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Journal articles on the topic 'Evolutionary structural optimisation'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

Chen, Yu‐Ming, Kuo‐Shuh Fan, and Ban‐Jwu Shih. "2.5D Nodal based evolutionary structural optimisation." Journal of the Chinese Institute of Engineers 33, no. 6 (September 2010): 899–908. http://dx.doi.org/10.1080/02533839.2010.9671678.

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2

Proos, K. A., G. P. Steven, O. M. Querin, and Y. M. Xie. "Stiffness and inertia multicriteria evolutionary structural optimisation." Engineering Computations 18, no. 7 (November 2001): 1031–54. http://dx.doi.org/10.1108/02644400110404028.

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3

Querin, O. M., G. P. Steven, and Y. M. Xie. "Evolutionary structural optimisation using an additive algorithm." Finite Elements in Analysis and Design 34, no. 3-4 (February 2000): 291–308. http://dx.doi.org/10.1016/s0168-874x(99)00044-x.

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4

Kim, H., M. J. Garcia, O. M. Querin, G. P. Steven, and Y. M. Xie. "Introduction of fixed grid in evolutionary structural optimisation." Engineering Computations 17, no. 4 (June 2000): 427–39. http://dx.doi.org/10.1108/02644400010334838.

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5

Querin, O. M., G. P. Steven, and Y. M. Xie. "Evolutionary structural optimisation (ESO) using a bidirectional algorithm." Engineering Computations 15, no. 8 (December 1998): 1031–48. http://dx.doi.org/10.1108/02644409810244129.

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6

Guan, Hong, Grant P. Steven, and Yi-Min Xie. "Evolutionary Structural Optimisation Incorporating Tension and Compression Materials." Advances in Structural Engineering 2, no. 4 (August 1999): 273–88. http://dx.doi.org/10.1177/136943329900200403.

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7

Chen, G., G. J. Pettet, M. Pearcy, and D. L. S. McElwain. "Modelling external bone adaptation using evolutionary structural optimisation." Biomechanics and Modeling in Mechanobiology 6, no. 4 (August 25, 2006): 275–85. http://dx.doi.org/10.1007/s10237-006-0055-9.

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8

Chiu, Louis N. S., Daniel Stojanov, Bernard Rolfe, and Wen Yi Yan. "Effect of Optimisation Parameters in Topology Optimisation." Key Engineering Materials 725 (December 2016): 529–34. http://dx.doi.org/10.4028/www.scientific.net/kem.725.529.

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The Bi-directional Evolutionary Structural Optimisation (BESO) method had been used by many authors for the optimisation of structures. This work sets out to investigate the effect of key optimisation parameters utilized in the BESO method, the evolution rate and the filter radius, on the outcome of the optimisation. An understanding of the interaction of these factors in the optimisation process enables a more efficient way to produce optimised components that can fully capitalise on the capabilities of additive manufacturing.

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9

Steven, Grant, Osvaldo Querin, and Mike Xie. "Evolutionary structural optimisation (ESO) for combined topology and size optimisation of discrete structures." Computer Methods in Applied Mechanics and Engineering 188, no. 4 (August 2000): 743–54. http://dx.doi.org/10.1016/s0045-7825(99)00359-x.

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Merten, Errol A. "Application of Evolutionary Structural Optimisation; Reinventing the (Bicycle) Wheel." Applied Mechanics and Materials 553 (May 2014): 830–35. http://dx.doi.org/10.4028/www.scientific.net/amm.553.830.

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This paper reports the application of Evolutionary Structural Optimisation (ESO) to minimise the weight of a bicycle wheel rim. Two finite element models were developed to analyse the rims internal structure. The internal structure of a bicycle wheel rim consists of two walls connected by a rounded spoke bed, and a horizontal spar. One model was constructed with linear plate elements and suggested material should be distributed around the spoke bed and away from the rim walls, however inaccuracies were found in the spar section. ESO of the second model, consisting of linear brick elements presented similar results but modeled the spar section accurately. Results suggested that ESO applied to bicycle wheel rims can improve the performance of the wheel through weight reduction. These findings present a simple example to engineers who wish to use ESO for improvements to the material distribution of any hollow shell structure.

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Rong, J. H., Y. M. Xie, and X. Y. Yang. "An improved method for evolutionary structural optimisation against buckling." Computers & Structures 79, no. 3 (January 2001): 253–63. http://dx.doi.org/10.1016/s0045-7949(00)00145-0.

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Bureerat, Sujin, and Jumlong Limtragool. "Performance enhancement of evolutionary search for structural topology optimisation." Finite Elements in Analysis and Design 42, no. 6 (March 2006): 547–66. http://dx.doi.org/10.1016/j.finel.2005.10.011.

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13

Querin, O. M., V. Young, G. P. Steven, and Y. M. Xie. "Computational efficiency and validation of bi-directional evolutionary structural optimisation." Computer Methods in Applied Mechanics and Engineering 189, no. 2 (September 2000): 559–73. http://dx.doi.org/10.1016/s0045-7825(99)00309-6.

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14

Hofmeyer, H., and J. M. Davila Delgado. "Automated design studies: Topology versus One-Step Evolutionary Structural Optimisation." Advanced Engineering Informatics 27, no. 4 (October 2013): 427–43. http://dx.doi.org/10.1016/j.aei.2013.03.003.

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15

Kunakote, Tawatchai, and Sujin Bureerat. "Surrogate-Assisted Multiobjective Evolutionary Algorithms for Structural Shape and Sizing Optimisation." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/695172.

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The work in this paper proposes the hybridisation of the well-established strength Pareto evolutionary algorithm (SPEA2) and some commonly used surrogate models. The surrogate models are introduced to an evolutionary optimisation process to enhance the performance of the optimiser when solving design problems with expensive function evaluation. Several surrogate models including quadratic function, radial basis function, neural network, and Kriging models are employed in combination with SPEA2 using real codes. The various hybrid optimisation strategies are implemented on eight simultaneous shape and sizing design problems of structures taking into account of structural weight, lateral bucking, natural frequency, and stress. Structural analysis is carried out by using a finite element procedure. The optimum results obtained are compared and discussed. The performance assessment is based on the hypervolume indicator. The performance of the surrogate models for estimating design constraints is investigated. It has been found that, by using a quadratic function surrogate model, the optimiser searching performance is greatly improved.

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16

Kwan, A. S. K. "An Evolutionary Approach for Layout Optimisation of Truss Structures." International Journal of Space Structures 13, no. 3 (September 1998): 145–55. http://dx.doi.org/10.1177/026635119801300304.

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A new approach is presented in this paper for optimisation of framework structures where the number of joints and bars, as well as their locations, are allowed to change. Only the positions of loads and supports need to be provided, and the optimisation process is allowed to propose any suitable structural configuration based on a set of given quantifiable criteria. No underlying grid is used, thus neither topology nor geometry is restricted to a predefined array. The approach is based on, but is also distinct from, the principles of Genetic Algorithms. Two illustrative examples are presented to demonstrate the potential of the present approach.

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Stojanov, Daniel, Brian G. Falzon, Xin Hua Wu, and Wen Yi Yan. "An Application of Bi-Directional Evolutionary Structural Optimisation for Optimising Energy Absorbing Structures Using a Material Damage Model." Applied Mechanics and Materials 553 (May 2014): 836–41. http://dx.doi.org/10.4028/www.scientific.net/amm.553.836.

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The Bi-directional Evolutionary Structural Optimisation (BESO) method is a numerical topology optimisation method developed for use in finite element analysis. This paper presents a particular application of the BESO method to optimise the energy absorbing capability of metallic structures. The optimisation objective is to evolve a structural geometry of minimum mass while ensuring that the kinetic energy of an impacting projectile is reduced to a level which prevents perforation. Individual elements in a finite element mesh are deleted when a prescribed damage criterion is exceeded. An energy absorbing structure subjected to projectile impact will fail once the level of damage results in a critical perforation size. It is therefore necessary to constrain an optimisation algorithm from producing such candidate solutions. An algorithm to detect perforation was implemented within a BESO framework which incorporated a ductile material damage model.

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Barthold, F. J. "A structural optimisation viewpoint on growth phenomena." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 2 (October 1, 2012): 247–52. http://dx.doi.org/10.2478/v10175-012-0033-6.

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Abstract. Evolutionary solid bodies undergoing changes of mass, of properties, and of shapes are considered in models of growth and adaptation and similarily in structural optimisation. A fundamental separation of different growth phenomena and a subsequent parametrisation using independent design variables for the amount of substance as well as for molar mass and molar volume facilitates an efficient formulation of the design space. Thus, the effects of design variations, i.e. change of amount of substance, on the variations of the structural response, i.e. the deformation in physical space, can be clearly described. Overall, a novel treatment of growth processes based on an evolution of the amount of substance is outlined. The parallelism of variations in physical and design space are highlighted and compared with the multiplicative decomposition of the deformation gradient into a growth and an elastic part incorporating an incompatible intermediate configuration. This drawback is overcome by a compatible manifold based on material points modelling the amount of substance outside of any geometrical space.

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Bao, Ding Wen, Xin Yan, and Yi Min Xie. "Encoding topological optimisation logical structure rules into multi-agent system for architectural design and robotic fabrication." International Journal of Architectural Computing 20, no. 1 (March 2022): 7–17. http://dx.doi.org/10.1177/14780771221082257.

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Natural phenomena have been explored as a source of architectural and structural design inspiration with different approaches undertaken within architecture and engineering. The research proposes a connection between two dichotomous principles: architectural complexity and structural efficiency through a hybrid of natural phenomena, topology optimisation and generative design. Both Bi-directional Evolutionary Structural Optimisation (BESO) and multi-agent algorithms are emerging technologies developed into new approaches that transform architectural and structural design, respectively, from the logic of topology optimisation and swarm intelligence. This research aims to explore a structural behaviour feedback loop in designing intricate functional forms through encoding BESO logical structure rules into the multi-agent algorithm. This research intends to study and evaluate the application of topology optimisation and multi-agent system in form-finding and later robotic fabrication through a series of prototypes. It reveals a supposition that the structural behaviour-based design method matches the beauty and function of natural appearance and structure. Thus, a new exploration of architectural design and fabrication strategy is introduced, which benefits the collaboration among architects, engineers and manufacturers. There is the potential to seek the ornamental complexities in architectural forms and the most efficient use of material based on structural performance in the process of generating complex geometry of the building and its various elements.

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20

Munk, David J., Gareth A. Vio, and Grant P. Steven. "A Bi-directional Evolutionary Structural Optimisation algorithm with an added connectivity constraint." Finite Elements in Analysis and Design 131 (September 2017): 25–42. http://dx.doi.org/10.1016/j.finel.2017.03.005.

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21

Haiba, M., D. C. Barton, P. C. Brooks, and M. C. Levesley. "Evolutionary structural optimisation of dynamically loaded components in consideration of fatigue life." Advances in Engineering Software 36, no. 1 (January 2005): 49–57. http://dx.doi.org/10.1016/j.advengsoft.2004.03.022.

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22

Bhat, Shantanu S., Jisheng Zhao, John Sheridan, Kerry Hourigan, and Mark C. Thompson. "Evolutionary shape optimisation enhances the lift coefficient of rotating wing geometries." Journal of Fluid Mechanics 868 (April 11, 2019): 369–84. http://dx.doi.org/10.1017/jfm.2019.183.

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Wing shape is an important factor affecting the aerodynamic performance of wings of monocopters and flapping-wing micro air vehicles. Here, an evolutionary structural optimisation method is adapted to optimise wing shape to enhance the lift force due to aerodynamic pressure on the wing surfaces. The pressure distribution is observed to vary with the span-based Reynolds number over a range covering most insects and samaras. Accordingly, the optimised wing shapes derived using this evolutionary approach are shown to adjust with Reynolds number. Moreover, these optimised shapes exhibit significantly higher lift coefficients (${\sim}50\,\%$) than the initial rectangular wing forebear. Interestingly, the optimised shapes are found to have a large area outboard, broadly in line with the features of high-lift forewings of multi-winged insects. According to specific aerodynamic performance requirements, this novel method could be employed in the optimisation of improved wing shapes for micro air vehicles.

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23

Shankar, M. R., A. C. Niranjanappa, and B. Dattaguru. "Aerodynamic and Structural Optimisation of Maritime Patrol Radar System Radome using Evolutionary Algorithms." Defence Science Journal 71, no. 4 (July 1, 2021): 421–28. http://dx.doi.org/10.14429/dsj.71.16249.

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Airborne early warning systems are deployed for collecting surveillance information on airborne enemy targets in real-time. The Maritime Patrol Radar system is used for surveillance of sea surface for various types of ships and low flying aircraft. Radio Detection And Ranging system, or RADAR, in short, is an Electromagnetic sensor integrated on such airborne platforms. An antenna of this radar system is generally mounted under the belly of the aircraft and protected by a cover called a radome. This radome is installed to protect the radar antenna from environmental disturbances. Due to the installation of the radome, increased drag is experienced by aircraft during its flight due to resistance to the flow of the oncoming air. Radome design is a multidisciplinary effort involving structural, aerodynamics, and electromagnetic disciplines. In this study, the multi-disciplinary design of the maritime patrol aircraft radome for optimality in terms of structural strength and aerodynamic performance is carried out by integrating both disciplinary analyses on an optimisation software platform. The utopia point in terms of these two disciplines is found.

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Cervera, E., and J. Trevelyan. "Evolutionary structural optimisation based on boundary representation of NURBS. Part I: 2D algorithms." Computers & Structures 83, no. 23-24 (September 2005): 1902–16. http://dx.doi.org/10.1016/j.compstruc.2005.02.016.

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Cervera, E., and J. Trevelyan. "Evolutionary structural optimisation based on boundary representation of NURBS. Part II: 3D algorithms." Computers & Structures 83, no. 23-24 (September 2005): 1917–29. http://dx.doi.org/10.1016/j.compstruc.2005.02.017.

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26

Das, R., R. Jones, and S. Chandra. "Damage tolerance based shape design of a stringer cutout using evolutionary structural optimisation." Engineering Failure Analysis 14, no. 1 (January 2007): 118–37. http://dx.doi.org/10.1016/j.engfailanal.2005.11.008.

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27

Dörgő, Gyula, and János Abonyi. "Hierarchical Representation Based Constrained Multi-objective Evolutionary Optimisation of Molecular Structures." Periodica Polytechnica Chemical Engineering 63, no. 1 (June 14, 2018): 210–25. http://dx.doi.org/10.3311/ppch.12021.

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We propose an efficient algorithm to generate Pareto optimal set of reliable molecular structures represented by group contribution methods. To effectively handle structural constraints we introduce goal oriented genetic operators to the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The constraints are defined based on the hierarchical categorisation of the molecular fragments. The efficiency of the approach is tested on several benchmark problems. The proposed approach is highly efficient to solve the molecular design problems, as proven by the presented benchmark and refrigerant design problems.

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Ebenau, Carsten, Jens Rottschäfer, and Georg Thierauf. "An advanced evolutionary strategy with an adaptive penalty function for mixed-discrete structural optimisation." Advances in Engineering Software 36, no. 1 (January 2005): 29–38. http://dx.doi.org/10.1016/j.advengsoft.2003.10.008.

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Manikowski, Pawel L., David J. Walker, and Matthew J. Craven. "Multi-Objective Optimisation of the Benchmark Wind Farm Layout Problem." Journal of Marine Science and Engineering 9, no. 12 (December 3, 2021): 1376. http://dx.doi.org/10.3390/jmse9121376.

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Wind farm layout optimisation has become a very challenging and widespread problem in recent years. In many publications, the main goal is to achieve the maximum power output and minimum wind farm cost. This may be accomplished by applying single or multi-objective optimisation techniques. In this paper, we apply a single objective hill-climbing algorithm (HCA) and three multi-objective evolutionary algorithms (NSGA-II, SPEA2 and PESA-II) to a well-known benchmark optimisation problem proposed by Mosetti et al., which includes three different wind scenarios. We achieved better results by applying single- and multi-objective algorithms. Furthermore, we showed that the best performing multi-objective algorithm was NSGA-II. Finally, an extensive comparison of the results of past publications is made.

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Ekici, Berk, Cemre Cubukcuoglu, Michela Turrin, and I. Sevil Sariyildiz. "Performative computational architecture using swarm and evolutionary optimisation: A review." Building and Environment 147 (January 2019): 356–71. http://dx.doi.org/10.1016/j.buildenv.2018.10.023.

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Rajagopal, S., and R. Ganguli. "Conceptual design of UAV using Kriging based multi-objective genetic algorithm." Aeronautical Journal 112, no. 1137 (November 2008): 653–62. http://dx.doi.org/10.1017/s0001924000002621.

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Abstract This paper highlights unmanned aerial vehicle (UAV) conceptual design using the multi-objective genetic algorithm (MOGA). The design problem is formulated as a multidisciplinary design optimisation (MDO) problem by coupling aerodynamic and structural analysis. The UAV considered in this paper is a low speed, long endurance aircraft. The optimisation problem uses endurance maximization and wing weight minimisation as dual objective functions. In this multi-objective optimisation, aspect ratio, wing loading, taper ratio, thickness-to-chord ratio, loiter velocity and loiter altitude are considered as design variables with stall speed, maximum speed and rate of climb as constraints. The MDO system integrates the aircraft design code, RDS and an empirical relation for objective function evaluation. In this study, the optimisation problem is solved in two approaches. In the first approach, the RDS code is directly integrated in the optimisation loop. In the second approach, Kriging model is employed. The second approach is fast and efficient as the meta-model reduces the time of computation. A relatively new multi-objective evolutionary algorithm named NSGA-II (non-dominated sorting genetic algorithm) is used to capture the full Pareto front for the dual objective problem. As a result of optimisation using multi-objective genetic algorithm, several non-dominated solutions indicating number of useful Pareto optimal designs is identified.

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Nguyen, T., K. Ghabraie, and T. Tran-Cong. "Applying bi-directional evolutionary structural optimisation method for tunnel reinforcement design considering nonlinear material behaviour." Computers and Geotechnics 55 (January 2014): 57–66. http://dx.doi.org/10.1016/j.compgeo.2013.07.015.

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33

Noilublao, Norapat, and Sujin Bureerat. "Simultaneous Topology, Shape, and Sizing Optimisation of Plane Trusses with Adaptive Ground Finite Elements Using MOEAs." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/838102.

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This paper proposes a novel integrated design strategy to accomplish simultaneous topology shape and sizing optimisation of a two-dimensional (2D) truss. An optimisation problem is posed to find a structural topology, shape, and element sizes of the truss such that two objective functions, mass and compliance, are minimised. Design constraints include stress, buckling, and compliance. The procedure for an adaptive ground elements approach is proposed and its encoding/decoding process is detailed. Two sets of design variables defining truss layout, shape, and element sizes at the same time are applied. A number of multiobjective evolutionary algorithms (MOEAs) are implemented to solve the design problem. Comparative performance based on a hypervolume indicator shows that multiobjective population-based incremental learning (PBIL) is the best performer. Optimising three design variable types simultaneously is more efficient and effective.

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Ullah, Baseer, Jon Trevelyan, and Siraj-ul-Islam. "A boundary element and level set based bi-directional evolutionary structural optimisation with a volume constraint." Engineering Analysis with Boundary Elements 80 (July 2017): 152–61. http://dx.doi.org/10.1016/j.enganabound.2017.02.012.

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35

Du, Xu Ran, Mike Xie, Xiao Ying Yang, and Zhi Hao Zuo. "Topology Optimisation of Composites Containing Base Materials of Distinct Poisson’s Ratios." Applied Mechanics and Materials 553 (May 2014): 813–17. http://dx.doi.org/10.4028/www.scientific.net/amm.553.813.

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From recent studies on natural composites such as nacre and bone, it has shown that the mechanical properties of the composite are significantly affected by the Poisson’s ratio of each constituent phase. In some cases it is found that when the Poisson’s ratio approaches the incompressibility limit, the stiffness of the composite in one or more directions can increase dramatically, in some cases by two or more orders of magnitude than the softer phase. In this paper we investigate designing the composite of maximum stiffness by a topology optimisation approach. The method used is based on the bi-directional evolutionary structural optimisation (BESO). The Optimisation problem is formulated and it is solved by a searching algorithm based on the sensitivity analysis. The effect of interpolation function in the sensitivity analysis is studied. Examples of different combinations of Poisson’s ratios are presented. The stiffness is found to increase from its base value. In the case of one phase having negative Poisson’s ratio, the increase is very significant. It is concluded that the proposed method is effective in optimising the stiffness of this class of composite.

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Novotný, Pavel, Martin Jonák, and Jiří Vacula. "Evolutionary Optimisation of the Thrust Bearing Considering Multiple Operating Conditions in Turbomachinery." International Journal of Mechanical Sciences 195 (April 2021): 106240. http://dx.doi.org/10.1016/j.ijmecsci.2020.106240.

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37

van Hemert, Jano I. "Evolving Combinatorial Problem Instances That Are Difficult to Solve." Evolutionary Computation 14, no. 4 (December 2006): 433–62. http://dx.doi.org/10.1162/evco.2006.14.4.433.

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This paper demonstrates how evolutionary computation can be used to acquire difficult to solve combinatorial problem instances. As a result of this technique, the corresponding algorithms used to solve these instances are stress-tested. The technique is applied in three important domains of combinatorial optimisation, binary constraint satisfaction, Boolean satisfiability, and the travelling salesman problem. The problem instances acquired through this technique are more difficult than the ones found in popular benchmarks. In this paper, these evolved instances are analysed with the aim to explain their difficulty in terms of structural properties, thereby exposing the weaknesses of corresponding algorithms.

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Zhu, Wenjin, Zhiming Chao, and Guotao Ma. "A Support Vector Machine Model with Hyperparameters Optimised by Mind Evolutionary Algorithm for Assessing Permeability of Rock." Advances in Civil Engineering 2020 (September 8, 2020): 1–12. http://dx.doi.org/10.1155/2020/4718493.

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In this paper, a database developed from the existing literature about permeability of rock was established. Based on the constructed database, a Support Vector Machine (SVM) model with hyperparameters optimised by Mind Evolutionary Algorithm (MEA) was proposed to predict the permeability of rock. Meanwhile, the Genetic Algorithm- (GA-) and Particle Swarm Algorithm- (PSO-) SVM models were constructed to compare the improving effects of MEA on the foretelling accuracy of machine learning models with those of GA and PSO, respectively. The following conclusions were drawn. MEA can increase the predictive accuracy of the constructed machine learning models remarkably in a few iteration times, which has better optimisation performance than that of GA and PSO. MEA-SVM has the best forecasting performance, followed by PSO-SVM, while the estimating precision of GA-SVM is lower than them. The proposed MEA-SVM model can accurately predict the permeability of rock indicating the model having a satisfactory generalization and extrapolation capacity.

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Gheraibia, Youcef, Abdelouahab Moussaoui, Youcef Djenouri, Sohag Kabir, Peng-Yeng Yin, and Smaine Mazouzi. "Penguin Search Optimisation Algorithm for Finding Optimal Spaced Seeds." International Journal of Software Science and Computational Intelligence 7, no. 2 (April 2015): 85–99. http://dx.doi.org/10.4018/ijssci.2015040105.

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This paper develops PeSeeD, a new metaheuristic algorithm for finding optimal spaced seed. Sequences matching is a hot topic in bio-informatics, which is used in many applications such as understanding the functional, structural, or evolutionary relationships between the sequences. The most relevant sequences matching methods are based on seeds designed to match two biological sequences. The first approach which introduced seeds was facilitated via Blastn tool, the approach builds seeds of 11 length size. However, it is clear that not all local alignments have to include an identical fragment of length 11. The spaced seeds approach is one of the methods which does not require a consecutive matching position. Dynamic programming is used to solve this kind of problem and it takes quadratic time. Several approaches have then been proposed to improve the sensitivity of searching in reasonable runtime. To reduce the complexity of such approaches, other heuristics based approaches can also be reviewed. The aim is to find spaced seeds subset which improves sensitivity without increasing the computation time. In this paper, the optimal subset spaced seeds are explored using the bio-inspired approach, penguins search optimisation algorithm (‘'PeSOA'' for short). The authors further propose an efficient heuristic for computing the overlap complexity between seeds. To evaluate the efficiency of the proposed approach, they compared the obtained results with the results of several seeds based software tools. The obtained results are very promising in terms of sensitivity and computation time for the overlap complexity.

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40

Arnold, Will, Ed Clark, and Giancarlo Torpiano. "Solar Gate, Hull – form through function." Structural Engineer 97, no. 1 (January 1, 2019): 20–29. http://dx.doi.org/10.56330/llun5649.

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The Solar Gate sculpture is one of a number of Arup-led interventions in Hull made possible through the city being awarded the title of UK City of Culture for 2017. The ethereal structure’s fabrication made use of local industry, allowing Hull’s industrial heritage and knowledge to reinvent itself to produce cutting-edge, contemporary art. This paper summarises the innovative design process that was followed by the team, testing methods in parametric design and evolutionary optimisation to take a design concept and refine it into an optimised structure. It describes how digital methods of working were used to facilitate a fast and efficient design dialogue between engineer and architect, leading to a design where the inherent beauty results from its engineering efficiency.

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Li, Shen, Andrea Coraddu, and Feargal Brennan. "A Framework for Optimal Sensor Placement to Support Structural Health Monitoring." Journal of Marine Science and Engineering 10, no. 12 (November 25, 2022): 1819. http://dx.doi.org/10.3390/jmse10121819.

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Offshore or drydock inspection performed by trained surveyors is required within the integrity management of an in-service marine structure to ensure safety and fitness for purpose. However, these physical inspection activities can lead to a considerable increase in lifecycle cost and significant downtime, and they can impose hazards for the surveyors. To this end, the use of a structural health monitoring (SHM) system could be an effective resolution. One of the key performance indicators of an SHM system is its ability to predict the structural response of unmonitored locations by using monitored data, i.e., an inverse prediction problem. This is highly relevant in practical engineering, since monitoring can only be performed at limited and discrete locations, and it is likely that structurally critical areas are inaccessible for the installation of sensors. An accurate inverse prediction can be achieved, ideally, via a dense sensor network such that more data can be provided. However, this is usually economically unfeasible due to budget limits. Hence, to improve the monitoring performance of an SHM system, an optimal sensor placement should be developed. This paper introduces a framework for optimising the sensor placement scheme to support SHM. The framework is demonstrated with an illustrative example to optimise the sensor placement of a cantilever steel plate. The inverse prediction problem is addressed by using a radial basis function approach, and the optimisation is carried out by means of an evolutionary algorithm. The results obtained from the demonstration support the proposal.

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Wang, ZhenZhou, and Adam Sobey. "A comparative review between Genetic Algorithm use in composite optimisation and the state-of-the-art in evolutionary computation." Composite Structures 233 (February 2020): 111739. http://dx.doi.org/10.1016/j.compstruct.2019.111739.

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Showkatbakhsh, Milad, and Mohammed Makki. "Multi-Objective Optimisation of Urban Form: A Framework for Selecting the Optimal Solution." Buildings 12, no. 9 (September 17, 2022): 1473. http://dx.doi.org/10.3390/buildings12091473.

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The complexity associated with the design of urban tissues is driven by the multitude of design goals that influence urban development and growth. This complexity is amplified by the design goals being inherently conflicting, necessitating preference-based decisions within the design process—an approach that results in predetermined design solutions driven by personal biases. The utility of population-based optimisation algorithms addresses this by allowing for the examination of multiple conflicting objectives within the same design problem, negating the need for trade-off decisions between the design goals. The application of these algorithms is associated with three primary steps. The first is the formulation of the design problem, the second is the application of the algorithm, and the third is selecting the most optimal solution from the algorithm’s output. This paper examines the third step in this process, in which various methods are employed to facilitate data-driven selection mechanisms that are both objective as well as subjective in their formulation. The selection mechanisms are demonstrated on a speculative urban tissue that examines the potential of inhabiting interstitial spaces, through various morphological interventions, within the urban fabric. The results present a scalable and adaptable framework that assists designers employing multi-objective evolutionary algorithms (MOEAs) to select the optimal solution from their generated populations, a challenge commonly associated with the application of MOEAs in design.

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Makropoulos, C. K., and D. Butler. "A multi-objective evolutionary programming approach to the ‘object location’ spatial analysis and optimisation problem within the urban water management domain." Civil Engineering and Environmental Systems 22, no. 2 (June 2005): 85–101. http://dx.doi.org/10.1080/10286600500126280.

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Balamurugan, C., A. Saravanan, P. Dinesh Babu, S. Jagan, and S. Ranga Narasimman. "Concurrent optimal allocation of geometric and process tolerances based on the present worth of quality loss using evolutionary optimisation techniques." Research in Engineering Design 28, no. 2 (May 28, 2016): 185–202. http://dx.doi.org/10.1007/s00163-016-0230-7.

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Larsson, Magnus, Alex Kaiser, and Ulf Arne Girhammar. "Conflict and Compromise in multi-storey timber architecture." Architectural Research Quarterly 19, no. 3 (September 2015): 283–94. http://dx.doi.org/10.1017/s1359135515000536.

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From the Stone Age to the Silicon Age, humanity’s relationship with materials has framed our scientific and cultural evolution. Despite recent scientific advances that open the laboratory doors to a future of material experimentation, the building industry remains stuck in the Concrete Age. The next material age is likely to be a Timber Age, as engineered timber finally comes into its own as a structural material suitable for multi-storey buildings.The research and design methodology of our practice can be summarised as an essay in conflict and compromise; a continual infliction of self-imposed constraints that allows us to discover hitherto unimaginable paths through the available options. We achieve this by searching through a space of possibilities demarcated by the properties and performative logic of the material at hand. First, we create an abundant field of alternatives, then we often use evolutionary computations to find our way through this field towards a visionary and original design.All design involves conflicting objectives. The best schemes offer the best compromise between desires. This article discusses how evolutionary solvers can be used as a tool for material-based architectural optimisation of geometries and structures, and how we have used them in designs for the imminent Timber Age. As one potentially conflicting objective is weighed against another, we move closer and closer to a tradeoff: the fitness of cooperating, opposing or unconnected variables.A population of possible design responses is visualised as a ‘fitness landscape’. Inspired by the role of natural selection in biological evolution, we use evolutionary algorithms to perform a selection process in which the ‘most fit’ members of such a population survive, while the ‘least fit’ members are eliminated, with the selection process guiding the algorithm towards ever-better solutions. The resulting timber structures would have made Darwin proud.

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Ashrafian, Ali, Naser Safaeian Hamzehkolaei, Ngakan Ketut Acwin Dwijendra, and Maziar Yazdani. "An Evolutionary Neuro-Fuzzy-Based Approach to Estimate the Compressive Strength of Eco-Friendly Concrete Containing Recycled Construction Wastes." Buildings 12, no. 8 (August 21, 2022): 1280. http://dx.doi.org/10.3390/buildings12081280.

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There has been a significant increase in construction and demolition (C&D) waste due to the growth of cities and the need for new construction, raising concerns about the impact on the environment of these wastes. By utilising recycled C&D waste, especially in concretes used in construction, further environmental damage can be prevented. By using these concretes, energy consumption and environmental impacts of concrete production can be reduced. The behaviour of these types of concrete in laboratories has been extensively studied, but reliable methods for estimating their behaviour based on the available data are required. Consequently, this research proposes a hybrid intelligent system, Fuzzy Group Method of Data Handling (GMDH)–Horse herd Optimisation Algorithm (HOA), for predicting one of the most important parameters in concrete structure design, compressive strength. In order to avoid uncertainty in the modelling process, crisp input values were converted to Fuzzy values (Fuzzification). Next, using Fuzzy input variables, the group method of data handling is used to predict the compressive strength of recycled aggregate concrete. The HOA algorithm is one of the newest metaheuristic algorithms being used to optimise the Fuzzy GMDH structure. Several databases containing experimental mix design records containing mixture components are gathered from published documents for compressive strength to assess the accuracy and reliability of the proposed hybrid Fuzzy-based model. Compared to other original approaches, the proposed Fuzzy GMDH model with the HOA optimiser outperformed them in terms of accuracy. A Monte Carlo simulation is also employed for uncertainty analysis of the empirical, standalone, and hybridised models in order to demonstrate that the evolutionary Fuzzy-based approach has less uncertainty than the standalone methods when simulating compressive strength.

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Wang, Chunguang, Krishnakumar Shankar, and Evgeny V. Morozov. "Tailored design of top-tensioned composite risers for deep-water applications using three different approaches." Advances in Mechanical Engineering 9, no. 1 (January 2017): 168781401668427. http://dx.doi.org/10.1177/1687814016684271.

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Risers with high-grade steel are widely used in offshore oil and gas industry at present. The extreme weight, lower fatigue and corrosion resistance of steel risers significantly limited the exploitation depths and the production capacity. Nowadays, it is acknowledged that using fibre-reinforced polymer composites to manufacture risers can be a better option. The prototypes of composite risers fabricated and tested confirm that fibre-reinforced polymer composites have an obvious advantage over steel risers on weight saving. Three different approaches are developed here to minimise composite risers’ weights: (1) enhancing the riser with only axial-direction and hoop-direction fibre; (2) off-axis reinforcements are included using an iterative approach of manual inspection and selection and (3) employing the optimisation technique of surrogate-assisted evolutionary algorithm. These design approaches have been applied to eight different material combinations to achieve the minimum structural weight by optimising their laminate configurations. The designs are conducted in accordance with the Standards, considering both local load cases and global – functional as well as environmental loads using ANSYS 15.0. The results show that comparing with steel risers, weight savings achieved by different design approaches and material combinations are different.

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Lee, Jongsoo, Seungjin Kim, and Shinill Kang. "Evolutionary fuzzy modelling in global approximate structual optimisation." International Journal of Vehicle Design 28, no. 4 (2002): 339. http://dx.doi.org/10.1504/ijvd.2002.001994.

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Arnaudo, María Eugenia, Néstor Toledo, Leopoldo Soibelzon, and Paula Bona. "Phylogenetic signal analysis in the basicranium of Ursidae (Carnivora, Mammalia)." PeerJ 7 (March 15, 2019): e6597. http://dx.doi.org/10.7717/peerj.6597.

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Ursidae is a monophyletic group comprised of three subfamilies: Tremarctinae, Ursinae and Ailuropodinae, all of which have a rich geographical distribution. The phylogenetic relationships within the Ursidae group have been underexamined, especially regarding morphological traits such as the basicranium. Importantly, the basicranium is a highly complex region that covers a small portion of the skull, combining both structural and functional aspects that determine its morphology. Phylogenetic hypotheses of the Ursidae (including Tremarctinae) have been made based on morphological characters that considers skull, mandible and teeth features, while specific characters of the auditory region and basicranium have not been taken into account. To do this, we analyse the shape and size macroevolution of the basicranium of Ursidae, testing its morphological disparity in a phylogenetic context, which is quantified by means of the phylogenetic signal. We investigated phylogenetical autocorrelation by shape (depicted by Principal Components Analysis scores from previous published analyses) and basicranium size (depicted by centroid size, CS) using an orthonormal decomposition analysis and Abouheif C mean. The main advantages of these methods are that they rely exclusively on cladogram topology and do not require branch-length estimates. Also, an optimisation of the ancestral nodes was performed using TNT 1.5 software. In relation to the phylogenetic signal, both methods showed similar results: the presence of autocorrelation was detected in PC1 and PC2, while in PC3, PC4 and PC5 and in the size of the basicranium (CS), the absence of autocorrelation occurred. The most significant nodes (where there is autocorrelation) are the basal nodes ‘Ursidae’ and ‘Ursinae-Tremarctinae’. Within this last group, distinctive basicranium morphology is observed, being more conservative in Tremarctinae than in Ursinae. The differences between these subfamilies could be related to historical events involving varying food and environmental preferences. The high phylogenetic signal in the node Tremarctinae probably indicates that the basicranium configuration of these bears was obtained early in their evolutionary history. Finally, our results of the basicranium and skull length ratios indicate that in Tremarctinae, the basicranium size was not determined by phylogeny but instead by other factors, such as adaptive responses to climatic changes and competition with other carnivores.

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