Full－range nonlinear analysis of fatigue behaviors of reinforced concrete structures by finite element method

The offshore reinforced concrete structures are always subject to cyclic load, such as wave load.In this paper a new finite element analysis model is developed to analyze the stress and strain state of reinforced concrete structures including offshore concrete structures, subject to any number of the cyclic load. On the basis of the anal ysis of the experimental data,this model simplifies the number of cycles-total cyclic strain curve of concrete as three straight line segments,and it is assumed that the stress-strain curves of different cycles in each segment are the same, thus the elastoplastic analysis is only needed for the first cycle of each segment, and the stress or strain corresponding to any number of cycles can be obtained by superposition of stress or strain obtained by the above e lastoplastic analysis based on the cyclic numbers in each segment.This model spends less computer time,and can obtain the stress and strain states of the structures after any number of cycles.The endochronic-damage and ideal offshore concrete platform subject to cyclic loading are experimented and analyzed by the finite element method based on the model proposed in this paper. The results between the experiment and the finite element analysis are in good agreement,which demonstrates the validity and accuracy of the proposed model.

THE RATIONALISM THEORY AND ITS FINITE ELEMENT ANALYSIS METHOD OF SHELL STRUCTURES

In this paper, a kind of rationalism theory of shell is established which is of different mechanic characters in tension and in compression, and the finite element numerical analysis method is also described.

Development of a Methodology for Determination and Analysis of Thermal Displacements of Machine Tools Using Finite Elements Method and Artificial Neural Network

In the processes of manufacturing, MT （machine tools） plays an important role in the manufacture of work pieces with complex and high dimensional and geometric accuracy. Much of the errors of a machine tool are those which are thermally induced which are from internal and external heat sources acting on the machine. In this paper, a methodology for determining and analyzing the thermal deformation of machine tools using FEM （finite element method） and ANN （artificial neural networks） is presented. After modeling the machine using FEM is defined the location of the heat sources, it is possible to obtain the temperature gradient and the corresponding thermal deformation at predetermined periods. Results obtained with simulations using the software NX.7.5 showed that this methodology is an effective tool in determining the thermal deformation of the machine, correlating the temperature reading at strategic points with volumetric deformation at the tool tip. Therefore, the thermal analysis of the errors in the pair tool part can be established. After training and validation process, the network will be able to make the prediction of thermal errors just stating the temperature values of specific points of each heat source, providing a way for compensation of thermally induced errors.

Multi-Material Topology Optimization of 2D Structures Using Convolutional Neural Networks

In recent years,there has been significant research on the application of deep learning(DL)in topology optimization(TO)to accelerate structural design.However,these methods have primarily focused on solving binary TO problems,and effective solutions for multi-material topology optimization(MMTO)which requires a lot of computing resources are still lacking.Therefore,this paper proposes the framework of multiphase topology optimization using deep learning to accelerate MMTO design.The framework employs convolutional neural network(CNN)to construct a surrogate model for solving MMTO,and the obtained surrogate model can rapidly generate multi-material structure topologies in negligible time without any iterations.The performance evaluation results show that the proposed method not only outputs multi-material topologies with clear material boundary but also reduces the calculation cost with high prediction accuracy.Additionally,in order to find a more reasonable modeling method for MMTO,this paper studies the characteristics of surrogate modeling as regression task and classification task.Through the training of 297 models,our findings show that the regression task yields slightly better results than the classification task in most cases.Furthermore,The results indicate that the prediction accuracy is primarily influenced by factors such as the TO problem,material category,and data scale.Conversely,factors such as the domain size and the material property have minimal impact on the accuracy.

Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group

This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.

Finite element response sensitivity analysis of three-dimensional soil-foundation-structure interaction (SFSI) systems

The nonlinear finite element（FE） analysis has been widely used in the design and analysis of structural or geotechnical systems.The response sensitivities（or gradients） to the model parameters are of significant importance in these realistic engineering problems.However the sensitivity calculation has lagged behind,leaving a gap between advanced FE response analysis and other research hotspots using the response gradient.The response sensitivity analysis is crucial for any gradient-based algorithms,such as reliability analysis,system identification and structural optimization.Among various sensitivity analysis methods,the direct differential method（DDM） has advantages of computing efficiency and accuracy,providing an ideal tool for the response gradient calculation.This paper extended the DDM framework to realistic complicated soil-foundation-structure interaction（SFSI） models by developing the response gradients for various constraints,element and materials involved.The enhanced framework is applied to three-dimensional SFSI system prototypes for a pilesupported bridge pier and a pile-supported reinforced concrete building frame structure,subjected to earthquake loading conditions.The DDM results are verified by forward finite difference method（FFD）.The relative importance（RI） of the various material parameters on the responses of SFSI system are investigated based on the DDM response sensitivity results.The FFD converges asymptotically toward the DDM results,demonstrating the advantages of DDM（e.g.,accurate,efficient,insensitive to numerical noise）.Furthermore,the RI and effects of the model parameters of structure,foundation and soil materials on the responses of SFSI systems are investigated by taking advantage of the sensitivity analysis results.The extension of DDM to SFSI systems greatly broaden the application areas of the d gradient-based algorithms,e.g.FE model updating and nonlinear system identification of complicated SFSI systems.

Finite element analysis of dynamic response and structure borne noise of gearbox

A dynamic finite element method combined with finite element mixed formula for contact problem is used to analyze the dynamic characteristics of gear system. Considering the stiffness excitation, error excitation and meshing shock excitation, the dynamic finite element model is established for the entire gear system which includes gears, shafts, bearings and gearbox housing. By the software of I-DEAS, the natural frequency, normal mode, dynamic time-domain response, frequency-domain response and one-third octave velocity grade structure borne noise of gear system are studied by the method of theoretical modal analysis and dynamic response analysis. The maximum values of vibration and structure borne noise are occurred at the mesh frequency of output grade gearing.

Three-Dimensional Thermo-Elastic-Plastic Finite Element Method Modeling for Predicting Weld-Induced Residual Stresses and Distortions in Steel Stiffened-Plate Structures

The objective of the present paper is to develop nonlinear finite element method models for predicting the weld-induced initial deflection and residual stress of plating in steel stiffened-plate structures. For this purpose, three-dimensional thermo-elastic-plastic finite element method computations are performed with varying plate thickness and weld bead length (leg length) in welded plate panels, the latter being associated with weld heat input. The finite element models are verified by a comparison with experimental database which was obtained by the authors in separate studies with full scale measurements. It is concluded that the nonlinear finite element method models developed in the present paper are very accurate in terms of predicting the weld-induced initial imperfections of steel stiffened plate structures. Details of the numerical computations together with test database are documented.

Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools

Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM) in this research field.The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort,therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time.This study aims to apply a hybrid method using FEM simulation and artificial neural network(ANN) analysis to approximate ballistic limit thickness for armor steels.To achieve this objective,a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition.In this methodology,the FEM simulations are used to create training cases for Multilayer Perceptron(MLP) three layer networks.In order to validate FE simulation methodology,ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569.Afterwards,the successfully trained ANN(s) is used to predict the ballistic limit thickness of 500 HB high hardness steel armor.Results show that even with limited number of data,FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy.

Fatigue Strength Analysis of Dissimilar Aluminum Alloy TIG Welds

The constant amplitude loading fatigue tests were carried out on the 6061/7075 aluminum alloy TIG fillet welded lap specimens in this study,and the weld seam cross-section hardness was measured.The experimental results show that most specimens mainly failed at the 7075 side weld toes even though the base material tensile strength of 7075 is higher than that of 6061.The maximum stress-strain concentration in the two finite element models is located at the 7075 side weld toe,which is basically consistent with the actual fracture location.The weld zone on the 7075 side experiences severe material softening,with a large gradient.However,the Vickers hardness value on the 6061 side negligibly changes and fluctuates around 70 HV.No obvious defects are found on the fatigue fracture,but a large number of secondary cracks appear.Cracks germinate from the weld toe and propagate in the direction of the plate thickness.Weld reinforcement has a serious impact on fatigue life.Fatigue life will decrease exponentially as the weld reinforcement increases under low stress.It is found that the notch stress method can give a better fatigue life prediction for TIG weldments,and the errors of the predicted results are within the range of two factors,while the prediction accuracy decreases under low stress.The equivalent structural stress method can also be used for fatigue life prediction of TIG weldments,but the errors of prediction results are within the range of three factors,and the accuracy decreases under high stress.

NONLINEAR ANALYSIS OF REINFORCED CONCRETE RECTANGULAR SLABS USING FINITE ELEMENT METHOD

The nonlinear analysis of reinforced concrete rectangular slabs undermonotonic transverse loads is performed by finite element method.The layered rectangu-lar element with 4 nodes and 20 degrees of freedom is developed,in whichbending-stretching coupling effect is taken into account.An orthotropic equivalentuniaxial stress-strain constitutive model of concrete is used.A program is worked out andused to calculate two reinforced concrete slabs.The results of calculation are in goodconformity with the corresponding test results.In addition,the influence of tension stif-fening effect of cracked concrete on the results of calculation is discussed.

Elasto-Plastic Analysis of Tubular Joints of Offshore Platforms by Finite Element Method

The plastic node method is reformulated by the variational principle and is applied to elasto-plastic finite element analysis of tubular joints, eventually including the effect of internal and external gussets, stiffener rings, etc., if necessary. Four different joints are studied here in detail for the elasto-plastic behavior, the strain at the hot spot, the strain concentration factor around the intersection line, and the propagation of the plastic region with loading up to collapse in order to determine the ultimate strength, safety factor, and development of the plastic field. The present results are in good agreement with the experimental results.

APPLICATION OF STOCHASTIC FINITE ELEMENT METHOD TO STRENGTH AND STABILITY ANALYSIS OF EARTH DAMS

This paper applies the stochastic finite element method to analyse the statistics of stresses in earth dams and assess the safety and reliability of the dams. Formulations of the stochastic finite element method are briefly reviewed and the procedure for assessing dam's strength and stability is described. As an example, a detailed analysis for an actual dam Nululin dam is performed. A practical method for studying built-dams based on the prototype observation data is described.

A NEW METHOD FOR STRESS ANALYSIS OF A CYCLICALLY SYMMETRIC STRUCTURE

In this paper, a computational method for finite element stress analysis of a cyclically symmetric structure subjected to arbitrary loads is provided. At first, using discrete Fourier transformation technique, the complete structure is analyzed by considering only one sector with appropriate complex constraints on its boundary with the adjacent sectors. Next, an imaginary structure which is composed of two identically overlapping sectors is constructed, and that the complex constraints mentioned above can be equivalently replaced by a set of real constraints on this imaginary structure is proved. Therefore, the stress analysis of a cyclically symmetric structure can be solved conveniently by most of finite element programs.

Neural network method for solving elastoplastic finite element problems

A basic optimization principle of Artificial Neural Network—the Lagrange Programming Neural Network (LPNN) model for solving elastoplastic finite element problems is presented. The nonlinear problems of mechanics are represented as a neural network based optimization problem by adopting the nonlinear function as nerve cell transfer function. Finally, two simple elastoplastic problems are numerically simulated. LPNN optimization results for elastoplastic problem are found to be comparable to traditional Hopfield neural network optimization model.

Energy density response prediction of structures with uncertainty using interval analysis method

Prediction of vibration energy responses of structures with uncertainties is of interest in many fields. The energy density control equation for one-dimensional structure is provided firstly. Interval analysis method is applied to the control equation to obtain the range of energy density responses of structures with interval parameters. A cantilever beam with interval-valued damping coefficient is exemplified to carry out a simulation. The result shows that the mean value of energy density from the interval analysis method is the same as that from a probabilistic method which validates the interval analysis method. Besides, the response range from the interval analysis method is wider and includes that from the probabilistic method which indicates the interval analysis method is a more conservative method and is safer in realistic engineering structures.

Prediction on the relative permittivity of energy storage composite dielectrics using convolutional neural networks:A fast and accurate alternative to finite-element method

The relative permittivity is one of the essential parameters determines the physical polarization behaviors of the nanocomposite dielectrics in many applications,particularly for capacitive energy storage.Predicting the relative permittivity of particle/polymer nanocomposites from the microstructure is of great significance.However,the classical effective medium theory and physics-based numerical calculation represented by finite element method are time-consuming and cumbersome for complex structures and nonlinear problem.The work explores a novel architecture combining the convolutional neural network(ConvNet)and finite element method(FEM)to predict the relative permittivity of nanocomposite dielectrics with incorporated barium titanite(BT)particles in polyvinylidene fluoride(PVDF)matrix.The ConvNet was trained and evaluated on big datasets with 14266 training data and 3514 testing data generated form a programmatic algorithm.Through numerical experiments,we demonstrate that the trained network can efficiently provide an accurate agreement between the ConvNet model and FEM by virtue of the significant evaluation metrics R2,which reaches as high as 0.9783 and 0.9375 on training and testing data,respectively.The strong universality of the presented method allows for an extension to fast and accurately predict other properties of the nanocomposite dielectrics.

An improved pseudo-static method for seismic resistant design of underground structures

This paper describes a commonly used pseudo-static method in seismic resistant design of the cross section of underground structures. Based on dynamic theory and the vibration characteristics of underground structures, the sources of errors when using this method are analyzed. The traditional seismic motion loading approach is replaced by a method in which a one-dimensional soil layer response stress is differentiated and then converted into seismic live loads. To validate the improved method, a comparison of analytical results is conducted for internal forces under earthquake shaking of a typical shallow embedded box-shaped subway station structure using four methods： the response displacement method, finite element response acceleration method, the finite element dynamic analysis method and the improved pseudo-static calculation method. It is shown that the improved finite element pseudo-static method proposed in this paper provides an effective tool for the seismic design of underground structures. The evaluation yields results close to those obtained by the finite element dynamic analysis method, and shows that the improved finite element pseudo-static method provides a higher degree of precision.

Calculation method of ship collision force on bridge using artificial neural network

Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent static load, or the use of finite element method (FEM) which is more time-consuming and requires supercomputing resources. In this paper, we proposed an alternative approach that combines FEM with artificial neural network (ANN). The radial basis function neural network (RBFNN) employed for calculating the impact force in consideration of ship-bridge collision mechanics. With ship velocity and mass as the input vectors and ship collision force as the output vector, the neural networks for different network parameters are trained by the learning samples obtained from finite element simulation results. The error analyses of the learning and testing samples show that the proposed RBFNN is accurate enough to calculate ship-bridge collision force. The input-output relationship obtained by the RBFNN is essentially consistent with the typical empirical formulae. Finally, a special toolbox is developed for calculation efficiency in application using MATLAB software.