Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.3
2.2
Journals
Computation
Special Issues
Computational Methods in Structural Engineering
share announcement

Computational Methods in Structural Engineering

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 6367

Special Issue Editors

Dr. Manolis Georgioudakis

E-Mail Website1 Website2 Website3
Guest Editor
School of Civil Engineering, National Technical University of Athens, Athens, Greece
Interests: computational mechanics; engineering optimization; machine learning in engineering; structural analysis (statics/dynamics); finite element method; earthquake engineering; structural reliability; crack simulation in structures (XFEM); failure mechanics
Dr. Vagelis Plevris

E-Mail Website1 Website2 Website3
Guest Editor
Department of Civil and Environmental Engineering, Qatar University, Doha, Qatar
Interests: finite element method (FEM); static and dynamic analysis of structures with FEM; earthquake engineering; optimum design of structures; reliability and probabilistic analysis of structures; neural networks and their applications in engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Mahdi Kioumarsi

E-Mail Website1 Website2 Website3
Guest Editor
Department of Built Environment, Oslo Metropolitan University, Oslo, Norway
Interests: sustainable concrete; durability; corrosion; concrete technology; reinforced concrete structures; remaining service life of concrete structures; structural engineering; finite element analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Structural engineering involves the analysis and design of structures to ensure their safety, durability, and functionality. Traditionally, engineers have relied on manual calculations and simplified analytical models; however, with the rapid advancements in computing technology, computational methods have emerged as indispensable tools in the structural engineering domain. Computational methods have revolutionized the field of structural engineering by providing powerful tools for analysis, design, and optimization. As computing technology continues to evolve, computational methods will play an increasingly vital role in advancing the field of structural engineering, fostering safer, more efficient, and sustainable structures.

This Special Issue explores the application of computational methods in structural engineering, highlighting their significance, benefits, and challenges. It is devoted to presenting recent developments and bringing a new understanding to the field. The topics of this Special Issue include, but are not limited, to:

  • Finite element analysis;
  • Structural optimization;
  • Computational optimization techniques;
  • Performance-based design;
  • Numerical techniques for the analysis of composite structures or structures with unconventional shapes;
  • Complex structural dynamical systems;
  • Computational methods in conceptual structural design;
  • Machine learning applications in structural engineering.

Dr. Manolis Georgioudakis
Dr. Vagelis Plevris
Dr. Mahdi Kioumarsi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • structural engineering
  • computational methods
  • FEM
  • finite element analysis
  • optimization

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 743 KiB  
Article
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
Viewed by 620
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)
►▼ Show Figures

Figure 1

15 pages, 630 KiB  
Article
Taylor Polynomials in a High Arithmetic Precision as Universal Approximators
by Nikolaos Bakas
Computation 2024, 12(3), 53; https://doi.org/10.3390/computation12030053 - 07 Mar 2024
Viewed by 944
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)
►▼ Show Figures

Figure 1

14 pages, 1625 KiB  
Article
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 - 05 Mar 2024
Viewed by 774
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)
►▼ Show Figures

Figure 1

25 pages, 6445 KiB  
Article
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 - 09 Feb 2024
Viewed by 1201
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)
►▼ Show Figures

Figure 1

22 pages, 9781 KiB  
Article
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 1 | Viewed by 1922
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)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: USING MACHINE LEARNING ALGORITHMS TO DEVELOP A PREDICTIVE MODEL FOR COMPUTING THE MAXIMUM DEFLECTION OF HORIZONTALLY CURVED STEEL I-BEAMS
Authors: George Markou; Sarah Skorpen; Elvis Abab
Affiliation: Department of Civil Engineering, University of Pretoria, Pretoria, South Africa
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 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 on the serviceability limit states considerations of horizontally curved steel I-beams. In this research, an experimentally validated dataset was created which 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 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.

Back to TopTop