Implementation of a coupled FEM-SBFEM for soil-structure interaction analysis of large-scale 3D base-isolated nuclear structures

Li, Jianbo; Liu, Yu; Lin, Gao

doi:10.1016/j.compgeo.2023.105669

Cited by 34 publications

(8 citation statements)

References 34 publications

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“…In recent years, the use of numerical and finite element methods in various fields of engineering sciences has been greatly developed [34][35][36][37]. A multi-scale finite element method [38] has been used to investigate the mechanical behavior of ductile iron, taking into account a random distribution of spherical graphite particles.…”

Section: Multi-scale Finite Element Simulationmentioning

confidence: 99%

Mechanical properties prediction of ductile iron with spherical graphite using multi-scale finite element model

Alizadeh,

Ajri,

Maleki

2023

Phys. Scr.

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In this paper, using the multi-scale finite element method, the effect of graphite particles on the mechanical behavior of ductile iron has been investigated under tensile loading. For this purpose, taking into account the spherical geometric shape of the graphite phase and considering a specific volume fraction, these spheres are randomly placed in the whole body and a two-component composite material is created. Next, by defining the mechanical properties of two separate phases including the matrix and graphites as well as the interfaces between these two phases, a micromechanical model is developed for these materials. The mechanical properties of the matrix are simulated using the Ramberg-Osgood elastic-plastic model. By simulation in ABAQUS software and using nonlinear dynamic analysis, the effects of volume percentages and adhesion of graphite particles with matrix on the direct tensile load-displacement behavior of ductile iron were investigated. The results of experimental tests were used to verify the results of the numerical model. The weight percentage of graphite particles has a significant effect on the tensile strength and elastic modulus of these cast irons. The results show that with the increase in the amount of graphite particles, the tensile strength of cast iron increases up to a certain value and then reverses. With 21% graphite particles, the maximum tensile strength of ductile iron is 601 MPa. Compared with a pure sample of cast iron, the tensile strength increases by approximately 13.4% for this weight percentage of graphite particles.

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Section: Multi-scale Finite Element Simulationmentioning

confidence: 99%

Mechanical properties prediction of ductile iron with spherical graphite using multi-scale finite element model

Alizadeh,

Ajri,

Maleki

2023

Phys. Scr.

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“…Taking image processing as an evident example of these sophisticated techniques, Jafarzadeh et al 11 used it along with experimental data to validate their analytical model for simulating flexible circular submerged mound motion in gravity waves. Advanced sensors 12 , artificial intelligence models 13 , 2D and 3D analytical software 14 , 15 , and innovative materials 16 are other examples that have been promisingly used.…”

Section: Introductionmentioning

confidence: 99%

Integrated machine learning for modeling bearing capacity of shallow foundations

Liu,

Liang

2024

Sci Rep

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Analyzing the stability of footings is a significant step in civil/geotechnical engineering projects. In this work, two novel predictive tools are suggested based on an artificial neural network (ANN) to analyze the bearing capacity of a footing installed on a two-layered soil mass. To this end, backtracking search algorithm (BSA) and equilibrium optimizer (EO) are employed to train the ANN for approximating the stability value (SV) of the system. After executing a set of finite element analyses, the settlement values lower/higher than 5 cm are considered to indicate the stability/failure of the system. The results demonstrated the efficiency of these algorithms in fulfilling the assigned task. In detail, the training error of the ANN (in terms of root mean square error—RMSE)) dropped from 0.3585 to 0.3165 (11.72%) and 0.2959 (17.46%) by applying the BSA and EO, respectively. Moreover, the prediction accuracy of the ANN climbed from 93.7 to 94.3% and 94.1% (in terms of area under the receiving operating characteristics curve—AUROC). A comparison between the elite complexities of these algorithms showed that the EO enjoys a larger accuracy, while BSA is a more time-effective optimizer. Lastly, an explicit mathematical formula is derived from the EO-ANN model to be conveniently used in predicting the SV.

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“…Despite the mentioned advantages of experimental works, they suffer from scale limitations and are costly and time-consuming [ 20 ]. Nowadays, significant advancements have been made in the field of structural design using numerical analysis and finite element methods (FEM) that enable engineers to accurately simulate the three-dimensional behavior of large-scale structures [ 21 , 22 ]. Moreover, in parallel with laboratory studies, computational intelligence methods have seen a significant increase in related structural engineering and have demonstrated practical tools for optimizing structural designs, improving material utilization, and accurately predicting possible failure points [ [23] , [24] , [25] , [26] , [27] ].…”

Section: Introductionmentioning

confidence: 99%

Design optimization of irregularity RC structure based on ANN-PSO

Zhang

2024

Heliyon

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Implementation of a coupled FEM-SBFEM for soil-structure interaction analysis of large-scale 3D base-isolated nuclear structures

Cited by 34 publications

References 34 publications

Mechanical properties prediction of ductile iron with spherical graphite using multi-scale finite element model

Mechanical properties prediction of ductile iron with spherical graphite using multi-scale finite element model

Integrated machine learning for modeling bearing capacity of shallow foundations

Design optimization of irregularity RC structure based on ANN-PSO

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