Peer-reviewed journal articles covering structural engineering, computational mechanics, optimization, artificial intelligence, and related fields.
Research Article
J24. Optimum Design of RC Footings with Genetic Algorithms According to ACI 318-19
Authors:
G. Solorzano and V. Plevris*
Journal:
Buildings
Publisher:
MDPI
Volume: 10, Issue: 6
Article ID:
110
Page count:
17
Year: 2020
(First online: 11 June 2020)
Engineers usually use trial-and-error approaches for dealing with design problems where they need to find the most economical design of a structural element in terms of its material cost while satisfying all the safety requirements imposed by the design codes. In this study, we employ a genetic algorithm (GA) with a dominance-based tournament selection technique for dealing with this design challenge. The methodology is applied in the design of reinforced concrete rectangular-shaped isolated footings in accordance with the American Concrete Institute ACI 318-19. First, the footing is encoded into a set of decision variables and an objective function is defined to compute the total cost based on the different construction materials. Then, the compliance of the design with the ACI 318-19 code is enforced by a constraint function that takes into consideration all the demand–capacity ratios for the different resistance requirements such as the allowable bearing pressure of the supporting soil, and the shear and flexural capacities of the footing, among others. Two numerical examples are presented where the results show a significant advantage in terms of material-cost and design-time reduction in comparison with the commonly used trial and error approach, proving the applicability of optimization algorithms (OAs) into the everyday design routine of the structural engineer.
Research Article
J23. On the Performance of Differential Evolution Variants in Constrained Structural Optimization
Authors:
M. Georgioudakis and V. Plevris*
Journal:
Procedia Manufacturing
Publisher:
Elsevier
Volume: 44
Pages:
371–378
Year: 2020
Constrained optimization is a highly important field of engineering as most real-world optimization problems are associated with one or several constraints. Such problems are often challenging to solve due to their complexity and high nonlinearity. Differential evolution (DE) is arguably one of the most versatile and stable population-based search algorithms that exhibits robustness to multi-modal problems and has shown to be very efficient when solving constrained global optimization problems. In this paper we investigate the performance of several DE variants existing in the literature such as the traditional DE, the composite DE (CoDE), the adaptive DE with optional external archive (JADE) and the self-adaptive DE (jDE and SaDE), for handling constrained structural optimization problems. The performance of each DE variant is quantified by using three well-known benchmark structures in 2D and 3D. It is shown that JADE, which updates control parameters in an adaptive way, truly exhibits superior performance and outperforms the other DE variants in all the cases examined.
Editorial
J22. Editorial: Innovative Approaches in Computational Structural Engineering
Authors:
G. Tsiatas and V. Plevris*
Journal:
Frontiers in Built Environment
Publisher:
Frontiers
Volume: 6
Article ID:
39
Year: 2020
(First online: 08 April 2020)
This editorial introduces the Research Topic “Innovative Approaches in Computational Structural Engineering”, published in the Structural Engineering and Design section of Frontiers in Built Environment. Edited by George C. Tsiatas and Vagelis Plevris, the collection brings together contributions from researchers and engineering professionals worldwide, presenting innovative computational methods, numerical techniques, and practical applications in structural engineering. The Research Topic covers a broad range of areas, including structural modeling and analysis, finite and boundary element methods, structural optimization, earthquake engineering, structural dynamics, damage assessment, and advanced computational techniques. The editorial highlights the importance of continued innovation in computational structural engineering and the role of emerging methodologies in addressing increasingly complex engineering problems, improving numerical efficiency, and advancing structural analysis and design.
Research Article
J21. Cost-Based Optimum Design of Reinforced Concrete Retaining Walls Considering Different Methods of Bearing Capacity Computation
Authors:
N. Moayyeri, S. Gharehbaghi and V. Plevris*
Journal:
Mathematics
Publisher:
MDPI
Volume: 7, Issue: 12
Article ID:
1232
Page count:
21
Year: 2019
(First online: 12 December 2019)
This paper investigates the effect of computing the bearing capacity through different methods on the optimum construction cost of reinforced concrete retaining walls (RCRWs). Three well-known methods of Meyerhof, Hansen, and Vesic are used for the computation of the bearing capacity. In order to model and design the RCRWs, a code is developed in MATLAB. To reach a design with minimum construction cost, the design procedure is structured in the framework of an optimization problem in which the initial construction cost of the RCRW is the objective function to be minimized. The design criteria (both geotechnical and structural limitations) are considered constraints of the optimization problem. The geometrical dimensions of the wall and the amount of steel reinforcement are used as the design variables. To find the optimum solution, the particle swarm optimization (PSO) algorithm is employed. Three numerical examples with different wall heights are used to capture the effect of using different methods of bearing capacity on the optimal construction cost of the RCRWs. The results demonstrate that, in most cases, the final design based on the Meyerhof method corresponds to a lower construction cost. The research findings also reveal that the difference among the optimum costs of the methods is decreased by increasing the wall height.
Research Article
J20. Educational Challenges in Computer-based Finite Element Analysis and Design of Structures
Authors:
V. Plevris* and G. Markeset
Journal:
Journal of Computer Science
Publisher:
Science Publications
Volume: 14, Issue: 10
Pages:
1351–1362
Year: 2018
(First online: 24 October 2018)
Computer simulations and computational methods, such as the Finite Element Analysis (FEA) have become essential methodologies in science and engineering during the last decades, in a wide variety of academic fields. Six decades after the invention of the digital computer, advanced FE simulations are used to enhance and leapfrog theoretical and experimental progress, at different levels of complexity. Particularly in Civil and Structural Engineering, significant research work has been made lately on the development of FE simulation codes, methodologies and validation techniques for understanding the behavior of large and complex structures such as buildings, bridges, dams, offshore structures and others. These efforts are aimed at designing structures that are resilient to natural excitations (wind loads, earthquakes, floods) as well as human-made threats (impact, fire, explosion and others). The skill set required to master advanced FEA is inherently interdisciplinary, requiring in-depth knowledge of advanced mathematics, numerical methods and their computational implementation, as well as engineering sciences. In this paper, we focus on the importance of sound and profound engineering education and knowledge about the theory behind the Finite Element Method to obtain correct and reliable analysis results for designing real-world structures. We highlight common mistakes made by structural engineers while simulating complex structures and the risk of structural damage because of human-made mistakes or errors in the model assumptions. The event of the collapse and eventual sinking of a concrete offshore platform in the North Sea is presented as a case study where a serious error in the finite element analysis played a crucial role leading to structural failure and collapse.
Research Article
J19. OpenSeismoMatlab: A New Open-source Software for Strong Ground Motion Data Processing
Authors:
G. Papazafeiropoulos and V. Plevris*
Journal:
Heliyon
Publisher:
Cell Press
Volume: 4, Issue: 9
Article ID:
e00784
Page count:
39
Year: 2018
(First online: 26 September 2018)
OpenSeismoMatlab is an innovative open-source software for strong ground motion data processing, written in MATLAB. The software implements an elastoplastic bilinear kinematic hardening constitutive model and uses a state-of-the-art single step single solve time integration algorithm featuring exceptional speed, robustness and accuracy. OpenSeismoMatlab can calculate various time histories and corresponding peak values, Arias intensity and its time history, significant duration, various linear elastic response spectra and constant ductility inelastic response spectra, as well as Fourier amplitude spectrum and mean period. Due to its open-source nature, the software can be easily extended or modified, having high research and educational value for the professional engineering and research community. In the present paper, the structure, algorithms and main routines of the program are explained in detail and the results for various types of spectra of 11 earthquake strong ground motions are calculated and compared to corresponding results from other proprietary software.
Research Article
J18. A Combined Modal Correlation Criterion for Structural Damage Identification with Noisy Modal Data
Authors:
M. Georgioudakis and V. Plevris*
Journal:
Advances in Civil Engineering
Publisher:
Wiley
Volume: 2018
Article ID:
3183067
Page count:
20
Year: 2018
(First online: 14 May 2018)
Structural damage identification is a scientific field that has attracted a lot of interest in the scientific community during the recent years. There have been many studies intending to find a reliable method to identify damage in structural elements both in location and extent. Most damage identification methods are based on the changes of dynamic characteristics and static responses, but the incompleteness of the test data is a great obstacle for both. In this paper, a structural damage identification method based on the finite element model updating is proposed, in order to provide the location and the extent of structural damage using incomplete modal data of a damaged structure. The structural damage identification problem is treated as an unconstrained optimization problem which is solved using the differential evolution search algorithm. The objective function used in the optimization process is based on a combination of two modal correlation criteria, providing a measure of consistency and correlation between estimations of mode shape vectors. The performance and robustness of the proposed approach are evaluated with two numerical examples: a simply supported concrete beam and a concrete frame under several damage scenarios. The obtained results exhibit high efficiency of the proposed approach for accurately identifying the location and extent of structural damage.
Specialty Grand Challenge
J17. Computational structural engineering: Past achievements and future challenges
Authors:
V. Plevris* and G. Tsiatas
Journal:
Frontiers in Built Environment
Publisher:
Frontiers
Volume: 4
Article ID:
21
Year: 2018
(First online: 10 April 2018)
This Specialty Grand Challenge article presents an overview of the evolution, major achievements, and future research directions of computational structural engineering. Written by the authors in their capacity as Specialty Chief Editors of Frontiers in Built Environment, the article reflects on the development of computational methods and their transformative role in structural analysis and design. It discusses established numerical approaches, including the Finite Element Method (FEM) and Boundary Element Method (BEM), alongside emerging techniques such as meshfree methods and isogeometric analysis. Particular attention is given to key challenges facing the field, including system identification, multiscale and multiphysics modeling, simulation of additive manufacturing, uncertainty quantification, and high-performance and cloud computing. The article aims to outline a forward-looking research vision, identify promising opportunities for methodological advances, and encourage further innovation in computational structural engineering.
Research Article
J16. A new energy-based structural design optimization concept under seismic actions
Authors:
G. Papazafeiropoulos*, V. Plevris and M. Papadrakakis
Journal:
Frontiers in Built Environment
Publisher:
Frontiers
Volume: 3
Article ID:
44
Year: 2017
(First online: 31 July 2017)
A new optimization concept is introduced which involves the optimization of nonlinear planar shear buildings by using gradients based on equivalent linear structures, instead of the traditional practice of calculating the gradients from the nonlinear objective function. The optimization problem is formulated as an equivalent linear system of equations in which a target fundamental eigenfrequency and equal dissipated energy distribution within the storeys of the building are the components of the objective function. The concept is applied in a modified Newton-Raphson algorithm in order to find the optimum stiffness distribution of two representative linear or nonlinear MDOF shear buildings, so that the distribution of viscously damped and hysteretically dissipated energy respectively over the structural height is uniform. A number of optimization results are presented in which the effect of the earthquake excitation, the critical modal damping ratio and the normalized yield interstorey drift limit on the optimum stiffness distributions is studied. Structural design based on the proposed approach is more rational and technically feasible compared to other optimization strategies (e.g. uniform ductility concept), whereas it is expected to provide increased protection against global collapse and loss of life during strong earthquake events. Finally, it is proven that the new optimization concept not only reduces running times by as much as 91% compared to the classical optimization algorithms, but also it can be applied in other optimization algorithms which use gradient information to proceed to the optimum point.
Research Article
J15. Investigation of the mechanical behaviour of metakaolin-based sandcrete mixtures
Authors:
P.G. Asteris*, K. Kolovos, A. Athanasopoulou, V. Plevris and G. Konstantakatos
Journal:
European Journal of Environmental and Civil Engineering
Publisher:
Taylor & Francis
Volume: 23, Issue: 3
Pages:
300–324
Year: 2019
(First online: 19 January 2017)
This paper investigates the mechanical properties of sandcrete mixes with metakaolin (MK) as a mineral additive. Three different types of binders were prepared: one used as reference, based on 100% w/w ordinary Portland cement (PC), and two modified, where MK replaced PC in percentages 10% or 20% by weight of the reference cement content. Experimental tests have been carried out in order to obtain the full stress–strain diagram for the material under uniaxial compressive loading. The results of the tests have been post-processed numerically in order to obtain various mechanical characteristics of the sandcrete material, such as compressive strength, modulus of elasticity and strain at maximum strength. It has been found that, in general, specimens with metakaolin in the binder exhibit enhanced compressive strength compared to the reference samples without metakaolin. The improvement in compressive strength was more pronounced in samples with higher concentration of binder (50% w/w) and low or medium water per binder (W/B) ratio. Samples with higher compressive strength were characterised by higher values for the measured ultrasonic pulse velocity. The strain values, recorded at maximum strength, are considerably higher than the strain value of 2‰ for concrete under uniaxial compressive load.
Research Article
J14. A generalized algorithm framework for non-linear structural dynamics
Authors:
G. Papazafeiropoulos, V. Plevris and M. Papadrakakis
Journal:
Bulletin of Earthquake Engineering
Publisher:
Springer Nature
Volume: 15, Issue: 1
Pages:
411–441
Year: 2017
(First online: 28 July 2016)
The performance of a family of nonlinear generalized single step-single solve (GSSSS) time integration schemes is assessed by comparison of their results in terms of total energy and the agreement with respective results published in the literature. The nonlinear algorithms have been developed by their linear counterparts using a Newton–Raphson iterative procedure to ensure dynamic equilibrium inside each time step. A literature review of the available time integration schemes used for nonlinear problems and the family of linear GSSSS algorithms are presented along with several commonly used time integration algorithms as special cases. Afterwards, the nonlinear schemes are formulated, and outlined in an explicit flowchart, which describes the nonlinear integration procedure in detail. The nonlinear family of algorithms is applied to six benchmark problems involving the dynamic response of SDOF systems with various stiffness and damping properties, as well as to a 3dof structure representing finite element systems containing rigid connections, penalty factors and other such types of constraints. It is shown that the schemes with Continuous Acceleration formulation (such as the HHT-a method) perform in general better than the others, even with a large time step, which leads to reduced computational effort for the estimation of the nonlinear dynamic response with relatively little loss of accuracy.
Research Article
J13. Anisotropic Masonry Failure Criterion Using Artificial Neural Networks
Authors:
P.G. Asteris and V. Plevris
Journal:
Neural Computing and Applications
Publisher:
Springer Nature
Volume: 28, Issue: 8
Pages:
2207–2229
Year: 2017
(First online: 20 January 2016)
In the last decades, a plethora of advanced computational models and techniques have been proposed on the modeling, assessment and design of masonry structures. The successful application of such sophisticated models necessitates the development of reliable analytical models capable of describing the failure of masonry materials. Nevertheless, there is a lack of analytical models due to the anisotropic and brittle nature exhibited by the masonry materials. In the present paper, the use of neural networks (NNs) is proposed to approximate the failure surface of masonry materials in dimensionless form. The comparison of the derived results with experimental findings as well as analytical results demonstrates the promising potential of using NNs for the reliable and robust approximation of the masonry failure surface under biaxial stress.
Research Article
J12. Seismic vulnerability assessment of historical masonry structural systems
Authors:
P.G. Asteris*, M.P. Chronopoulos, C.Z. Chrysostomou, H. Varum, V. Plevris, N. Kyriakides and V. Silva
Journal:
Engineering Structures
Publisher:
Elsevier
Volume: 62–63
Pages:
118–134
Year: 2014
(First online: 15 February 2014)
Masonry structures are complex systems that require a thorough and detailed knowledge and information regarding their behavior under seismic loading. Appropriate modeling of a masonry structure is a prerequisite for a reliable earthquake resistant design or assessment. However, modeling a real structure to a robust quantitative (mathematical) representation is a very difficult, complex and computationally demanding task. This paper presents a methodology for earthquake resistant design or assessment of masonry structural systems. The entire process is illustrated using case studies from historical masonry structures in the European area. In particular, the applicability of the proposed method is checked via analyses of existing masonry buildings in three countries, namely Greece, Portugal and Cyprus, with different seismicity levels, influencing the risk impacting the masonry structures. Useful conclusions are drawn regarding the effectiveness of the intervention techniques used for the reduction of the vulnerability of the case-study structures, through the comparison of the results obtained.
Research Article
J11. Modeling of masonry failure surface under biaxial compressive stress using Neural Networks
Authors:
V. Plevris and P. Asteris*
Journal:
Construction and Building Materials
Publisher:
Elsevier
Volume: 55
Pages:
447–461
Year: 2014
(First online: 14 February 2014)
Masonry is a brittle anisotropic material that exhibits distinct directional properties because the mortar joints act as planes of weakness. To define failure under biaxial stress, a 3D surface in terms of the two principal stresses and their orientation to the bed joints, is required. In the present study, a novel method is proposed on applying Neural Networks (NNs) to approximate the failure surface for such brittle anisotropic materials. The method comprises a series of NNs that are trained with available experimental data. The results demonstrate the great potential of using NNs for the approximation of masonry failure surface under biaxial compressive stress.
Research Article
J10. Using LCR ultrasonic method to evaluate residual stress in dissimilar welded pipes
Authors:
Y. Javadi, V. Plevris and M.A. Najafabadi
Journal:
International Journal of Innovation, Management and Technology
Publisher:
IACSIT Press
Volume: 4, Issue: 1
Pages:
170–174
Year: 2013
The ultrasonic residual stresses measurement is based on the acoustoelastic effect that refers to the change in velocity of the elastic waves when propagating in a stressed media. The experimental method using the longitudinal critically refracted (LCR) waves requires an acoustoelastic calibration and an accuracy measurement of the time-of-flight on both stressed and unstressed media. This paper evaluates welding residual stresses in dissimilar pipe-pipe joint of AISI stainless steel 304 and Carbon Steel A106-B type. The residual stresses in inner and outer surface of pipes were evaluated by LCR ultrasonic waves by using 1Mhz, 2Mhz, 4Mhz and 5Mhz transducers. It has been shown that the difference in residual stresses between inner and outer surfaces of pipes and also between Stainless steel and Carbon Steel side can be inspected by LCR waves.
Research Article
J09. A hybrid Particle Swarm – Gradient algorithm for global structural optimization
Authors:
V. Plevris and M. Papadrakakis*
Journal:
Computer-Aided Civil and Infrastructure Engineering
Publisher:
Wiley
Volume: 26, Issue: 1
Pages:
48–68
Year: 2011
(First online: 07 April 2010)
The particle swarm optimization (PSO) method is an instance of a successful application of the philosophy of bounded rationality and decentralized decision making for solving global optimization problems. A number of advantages with respect to other evolutionary algorithms are attributed to PSO making it a prospective candidate for optimum structural design. The PSO-based algorithm is robust and well suited to handle nonlinear, nonconvex design spaces with discontinuities, exhibiting fast convergence characteristics. Furthermore, hybrid algorithms can exploit the advantages of the PSO and gradient methods. This article presents in detail the basic concepts and implementation of an enhanced PSO algorithm combined with a gradient-based quasi-Newton sequential quadratic programming (SQP) method for handling structural optimization problems. The proposed PSO is shown to explore the design space thoroughly and to detect the neighborhood of the global optimum. Then the mathematical optimizer, starting from the best estimate of the PSO and using gradient information, accelerates convergence toward the global optimum. A nonlinear weight update rule for PSO and a simple, yet effective, constraint handling technique for structural optimization are also proposed. The performance, the functionality, and the effect of different setting parameters are studied. The effectiveness of the approach is illustrated in some benchmark structural optimization problems. The numerical results confirm the ability of the proposed methodology to find better optimal solutions for structural optimization problems than other optimization algorithms.
Research Article
J08. Neurocomputing strategies for solving reliability-robust design optimization problems
Authors:
N.D. Lagaros, V. Plevris and M. Papadrakakis
Journal:
Engineering Computations
Publisher:
Emerald Publishing
Volume: 27, Issue: 7
Pages:
819–840
Year: 2010
Purpose – This paper, by taking randomness and uncertainty of structural systems into account aims to implement a combined reliability-based robust design optimization (RRDO) formulation. The random variables to be considered include the cross section dimensions, modulus of elasticity, yield stress, and applied loading. The RRDO problem is to be formulated as a multi-objective optimization problem where the construction cost and the standard deviation of the structural response are the objectives to be minimized.
Design/methodology/approach – The solution of the optimization problem is performed with the non-dominant cascade evolutionary algorithm with the weighted Tchebycheff metric, while the probabilistic analysis required is carried out with the Monte Carlo simulation method. Despite the computational advances, the solution of a RRDO problem for real-world structures is extremely computationally demanding and for this reason neurocomputing estimations are implemented.
Findings – The obtained estimates with the neural network predictions are shown to be very satisfactory in terms of accuracy for performing this type of computation. Furthermore, the present numerical results manage to achieve a reduction in computational time up to four orders of magnitude, for low probabilities of violation, compared to the conventional procedure making thus feasible the reliability-robust design optimization of realistic structures under probabilistic constraints.
Originality/value – The novel parts of the present work include the implementation of neurocomputing strategies in RRDO problems for reducing the computational cost and the comparison of the results given by RRDO and robust design optimization formulations, where the significance of taking into account probabilistic constraints is emphasized.
Research Article
J07. Reliability based robust design optimization of steel structures
Authors:
N.D. Lagaros, V. Plevris and M. Papadrakakis
Journal:
International Journal for Simulation and Multidisciplinary Design Optimization
Publisher:
EDP Sciences
Volume: 1, Issue: 1
Pages:
19–29
Year: 2007
(First online: 12 December 2007)
In this work the uncertainty of a structural system is taken into account in the framework of a structural Reliability based Robust Design Optimization (RRDO) formulation where probabilistic constraints are incorporated into the robust design optimization formulation. A robust design optimization problem is formulated as a multi-criteria optimization problem. The Pareto front representing the solution of the RRDO problem is composed by designs with a state of robustness, since their performance is the least sensitive to the variability of the uncertain variables. The cross section dimensions together with other structural parameters, such as the modulus of elasticity, the yield stress and the applied loading, are considered as random variables. For the solution of the RRDO problem, the non-dominant Cascade Evolutionary Algorithm is employed combined with a weighted Tchebycheff metric.
Research Article
J06. Design optimization of steel structures considering uncertainties
Authors:
M. Papadrakakis*, N.D. Lagaros and V. Plevris
Journal:
Engineering Structures
Publisher:
Elsevier
Volume: 27, Issue: 9
Pages:
1408–1418
Year: 2005
(First online: 17 May 2005)
In real world engineering applications the uncertainties of the structural parameters are inherent and the scatter from their nominal ideal values is in most cases unavoidable. These uncertainties play a dominant role in structural performance and the only way to assess this influence is to perform Reliability-Based Design Optimization (RBDO) and Robust Design Optimization (RDO). Compared to the basic deterministic-based optimization problem, a RBDO problem considers additional non-deterministic constraint functions, while the RDO yields a design with a state of robustness, so that its performance is the least sensitive to the variability of the uncertain parameters. The first part of this study examines the application of Neural Networks (NN) to the RBDO of large-scale structural systems, while the second part investigates the structural RDO problem. The use of NN in the framework of the RBDO problem is motivated by the approximate concepts inherent in reliability analysis and the time-consuming repeated analyses required by Monte Carlo Simulation. On the other hand the RDO is a multi-criteria optimization problem where the aim is to minimize both the weight of the structure and the variance of the structural response.
Research Article
J05. Multi-objective design optimization using cascade evolutionary computations
Authors:
N.D. Lagaros, V. Plevris and M. Papadrakakis*
Journal:
Computer Methods in Applied Mechanics and Engineering
Publisher:
Elsevier
Volume: 194, Issue: 30–33
Pages:
3496–3515
Year: 2005
(First online: 21 March 2005)
The consideration of uncertainties in conjunction with the probability of violation of the constraints imposed by the design codes is examined in the framework of structural optimization. The optimum design achieved based on a deterministic formulation is compared, in terms of the optimum weight, the probability of violation of the constraints and the probability of failure, with the optimum designs achieved through a robust design formulation where the variance of the response is considered as an additional criterion. The stochastic finite element problem is solved using the Monte Carlo Simulation method, combined with the Latin Hypercube Sampling technique for improving its computational efficiency. A non-dominant cascade evolutionary algorithm-based methodology is adopted for the solution of the multi-objective optimization problem encountered, in order to obtain the global Pareto front curve.