measured values of the displacements of the grid nodes in the region of interest (ROI) were combined with the Moving least squares (MLS) method to construct the strains of the high-density fibreboards at different loading forces, thus deriving the strain values of the fibreboards during the bending deformation process. To further analyze its force deformation mechanism, this paper used a portable electron microscope and scanning electron microscope to analyze the damage situation at the fracture damage, and at the same time, it verified that the constructed strain field model was accurate.

Prediction Method for Mechanical Characteristic Parameters of Weak Components of 110 kV Transmission Tower under Ice-Covered Condition Based on Finite Element Simulation and Machine Learning

Icing on transmission lines may cause damage to tower components and even lead to structural failure. Aiming at the lack of research on predicting mechanical characteristic parameters of weak components of transmission towers, and the cumbersome steps of building a finite element model (FEM), the study of prediction for mechanical characteristic parameters of weak components of towers based on a finite element simulation and machine learning is proposed. Firstly, a 110 kV transmission tower in a heavily iced area is taken as an example to establish its FEM. The locations of the weak components are analyzed, and the accuracy of FEM is verified. Secondly, meteorological and terrain parameters are considered as input parameters of the prediction model. The axial stresses and nodal displacements of four weak components are selected as output parameters. The FEM of the 110 kV transmission tower is used to obtain input and output datasets. Thirdly, five machine learning algorithms are considered to establish the prediction models for mechanical characteristic parameters of weak components, and the optimal prediction model is obtained. Finally, the accuracy of the prediction method is verified through an actual tower collapse case. The results show that ACO-BPNN is the optimal model that can accurately and quickly predict the mechanical characteristic parameters of the weak components of the transmission tower. This study can provide an early warning for the failure prediction of transmission towers in heavily iced areas, thus providing an important reference for their safe operation and maintenance.

An optimized single-side inverse finite element method for displacement field reconstruction of wind turbine tower

Wind turbines are subjected to complex dynamic loads during actual operation, which are difficult to predict accurately in advance, which may lead to inaccurate structural strength assessment in the structural design stage, and thus bring safety risks to wind power towers. Inverse Finite Element Method (iFEM) is a reconstruction method of structural displacement distribution, which can estimate the full-field displacement distribution of tower, and used for structural on-line health monitoring. However, conventional iFEM require sensors to be placed on both sides of the structure, which greatly limits its engineering practicality. For this reason, a single-side inverse finite element (iFEM_S) was developed in this study to enhance the engineering applicability of iFEM. Both iFEM and iFEM_S are based on the principle of least squares variation, and the solution equations can be established when the measured strain is known. The core idea of iFEM_S is to find the optimal node displacement s to minimize the sum of mean square errors between the theoretical and measured strains on one side of the structure, thereby generating a set of algebraic equations. Solving these linear equations can achieve the structural displacement field. Additionally, A cyclic optimization algorithm (COA) was developed in this study to improve the robustness of reconstruction results through optimizing strain gauge locations, in which the condition number of pseudo stiffness matrix was used as the objective function, and the condition number is minimized, named iFEM_S_O. Finally, the reconstruction accuracy and robustness and of iFEM, iFEM_S and iFEM_S_O were verified through several numerical examples. The simulated reconstruction results indicated that iFEM_S_O is more accurate and stable than iFEM_S, and iFEM_S is more accurate and stable than iFEM.

Dynamic analysis and shape sensing of pipes subjected to water hammer by the inverse finite element method

Pipe structures are commonly employed in modern industries. However, the transient pressure induced by water hammer leads to a high risk of structural damage, highlighting the importance of Structural Health Monitoring (SHM). The inverse Finite Element Method (iFEM) offers a way to reverse structural deformations based on a set of discrete strain data. In this paper, the water hammer phenomenon in a Reservoir-Pipe-Valve (RPV) system is investigated. The element iQS4 is adopted to reconstruct dynamic deformations for the pipe using iFEM. The water hammer resulting from an instantaneous closure of the valve can generate significant fluid pressure, leading to severe pipe vibrations. The vibration deformations can be monitored by iFEM. The maximum error of the pipe nodal displacement is below 3 % and the average error is below 2 % in the proposed several sensor location cases, even the mesh of iFEM is coarser than the FE model. Among the sparse sensor location cases, the X path which is suitable for Fiber Bragg Grating (FBG) sensors has the highest accuracy of the displacement reconstruction. The results reveal a promising prospect in the real-time monitoring based on iFEM for pipes.

Mixed finite element implementation of plane crack problems within strain gradient elasticity

AbstractAn approach is proposed and applied to solve boundary value problems within the strain gradient elasticity theory. A mixed variation formulation of the finite element method is used, where stresses, strains, and displacements are considered as independent variables. This significantly simplifies the pre‐requirement for approximating functions and allows one to use the simplest triangular finite elements with a linear approximation of displacements. Global unknowns in a discrete problem are nodal displacements, while the strains and stresses in nodes are treated as local unknowns. This discrete problem is solved by a modified iteration procedure, where the global stiffness matrix for classical elasticity problems is treated as a preconditioning matrix with fictitious elastic moduli. The modeling plane crack problem (tension of a square plate with a central crack) is solved. The obtained solution agrees with the results available in the literature. Good convergence is achieved by refining the mesh for all scale parameters.

A multistrategy differential evolution algorithm combined with Latin hypercube sampling applied to a brain–computer interface to improve the effect of node displacement

Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain–computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain–computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain–computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain–computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain–computer interface after node displacement optimization can be evaluated.

Enhancing Brain-Computer Interfaces through Kriging-Based Fusion of Sparse Regression Partial Differential Equations to Counter Injection Molding View of Node Displacement Effects.

Injection molding is an efficient and precise manufacturing technology that is widely used in the production of plastic products. In recent years, injection molding technology has made significant progress, especially with the combination of in-mold electronics (IME) technology, which makes it possible to embed electronic components directly into the surface of a product. IME technology improves the integration and performance of a product by embedding conductive materials and functional components in the mold. Brain-computer interfaces (BCIs) are a rapidly growing field of research that aims to capture, analyze, and feedback brain signals by directly connecting the brain to external devices. The Utah array, a high-density microelectrode array, has been widely used for the recording and transmission of brain signals. However, the traditional fabrication method of the Utah array suffers from high cost and low integration, which limits its promotion in practical applications. The lines that receive EEG signals are one of the key parts of a brain-computer interface system. The optimization of injection molding parameters is particularly important in order to effectively embed these lines into thin films and to ensure the precise displacement of the line nodes and the stability of signal transmission during the injection molding process. In this study, a method based on the Kriging prediction model and sparse regression partial differential equations (PDEs) is proposed to optimize the key parameters in the injection molding process. This method can effectively predict and control the displacement of nodes in the film, ensure the stability and reliability of the line during the injection process, and improve the accuracy of EEG signal transmission and system performance. The optimal injection parameters were finally obtained: a holding pressure of 525 MPa, a holding time of 50 s, and a melting temperature of 285 °C. Under this condition, the average node displacement of UA was reduced from the initial 0.19 mm to 0.89 µm, with an optimization rate of 95.32%.

Damage inference of statically deterministic structural trusses is carried out by Bayesian updating

In the face of harsh environments, aging construction materials, and increased traffic, the performance of Bridges in service continues to deteriorate. To avoid the serious consequences caused by structural damage, it is of great significance to infer the damage of the truss structure and evaluate the reliability of the bridge. In this paper, a random sampling process is used to characterize the resistance degradation, and the historical load test information and Bayesian method are used to update the resistance of serving Bridges. The modeling of structural mechanics solver determines the maximum axial force on the damaged structural member by loading different nodes. Secondly, node displacement is calculated. Then, the displacement results are taken as new information as the parameters of prior distribution for Bayesian update, and finally, the maximum probability of structural damage is obtained. The results show that the probability of EA values between 1.34 and 1.38 is the greatest. The uncertainty and variability in the process of resistance degradation of reinforced concrete bridge members can be described more reasonably by the Bayesian updating process. By integrating the real resistance degradation information detected during bridge service into practical application, and using the research method in this paper to analyze and calculate, Bridges' future service status and remaining service life can be predicted more accurately.