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10 pages, 708 KiB

Open AccessArticle

Numerical Homogenization Method Applied to Evaluate Effective Converse Flexoelectric Coefficients

by Khader M. Hamdia

Computation 2025, 13(2), 48; https://doi.org/10.3390/computation13020048 - 9 Feb 2025

Viewed by 544

This paper presents a numerical homogenization method for estimating the effective converse flexoelectric coefficients. A 2D model made of two-phase composite is developed at the microscale in consideration of a representative volume element that includes a continuous flexoelectric fiber embedded in a pure [...] Read more.

This paper presents a numerical homogenization method for estimating the effective converse flexoelectric coefficients. A 2D model made of two-phase composite is developed at the microscale in consideration of a representative volume element that includes a continuous flexoelectric fiber embedded in a pure elastic matrix. In the implementation, the constitutive equations are derived from the electromechanical enthalpy accounting for higher-order coupling terms. Electric boundary conditions associated with an inhomogeneous electric field are imposed, allowing the approximation of the generated mechanical strains and stresses. Accordingly, the numerical simulations yield the overall equivalent converse flexoelectricity tensor for the longitudinal, transversal, and shear couplings. The results showed that the composite undergoes an obvious straining, which creates actuation due to the converse effect. The components of the homogenized longitudinal and transverse coefficients were found to be dependent on the volume fraction and elastic properties of the constituents. Full article

(This article belongs to the Special Issue Computational Methods in Structural Engineering)

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35 pages, 15835 KiB

Open AccessArticle

Explainable Boosting Machine Learning for Predicting Bond Strength of FRP Rebars in Ultra High-Performance Concrete

by Alireza Mahmoudian, Maryam Bypour and Mahdi Kioumarsi

Computation 2024, 12(10), 202; https://doi.org/10.3390/computation12100202 - 9 Oct 2024

Cited by 4 | Viewed by 1469

Abstract

Aiming at evaluating the bond strength of fiber-reinforced polymer (FRP) rebars in ultra-high-performance concrete (UHPC), boosting machine learning (ML) models have been developed using datasets collected from previous experiments. The considered variables in this study are rebar type and diameter, elastic modulus and [...] Read more.

Aiming at evaluating the bond strength of fiber-reinforced polymer (FRP) rebars in ultra-high-performance concrete (UHPC), boosting machine learning (ML) models have been developed using datasets collected from previous experiments. The considered variables in this study are rebar type and diameter, elastic modulus and tensile strength of rebars, concrete compressive strength and cover, embedment length, and test method. The dataset contains two test methods: pullout tests and beam tests. Four types of rebar, including carbon fiber-reinforced polymer (CFRP), glass fiber-reinforced polymer (GFRP), basalt, and steel rebars, were considered. The boosting ML models applied in this study include AdaBoost, CatBoost, Gradient Boosting, XGBoost, and Hist Gradient Boosting. After hyperparameter tuning, these models demonstrated significant improvements in predictive accuracy, with XGBoost achieving the highest R² score of 0.95 and the lowest Root Mean Square Error (RMSE) of 2.21. Shapley values analysis revealed that tensile strength, elastic modulus, and embedment length are the most critical factors influencing bond strength. The findings offer valuable insights for applying ML models in predicting bond strength in FRP-reinforced UHPC, providing a practical tool for structural engineering. Full article

(This article belongs to the Special Issue Computational Methods in Structural Engineering)

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29 pages, 11922 KiB

Open AccessArticle

Using Machine Learning Algorithms to Develop a Predictive Model for Computing the Maximum Deflection of Horizontally Curved Steel I-Beams

by Elvis Ababu, George Markou and Sarah Skorpen

Computation 2024, 12(8), 151; https://doi.org/10.3390/computation12080151 - 24 Jul 2024

Viewed by 1162

Abstract

Horizontally curved steel I-beams exhibit a complicated mechanical response as they experience a combination of bending, shear, and torsion, which varies based on the geometry of the beam at hand. The behaviour of these beams is therefore quite difficult to predict, as they [...] Read more.

Horizontally curved steel I-beams exhibit a complicated mechanical response as they experience a combination of bending, shear, and torsion, which varies based on the geometry of the beam at hand. The behaviour of these beams is therefore quite difficult to predict, as they can fail due to either flexure, shear, torsion, lateral torsional buckling, or a combination of these types of failure. This therefore necessitates the usage of complicated nonlinear analyses in order to accurately model their behaviour. Currently, little guidance is provided by international design standards in consideration of the serviceability limit states of horizontally curved steel I-beams. In this research, an experimentally validated dataset was created and was used to train numerous machine learning (ML) algorithms for predicting the midspan deflection at failure as well as the failure load of numerous horizontally curved steel I-beams. According to the experimental and numerical investigation, the deep artificial neural network model was found to be the most accurate when used to predict the validation dataset, where a mean absolute error of 6.4 mm (16.20%) was observed. This accuracy far surpassed that of Castigliano’s second theorem, where the mean absolute error was found to be equal to 49.84 mm (126%). The deep artificial neural network was also capable of estimating the failure load with a mean absolute error of 30.43 kN (22.42%). This predictive model, which is the first of its kind in the international literature, can be used by professional engineers for the design of curved steel I-beams since it is currently the most accurate model ever developed. Full article

(This article belongs to the Special Issue Computational Methods in Structural Engineering)

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20 pages, 743 KiB

Open AccessFeature PaperArticle

Multi-Directional Functionally Graded Sandwich Plates: Buckling and Free Vibration Analysis with Refined Plate Models under Various Boundary Conditions

by Lazreg Hadji, Vagelis Plevris, Royal Madan and Hassen Ait Atmane

Computation 2024, 12(4), 65; https://doi.org/10.3390/computation12040065 - 27 Mar 2024

Cited by 18 | Viewed by 2009

Abstract

This study conducts buckling and free vibration analyses of multi-directional functionally graded sandwich plates subjected to various boundary conditions. Two scenarios are considered: a functionally graded (FG) skin with a homogeneous hard core, and an FG skin with a homogeneous soft core. Utilizing [...] Read more.

This study conducts buckling and free vibration analyses of multi-directional functionally graded sandwich plates subjected to various boundary conditions. Two scenarios are considered: a functionally graded (FG) skin with a homogeneous hard core, and an FG skin with a homogeneous soft core. Utilizing refined plate models, which incorporate a parabolic distribution of transverse shear stresses while ensuring zero shear stresses on both the upper and lower surfaces, equations of motion are derived using Hamilton’s principle. Analytical solutions for the buckling and free vibration analyses of multi-directional FG sandwich plates under diverse boundary conditions are developed and presented. The obtained results are validated against the existing literature for both the buckling and free vibration analyses. The composition of metal–ceramic-based FG materials varies longitudinally and transversely, following a power law. Various types of sandwich plates are considered, accounting for plate symmetry and layer thicknesses. This investigation explores the influence of several parameters on buckling and free vibration behaviors. Full article

(This article belongs to the Special Issue Computational Methods in Structural Engineering)

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15 pages, 630 KiB

Open AccessArticle

Taylor Polynomials in a High Arithmetic Precision as Universal Approximators

by Nikolaos Bakas

Computation 2024, 12(3), 53; https://doi.org/10.3390/computation12030053 - 7 Mar 2024

Viewed by 1863

Abstract

Function approximation is a fundamental process in a variety of problems in computational mechanics, structural engineering, as well as other domains that require the precise approximation of a phenomenon with an analytic function. This work demonstrates a unified approach to these techniques, utilizing [...] Read more.

Function approximation is a fundamental process in a variety of problems in computational mechanics, structural engineering, as well as other domains that require the precise approximation of a phenomenon with an analytic function. This work demonstrates a unified approach to these techniques, utilizing partial sums of the Taylor series in a high arithmetic precision. In particular, the proposed approach is capable of interpolation, extrapolation, numerical differentiation, numerical integration, solution of ordinary and partial differential equations, and system identification. The method employs Taylor polynomials and hundreds of digits in the computations to obtain precise results. Interestingly, some well-known problems are found to arise in the calculation accuracy and not methodological inefficiencies, as would be expected. In particular, the approximation errors are precisely predictable, the Runge phenomenon is eliminated, and the extrapolation extent may a priory be anticipated. The attained polynomials offer a precise representation of the unknown system as well as its radius of convergence, which provides a rigorous estimation of the prediction ability. The approximation errors are comprehensively analyzed for a variety of calculation digits and test problems and can be reproduced by the provided computer code. Full article

(This article belongs to the Special Issue Computational Methods in Structural Engineering)

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14 pages, 1625 KiB

Open AccessArticle

Numerical Covariance Evaluation for Linear Structures Subject to Non-Stationary Random Inputs

by M. Domaneschi, R. Cucuzza, L. Sardone, S. Londoño Lopez, M. Movahedi and G. C. Marano

Computation 2024, 12(3), 50; https://doi.org/10.3390/computation12030050 - 5 Mar 2024

Cited by 1 | Viewed by 1557

Abstract

Random vibration analysis is a mathematical tool that offers great advantages in predicting the mechanical response of structural systems subjected to external dynamic loads whose nature is intrinsically stochastic, as in cases of sea waves, wind pressure, and vibrations due to road asperity. [...] Read more.

Random vibration analysis is a mathematical tool that offers great advantages in predicting the mechanical response of structural systems subjected to external dynamic loads whose nature is intrinsically stochastic, as in cases of sea waves, wind pressure, and vibrations due to road asperity. Using random vibration analysis is possible, when the input is properly modeled as a stochastic process, to derive pieces of information about the structural response with a high quality (if compared with other tools), especially in terms of reliability prevision. Moreover, the random vibration approach is quite complex in cases of non-linearity cases, as well as for non-stationary inputs, as in cases of seismic events. For non-stationary inputs, the assessment of second-order spectral moments requires resolving the Lyapunov matrix differential equation. In this research, a numerical procedure is proposed, providing an expression of response in the state-space that, to our best knowledge, has not yet been presented in the literature, by using a formal justification in accordance with earthquake input modeled as a modulated white noise with evolutive parameters. The computational efforts are reduced by considering the symmetry feature of the covariance matrix. The adopted approach is applied to analyze a multi-story building, aiming to determine the reliability related to the maximum inter-story displacement surpassing a specified acceptable threshold. The building is presumed to experience seismic input characterized by a non-stationary process in both amplitude and frequency, utilizing a general Kanai–Tajimi earthquake input stationary model. The adopted case study is modeled in the form of a multi-degree-of-freedom plane shear frame system. Full article

(This article belongs to the Special Issue Computational Methods in Structural Engineering)

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25 pages, 6445 KiB

Open AccessArticle

The MLDAR Model: Machine Learning-Based Denoising of Structural Response Signals Generated by Ambient Vibration

by Spyros Damikoukas and Nikos D. Lagaros

Computation 2024, 12(2), 31; https://doi.org/10.3390/computation12020031 - 9 Feb 2024

Viewed by 2108

Abstract

Engineers have consistently prioritized the maintenance of structural serviceability and safety. Recent strides in design codes, computational tools, and Structural Health Monitoring (SHM) have sought to address these concerns. On the other hand, the burgeoning application of machine learning (ML) techniques across diverse [...] Read more.

Engineers have consistently prioritized the maintenance of structural serviceability and safety. Recent strides in design codes, computational tools, and Structural Health Monitoring (SHM) have sought to address these concerns. On the other hand, the burgeoning application of machine learning (ML) techniques across diverse domains has been noteworthy. This research proposes the combination of ML techniques with SHM to bridge the gap between high-cost and affordable measurement devices. A significant challenge associated with low-cost instruments lies in the heightened noise introduced into recorded data, particularly obscuring structural responses in ambient vibration (AV) measurements. Consequently, the obscured signal within the noise poses challenges for engineers in identifying the eigenfrequencies of structures. This article concentrates on eliminating additive noise, particularly electronic noise stemming from sensor circuitry and components, in AV measurements. The proposed MLDAR (Machine Learning-based Denoising of Ambient Response) model employs a neural network architecture, featuring a denoising autoencoder with convolutional and upsampling layers. The MLDAR model undergoes training using AV response signals from various Single-Degree-of-Freedom (SDOF) oscillators. These SDOFs span the 1–10 Hz frequency band, encompassing low, medium, and high eigenfrequencies, with their accuracy forming an integral part of the model’s evaluation. The results are promising, as AV measurements in an image format after being submitted to the trained model become free of additive noise. This with the aid of upscaling enables the possibility of deriving target eigenfrequencies without altering or deforming of them. Comparisons in various terms, both qualitative and quantitative, such as the mean magnitude-squared coherence, mean phase difference, and Signal-to-Noise Ratio (SNR), showed great performance. Full article

(This article belongs to the Special Issue Computational Methods in Structural Engineering)

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22 pages, 9781 KiB

Open AccessArticle

Investigation of the Failure Response of Masonry Walls Subjected to Blast Loading Using Nonlinear Finite Element Analysis

by Sipho G. Thango, Georgios E. Stavroulakis and Georgios A. Drosopoulos

Computation 2023, 11(8), 165; https://doi.org/10.3390/computation11080165 - 21 Aug 2023

Cited by 2 | Viewed by 3246

Abstract

A numerical investigation of masonry walls subjected to blast loads is presented in this article. A non-linear finite element model is proposed to describe the structural response of the walls. A unilateral contact–friction law is used in the interfaces of the masonry blocks [...] Read more.

A numerical investigation of masonry walls subjected to blast loads is presented in this article. A non-linear finite element model is proposed to describe the structural response of the walls. A unilateral contact–friction law is used in the interfaces of the masonry blocks to provide the discrete failure between the blocks. A continuum damage plasticity model is also used to account for the compressive and tensile failure of the blocks. The main goal of this article is to investigate the different collapse mechanisms that arise as an effect of the blast load parameters and the static load of the wall. Parametric studies are conducted to evaluate the effect of the blast source–wall (standoff) distance and the blast weight on the structural response of the system. It is shown that the traditional in-plane diagonal cracking failure mode may still dominate when a blast action is present, depending on the considered standoff distance and the blast weight when in-plane static loading is also applied to the wall. It is also highlighted that the presence of an opening in the wall may significantly reduce the effect of the blasting action. Full article

(This article belongs to the Special Issue Computational Methods in Structural Engineering)

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