Finite Element Method Results Research Articles

Comparing outdoor sound propagation simulations based on geometrical and numerical methods

Conventional simulations of outdoor noise propagation often rely on geometrical acoustic methods such as ray tracing or mirror sources. Especially for low frequencies, these methods deliver physically implausible results due to their inherent approach not resolving the properties of the acoustic field. In this contribution, the results of a conventional noise propagation simulation software based on geometrical acoustics are compared to numerical results originating from solving the Helmholtz equation with the Finite Element Method (FEM). A 2.6 km long cross-section through an alpine valley with a railway line is considered as an exemple. In comparison with the FEM results, the shortcomings of geometrical acoustic methods are demonstrated, which underline the need for powerful and computationally efficient numerical approaches for outdoor noise propagation.

Multiscale Analysis of Sandwich Beams with Polyurethane Foam Core: A Comparative Study of Finite Element Methods and Radial Point Interpolation Method.

This study presents a comprehensive multiscale analysis of sandwich beams with a polyurethane foam (PUF) core, delivering a numerical comparison between finite element methods (FEMs) and a meshless method: the radial point interpolation method (RPIM). This work aims to combine RPIM with homogenisation techniques for multiscale analysis, being divided in two phases. In the first phase, bulk PUF material was modified by incorporating circular holes to create PUFs with varying volume fractions. Then, using a homogenisation technique coupled with FEM and four versions of RPIM, the homogenised mechanical properties of distinct PUF with different volume fractions were determined. It was observed that RPIM formulations, with higher-order integration schemes, are capable of approximating the solution and field smoothness of high-order FEM formulations. However, seeking a comparable field smoothness represents prohibitive computational costs for RPIM formulations. In a second phase, the obtained homogenised mechanical properties were applied to large-scale sandwich beam problems with homogeneous and approximately functionally graded cores, showing RPIM's capability to closely approximate FEM results. The analysis of stress distributions along the thickness of the beam highlighted RPIM's tendency to yield lower stress values near domain edges, albeit with convergence towards agreement among different formulations. It was found that RPIM formulations with lower nodal connectivity are very efficient, balancing computational cost and accuracy. Overall, this study shows RPIM's viability as an alternative to FEM for addressing practical elasticity applications.

Novel strengthening method of signal tower using prestressed CFRP plates: Concept and behavior

A novel prestressed carbon fiber reinforced polymer (CFRP) plate strengthening method for signal towers was proposed. The proposed strengthening system incorporates a circumferential anchorage system, a support system, a column system, and CFRP plates. These elements synergistically interact with the tower, forming a self-balancing system. This integrated system can provide exceptional flexural capacity and reduce the lateral displacement of the tower under wind load. To validate this novel method, a series of four static load tests were conducted. The results showed that the initial stiffness of the strengthened tower with CFRP plate prestresses of 0, 200, and 400 MPa increased by 71.0 %, 113.8 %, and 113.8 % compared with the unstrengthened tower, respectively. Moreover, the CFRP plate slacking occurred later for the strengthened tower with higher CFRP plate prestress, and the stiffness of the tower after the CFRP plate slacking kept increasing as CFRP plate prestress increases. The results indicate that the prestressed CFRP plate strengthening method effectively improved the behavior of the tower under wind load. Moreover, the finite element method (FEM) was utilized to analyze the influence of the ratio of the circumferential anchorage system height to tower height, the ratio of the support system height to tower height, the ratio of the column height to tower height, the ratio of steel support length to tower diameter, the elastic modulus of the CFRP plate and CFRP plate prestress on the strengthening effect. Finally, a strengthening procedure was proposed according to FEM results and sensitivity analysis. The analysis findings can serve as a guide for designing the strengthening system.

KAJI NUMERIK GROUND PAD SHOE KENDARAAN TEMPUR DENGAN MODEL MATERIAL HYPERELASTIC

One of the important components on a combat vehicle is the ground pad shoe (GPS) or track pad. GPS that has been developed at this time mostly uses rubber material. Rubber raw materials themselves are found in Indonesia in large amount. Indonesia left 3 million tonnes of natural rubber unprocessed in 2018. On the other hand, natural rubber research is quite expensive and takes a long time to become a product. To make rubber products is needed special molds and tests. To solve the high costs and long time involved in rubber research, it is necessary to carry out numerical research. In this study, a numerical study of GPS rubber for combat vehicles was carried out with a hyperelastic material model. The research was conducted experimentally and numerically. Tensile testing is carried out on imported GPS samples which are then used as comparison data for numerical studies. The Mooney-Rivlin, Neo-Hookean, Yeoh, and Ogden hyperelastic models were varied to get the stress and strain values ​​closest to the experimental test. The results of the numerical study show that the best hyperelastic model for the imported GPS rubber model is the Neo-Hookean model. The tensile strength of GPS according to the experimental tensile test results is 16.93 MPa, while the result of the finite element method (FEM) is 16.75 MPa. The GPS modulus 200% according to the experimental tensile test results is 14.7 mm/mm, while the FEM result is 15.3 mm/mm. The difference between the FEM and experimental test values ​​for tensile strength and modulus 200% are below 5%. Maximum stress on GPS is 4,4 MPa and safety factor 3,8.

Measurement and evaluation of the flashover voltage on polluted cap and pin insulator: An experimental and theoretical study

This study investigates the flashover voltage on high voltage insulators in polluted environments, employing a combination of experimental and theoretical approaches. Utilizing Analysis of Variance (ANOVA) and the Finite Element Method (FEM), the research aims to offer a thorough comprehension of the phenomenon. The experimental aspect of the study involves subjecting insulators to polluted environments, replicating real-world scenarios, and measuring the resulting flashover voltages. This experimental study focuses on investigating flashover pollution, where artificial pollution is introduced into an experimental model. An observational approach is employed to assess the impact of conductivity and pollution distribution on the 1512 L cap and pin insulator, as well as its proposed experimental model. Complementing the experimental approach, the theoretical aspect of the study employs the FEM method to simulate and model the insulator's behaviour under pollution. This computational technique enables a detailed analysis of the electrical field distribution, surface potential, and stress repartition across the surface of the insulator. Simulations further explore the impact of pollution on flashover voltage and leakage current. The FEM results are then compared with experimental findings to validate the model's accuracy and reliability. By analyzing the collected data, the ANOVA technique is applied to identify significant differences in flashover voltage under diverse pollution levels. This statistical approach allows for the determination of the most influential factors affecting insulator performance. This research offers valuable insights into the influence of flashover voltage on high-voltage insulators in polluted environments, contributing to the development of more robust and efficient insulation systems. The combination of ANOVA and FEM methods provides a robust framework for understanding and predicting insulator performance, ultimately benefiting the power industry and promoting energy sustainability.

Analytical solution for forced vibration of multilayer structures composed of plates with different geometric dimensions

Multilayer system composed of parallel plate components with different geometric dimensions is frequently used to describe engineering objects, such as electronic assembly. In this work, an analytical method was proposed for forced vibration of this type of multilayer system. The proposed method overcomes the limitation that the traditional wave method is only applicable to all plate components must have the same in-plane dimensions. The proposed analytical method has an efficiency advantages in parameter analysis than element-based methods such as finite element method (FEM). The connection joints between two adjacent plate components, such as ball grid array (BGA) solder interconnect, are represented by elastic springs. The vibration of each component are described in terms of general and physical analytical waves, respectively, and the dynamic coupling between them are established by an equivalent dynamic flexibility matrix. The forced responses of the multilayer system are analytically calculated by solving the system equation in wave space. In the numerical examples, the effectiveness of the proposed method is validated by comparing the present results with the FEM results. The influence of number of the defective solder joints on vibration response is also investigated.

Iron Loss and Temperature Rise Analysis of a Transformer Core Considering Vector Magnetic Hysteresis Characteristics under Direct Current Bias.

Direct current (DC) bias induced by the DC transmission and geomagnetically induced current is a critical factor in the abnormal operation of electrical equipment and is widely used in the field of power transmission and distribution system state evaluation. As the main affected component, the vector magnetization state of a transformer core under DC bias has rarely been studied, resulting in inaccurate transformer operation state estimations. In this paper, a dynamic vector hysteresis model that considers the impact of rotating and DC-biased fields is introduced into the numerical analysis to simulate the distribution of magnetic properties, iron loss and temperature of the transformer core model and a physical 110 kV single-phase autotransformer core. The maximum values of B, H and iron loss exist at the corners and T-joint of the core under rotating and DC-biased fields. The corresponding maximum value of the temperature increase is found in the main core limb area. The temperature rise of the 110 kV transformer core under various DC-biased conditions is measured and compared with the FEM (Finite Element Method) results of the proposed model and the model solely based on the magnetization curve B||H. The calculation error of the temperature rise obtained by the improved model is approximately 3.76-15.73% and is much less than the model solely based on magnetization curve B||H (approximately 50.71-66.92%).

Approximate modeling of cross-beam multi-axis force/moment sensor through Gaussian process and partial dependence plots for design optimization including stress topology

This study introduces a novel approximate model (AM) aimed at optimizing the design of cross-beam multi-axis force/moment sensor. It considers the characteristics of flexure hinges in elastic beams and the flexibility of beam joints, resulting in improved accuracy and broader solutions for equivalent stresses and natural frequencies compared to existing approximate models in the literature. Model validation is carried out by comparing the results with the finite element model in different cases that each case contains 1000 simulations. Validation cases combine different sensor dimensions with different forces, moments, material properties, and center hub shapes. Each validation case typically requires approximately 19 h using the finite element method (FEM) and just 1 sec with the AM. Furthermore, by using the Gaussian process and partial dependence plots, the sensitivity of the model accuracy on normalized sensor dimensions is investigated. The evaluation of model linearity and dimension normalization allows for the utilization of the novel approximate model beyond the provided dimension range, while still adhering to the normalized parameters. The implementation of AM in the design optimization problem demonstrates its suitability and advantages compared to existing literature and FEM results. When compared to finite element solutions within the sensor workspace, the approximate model exhibits a stiffness error of 5%, a strain error of 2%, a fundamental frequency error of 3%, and an equivalent stress error of 8%, while maintaining correlations of axial sensor properties above 98%.

Multiscale analysis of carbon nanotube-reinforced curved beams: A finite element approach coupled with multilayer perceptron neural network

This paper presents a comprehensive investigation into the structural response of curved composite beams enhanced with carbon nanotube (CNT). Employing a multiscale framework, our analysis leverages the finite element method (FEM) to account for both bending and shear deformations across six degrees of freedom. The inquiry encompasses diverse mechanical, geometrical, and boundary configurations to assess these composite beams' natural vibration features. Moreover, we introduce a multilayer perceptron (MLP) neural network architecture designed to forecast such beams' dimensionless first natural frequency. Trained on a meticulously curated dataset derived from FEM simulations, the neural network model exhibits promising predictive capabilities concerning the free vibration frequency. To ascertain the efficacy and precision of our proposed methodology, we conduct a comparative analysis between FEM results and employ statistical metrics to evaluate the neural network's predictive performance. The findings of this study reveal an impressive predictive accuracy of over 95 % with regards to the initial natural frequency of the composite beams, thereby emphasizing the potential effectiveness of neural network methodologies in engineering analyses. This study significantly contributes to advancing our comprehension of the vibrational dynamics inherent in carbon nanotube-reinforced composite beams, while concurrently underscoring the potential efficacy of neural networks in forecasting their dynamic attributes.

FEM simulations applied to the failure analysis of RC structure under the influence of municipal sewage pressure

The paper discusses a failure mechanism of reinforced concrete (RC) structure with steel cover that failed under the influence of municipal sewage pressure. To explain the reasons of failure, in-situ measurements, laboratory experiments and comprehensive Finite Element Method (FEM) computations were performed. Non-destructive in-situ scanning tests were carried out to determine quantity and cover thickness of embedded reinforcement bars, simultaneously, laboratory tests regarding concrete and shotcrete thickness, density and compressive strength were performed on samples prepared from core drills taken from the RC structure. FEM computations were carried out with the constitutive continuum model for concrete and steel with material parameters designated on the basis of stress–strain curves in uniaxial compression and uniaxial tension, respectively. An isotropic coupled elasto-plastic-damage formulation based on the strain equivalence hypothesis was used. In order to describe strain localization in concrete, model was enhanced in a softening regime by a characteristic length of micro-structure by means of a non-local theory. FEM analyses were carried out for different values of sewage pressure. The main attention was paid to the evolution of steel cover deformation and strain localization of the RC ceiling slab. FEM results revealed strong dependence between a bond-slip between anchors and steel cover deformation as well as between sewage pressure value and strain localization pattern of RC structure. Mechanism of the structure failure under complex loading conditions was realistically captured and its reasons were discussed in detail.

Analytical Description of the Electric Field inside a High Voltage Glass Insulator

Research presented in this paper analyzes the accuracy and applicability of analytical expressions to describe the electric field of a high-voltage glass insulator. The metal cap and the metal pin of the glass insulator are represented by coaxial cylindrical electrodes. The insulating materials between them are modeled as coaxial hollow cylinders. Analytical expressions for the electric field in these insulating materials are derived. Using these expressions, the spatial distribution of the electric field in the insulating materials was determined. Suitable software, based on the finite element method (FEM), was used to determine the spatial distribution of the electric field in dielectrics in the same space. Both analyses were performed under quasi-static conditions, and the results of the analytical expressions were compared with the FEM results. Discrepancies between the results of the two mentioned analyses were observed. The reasons for the observed discrepancies and the applicability of the presented analytical expressions are commented on.

Electromagnetic torque modeling and validation for a permanent magnet spherical motor based on XGBoost

As a device characterized by multiple degrees of freedom in one driving unit, analytical electromagnetic torque modeling is needed for the rotor position tracking control of a Permanent Magnet Spherical Motor (PMSpM). In this paper, Extreme Gradient Boosting (XGBoost) was proposed to be employed for establishing the output relationship between the rotor position and the electromagnetic torque of PMSpM. The Finite Element Method (FEM) was applied to obtain train data and test data concerning the rotor position and electromagnetic torque of PMSpM. Particle Swarm Optimization (PSO) was applied to optimize partial parameters of XGBoost, which serves to enhance the modeling accuracy of electromagnetic torque via XGBoost. The predictive results of algorithms, including Random Forest (RF), Gradient Boosting Regression Tree (GBRT), Multi-task Gaussian Process (MTGP), and XGBoost, were compared with FEM results and experimental results over multiple indicators. The capability of XGBoost has been validated not only to perform modeling tasks within an abbreviated time span but also to generate models that display amplified accuracy and efficiency.

A Hybrid Analysis of Brushless DC Motor

Brushless DC (BLDC) motors and derives have gained popularity because of numerous advantages. This motor application is achievable since permanent magnet (PM) technology has advanced. It has higher efficiency, reliability, and power with less maintenance due to the absence of brushes. A higher torque-to-mass ratio and long life are two of the most attractive characteristics making it appropriate for high-performance applications. This study describes the hybrid methodology for analyzing a three-phase, four-pole, 1500 W, brushless DC (BLDC) motor with an inner rotor. PI controllers and PWM in Matlab /Simulink, and the chopped current control in Maxwell 2d ae used. BLDC motor modeled by rotation machine export (RMxprt) analytical software and analyzed with a finite element method (FEM) Maxwell 2D software to calculate motor performance, such as torque, speed, and efficiency. The FEM results were verified by comparing them with the results taken from the Matlab/Simulink program and with the aid of the RMxprt software to supply some missing data. The motor torque and efficiency results from Maxwell 2D and Matlab show good agreement. A hybrid FEM-analytical approach is successfully applied to study a BLDC motor using three software packages RMxprt, Maxwell 2D, and Matlab/Simulink despite the lack of motor test data. The RMxprt program offers missing data like stator resistance, stator inductance, and torque constant. It is provided in the design sheet to help us with motor modeling. The successful adoption of the proposed hybrid FEM-analytical methodology will provide a good starting point for future BLDC motor research work

Space–time finite element method with domain reduction techniques for dynamic soil–structure interaction problems

AbstractDesign of earth structures, such as dams, tunnels, and embankments, against the vibrational loading caused by high‐speed trains, road traffic, underground explosions, and, more importantly, earthquake motion, demands solutions of the dynamic soil–structure Interaction (SSI) problem. This paper presents a velocity‐based space–time finite element procedure, v‐ST/finite element method (FEM), to solve dynamic SSI problems. The main goal of this study is to present the computation details of implementing viscous boundary conditions of Lysmer–Kuhlemeyer to truncate the unbounded soil domain. Furthermore, additional time‐dependent boundary conditions, in terms of the free‐field response, are included to facilitate energy flow from the far field to the computation domain at the vertical truncated boundaries. In the FEM, seismic input motion is applied to an effective nodal force vector, which can be obtained explicitly in the numerical simulations. Finally, the response of a concrete gravity dam resting on an elastic half‐space to the horizontal component of earthquake motion is computed and successfully compared with the results of semidiscrete FEM using the Newmark‐ method.

Thermo-magnetic radiative flow in porous enclosure with deep-learning parameter estimation

This study numerically investigates convective heat transfer, fluid flow, and entropy generation within an inverted T-shaped porous enclosure filled with a hybrid nanofluid. The interplay between thermal radiation and a magnetic field is considered, as these factors significantly influence thermal acquisition processes in renewable and thermal engineering applications, particularly solar power collectors with the selected physical domain. By analyzing various combinations of flow parameters, this work aims to identify favorable conditions for maximizing heat transfer efficiency in such systems. A Darcy–Forchheimer–Brinkman-based mathematical model has been incorporated and numerically simulated using the penalty finite element method (FEM). Moreover, the variation of fluid flow, isotherms, and entropy generation is thoroughly analyzed for the variation of non-dimensional permeability parameter (Darcy number, Da), buoyancy force (Rayleigh number, Ra), magnetic field (Hartmann number, Ha), porosity value (ϵ), and thermal radiation parameter (Rd). The comprehensive result demonstrates that increasing values of Ra, Da, ϵ, and Rd intensify entropy generation, convective heat, and fluid flow phenomena, whereas the increasing value of Ha substantially weakens these phenomena. The thermal efficiency of the model attends at the lower buoyancy forces (Ra≤105) for each flow parameter (except for Ha). However, the increasing magnetic forces (Ha) substantially improve the thermal efficiency of the developed model. Additionally, this study employs a feed-forward neural network (FNN) based deep learning (DL) approach to predict the parametric influence on convective heat transport rate (Num), total entropy generation rate (Stot), and total Bejan number due to heat transport irreversibility (Beθ,tot) based on the minimally supplied experimental data for FNN training process. The trained FNN model predicts the results very quickly with 98.6% accuracy compared to FEM results, demonstrating its efficiency in approximating the parametric influence of complex mathematical model results. Moreover, this approach significantly reduces computational costs and efforts compared to traditional numerical methods, making it a valuable tool for forecasting the influence of an unknown set of flow parameters in complex mathematical models.

Disturbance range characterization and reinforcement design method of concealed bedding slope

The concealed bedding slopes exhibit sudden and extensive failure characteristics, harboring significant safety hazards. It is crucial to employ rapid and accurate methods to determine excavation disturbance ranges and reinforcement forces to prevent such slope disasters. This paper first analyzes the failure characteristics of slope excavation to reveal the mechanism of slope failure. Secondly, a novel method was proposed to calculate excavation disturbance ranges and solve for active reinforcement forces. Thirdly, the rationality of the method was validated through numerical simulation experiments. Finally, the method was applied to engineering. The results are as follows:(1) Excavation induces plastic shear failure of structural planes first, and the strength of the structural plane will be reduced, resulting in extrusion shear deformation of the rock mass at the foot of the slope under greater unidirectional load along the structural plane, forming a shear sliding surface. (2) From both strength and deformation perspectives, a method for calculating disturbance ranges in excavated slopes is proposed. This method determines key indicators for analyzing two disturbance range factors: ultimate height and disturbance thickness. The disturbance range is divided into shear plastic zones and traction sliding zones. (3) An active reinforcement design method is proposed considering the timing, scope, and force of reinforcement. The disturbance thickness is used to determine the reinforcement range, while the ultimate height is used to determine the timing of reinforcement. Active reinforcement forces are calculated based on the equilibrium of forces and deformation amounts. (4) A comparison with the Finite Element Method (FEM) shows that during excavation, the disturbance ranges and reinforcement forces calculated using this method are slightly larger than those obtained from the FEM. The calculations are fast, and as the excavation height approaches the limit slope height, the disturbance thickness and horizontal deformations closely match the FEM results, ensuring safety. It can provide monitoring, early warning and reinforcement guidance for bedding slope construction.

Clarification of appropriate conditions for forming complex shapes with multiple steps in two-stage deep drawing process

In this study, appropriate conditions for forming complex shapes with multiple steps, such as automotive side door inner in a two-stage deep drawing process are investigated, and the effectiveness of the process is verified in a test. In this forming process, the inner area of the part is formed in the first stage and the outer area is formed in the second stage. And in the second stage, a forming pad is used to reduce strain in the inner area that is formed in the first stage. The results of the forming test showed that it is necessary to control the blank holding force (BHF) and the pad force (PF) in the second stage to avoid blank ruptures in all areas. Therefore, the concept of the BHF and PF for blank rupture risk at each site was studied using the finite element method (FEM). An appropriate range of BHF and PF was estimated from the results of FEM and the validity of the estimated values was confirmed in an actual test. In addition, it was confirmed that a two-stage deep drawing process using the above appropriate values can form a more severe shape than a one-stage deep drawing process.

Numerical modeling of magnetic cores with combined electrical steel grades

PurposeThis paper aims to investigate the impact of combining grain-oriented electrical steel (GOES) grades on specific iron losses and the flux density distribution within a single-phase magnetic core.Design/methodology/approachThis paper presents the results of finite-element method (FEM) simulations investigating the impact of mixing two different GOES grades on losses of a single-phase magnetic core. The authors used different models: a 3D model with a highly detailed geometry including both saturation and anisotropy, as well as a simplified 2D model to save computation time. The behavior of the flux distribution in the mixed magnetic core is analyzed. Finally, the results from the numerical simulations are compared with experimental results.FindingsThe specific iron losses of a mixed magnetic core exhibit a nonlinear decrease with respect to the GOES grade with the lowest losses. Analyzing the magnetic core behavior using 2D and 3D FEM shows that the rolling direction of the GOES grades plays a critical role on the nonlinearity variation of the specific losses.Originality/valueThe novelty of this research lies in achieving an optimum trade-off between the manufacturing cost and the core efficiency by combining conventional and high-performance GOES grade in a single-phase magnetic core.

Multi-objective optimization of composite sandwich structures using Artificial Neural Networks and Genetic Algorithm

Sandwich structures offer significant opportunities to improve the performance of many industrial applications such as aerospace, automotive, and marine. The design of a composite sandwich structure is often challenging because it is driven by the balance between the weight and cost of these structures. In this paper, a multi-objective optimization model using a Genetic Algorithm (GA) and an Artificial Neural Network (ANN) is presented to evaluate a new optimization approach in terms of weight and cost minimization for the composite sandwich structures. Classical lamination and beam bending theories were used, along with Monte Carlo simulation, to generate the design data for the proposed composite sandwich structure, which is applied as a floor panel for a high-speed train. Multilayer feedforward neural networks were used for predicting safety factors, cost, and weight of the designed structure based on the following inputs: core density, core thickness, face sheet materials’ combinations, and applied load. The trained Neural Network model was able to predict the considered results with a good performance metric, namely the coefficient of determination (R2 = 0.99) and the Mean Square Error (MSE = 1.3·10−5).Multi-objective optimization for cost and weight minimization was performed with a Genetic Algorithm using the derived ANN model. The obtained Pareto front provided several non-dominated optimal points leading to insights on the optimization process. The Finite Element Method (FEM) was used to model the key points of the optimal designs (i.e. the design with the lowest cost, weight, and Pareto optimal points). The FEM and optimization results had a maximum deviation of about 8.9%, indicating a good agreement between the two techniques.The newly elaborated methodology demonstrates a new approach for obtaining the optimum design of the investigated composite sandwich structure constructed from honeycomb core and laminated face sheets in terms the cost and weight. The study concluded that the use of Carbon Fiber-Reinforced Plastic (CFRP) or Fiber-Metal Laminate (FML) face sheets results in significant weight savings of about 59.5% and 48.6%, respectively, compared to an all-aluminum sandwich structure.

Experimental Study on Seismic Behavior of Newly Assembled Concrete Beam–Column Joints with L-Shaped Steel Bars

A novel concrete beam–column connection utilizing L-shaped steel bars is proposed to address the growing demand for prefabricated buildings and to ensure good seismic performance in such beam–column structures. After positioning two prefabricated beams with L-shaped tendons into the designated connection points at the top and bottom of the columns, concrete is poured into the post-cast section of the joint and the composite beam area, realizing a connection between the beams and columns. Quasi-static tests were performed on four combined backbone curves and one cast-in-place joint to investigate their failure modes and stress mechanisms. Through low-cycle repeated loading tests, it is found that measures such as increasing the area of the post-cast concrete in the joint area, the length of the L-shape, and the concrete strength in the composite beam area can effectively improve the bonding ability between the post-cast area of the joint specimens and the precast members, to improve the ductility performance, energy dissipation capacity, and bearing capacity of the joint specimens. The initial stiffness of the joint can be effectively improved by presetting the steel pipe in the column. Concurrently, the finite element method (FEM) was employed for parameter analysis. By integrating the test and FEM results, an equation for calculating the shear capacity of the connection was derived. The findings demonstrate that the hysteresis curve of the newly assembled joints is full, and its overall performance index is roughly the same as that of the cast-in-place joints. Additionally, enhancing the post-casting area of concrete, the length of the L-shaped bars, the concrete strength in the composite beam region, the axial compression ratio, or the steel tube dimensions can effectively improve the overall performance. The derived equation for the shear-bearing capacity of the connection satisfies design and application requirements.