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Journal articles on the topic 'Structural Optimisation'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Vankan, W. J., R. Maas, and S. Grihon. "Efficient optimisation of large aircraft fuselage structures." Aeronautical Journal 118, no. 1199 (January 2014): 31–52. http://dx.doi.org/10.1017/s0001924000008915.

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Abstract This paper presents an innovative optimisation method for aircraft fuselage structural design. Detailed local finite element analyses of panel buckling are further processed such that they can be applied as failure constraints in the global level optimisation. The high computational costs involved with the finite element analyses are limited by advanced use of surrogate modelling methods. This yields high flexibility and efficiency in the local level optimisation procedure and allows for efficient gradient based search methods as well as more costly direct search optimisations like genetic algorithms (GAs). The method is demonstrated on a composite fuselage barrel design case considering common structural sizing variables like thicknesses and stringer dimensions. Optimised barrel designs are obtained where the constraints that are derived from the panel buckling analyses are active. The total computational cost for the complete local and global level optimisation procedures is in the order of days on common-performance hardware.

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2

De Wilde, W. P., T. Vandenbergh, and W. Debacker. "Structural optimisation and sustainable design." International Journal of Computational Methods and Experimental Measurements 3, no. 3 (September 30, 2015): 187–204. http://dx.doi.org/10.2495/cmem-v3-n3-187-204.

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3

W. Lim, J., and S. Sivaguru. "Chassis Structural Design of Track Racing One Manned Formula Car." International Journal of Engineering & Technology 7, no. 3.32 (August 26, 2018): 71. http://dx.doi.org/10.14419/ijet.v7i3.32.18396.

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The current work contains the design and optimisation of a spaceframe chassis for a track racing one manned formula car able to participate in the Formula Society of Automotive Engineers (Formula SAE) 2017/2018. Materials, profile cross section types were selected by considering the theories of elastic failure. The structural strength of the chassis was determined by Finite Element Analysis using ABAQUS software by determining the stress distribution during static and dynamic loading in addition to exposing the modal frequencies. Beam elements were used in the finite element model as it provides accurate modelling of small deflection bending responses. A simple baseline chassis design was developed that adheres to the Formula SAE 2017/2018 rules. Optimisations were made in terms of the configuration and material utilisation of the chassis members were done to prevent yielding during the static loading of car components and dynamic loading during acceleration and cornering. Furthermore, the same method of optimisation was used in prevention of the coincidence of natural frequency with the frequency of the engine.

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4

Tomašić, Dubravko, Dragan Peraković, and Marinko Jurčević. "Interdependence between Inventory Management and Employees’ Satisfaction." PROMET - Traffic&Transportation 25, no. 3 (June 19, 2013): 245–54. http://dx.doi.org/10.7307/ptt.v25i3.909.

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The study determines the correlation between the application of advanced models and methods of inventory optimisation in the supply chain in relation to the satisfaction of employees who are responsible for managing the inventory optimisation processes. The previous studies confirm that the optimisation of inventory management in the supply chain insures competitive advantages on the market. There is space for further research of impact of the achieved inventory optimisation in the supply chain on the change of the employees’ satisfaction. The paper establishes the interrelation of the interdependence of the achieved inventory optimisations on the satisfaction of the employees and the related synergy effects of acquiring added value of the companies on the market oriented to the satisfaction of the buyers and service users. The research has defined new knowledge in interdependence of inventory management optimisation on the change of indicators of employees’ satisfaction. Based on the performed research an assumption has been created for the design of an application package (so-called XaaS-based services) for the management of interaction processes of inventory optimization in the supply chain, satisfaction of service users and employees.

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5

Ribeiro, Tiago P., Luís F. A. Bernardo, and Jorge M. A. Andrade. "Topology Optimisation in Structural Steel Design for Additive Manufacturing." Applied Sciences 11, no. 5 (February 27, 2021): 2112. http://dx.doi.org/10.3390/app11052112.

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Topology Optimisation is a broad concept deemed to encapsulate different processes for computationally determining structural materials optimal layouts. Among such techniques, Discrete Optimisation has a consistent record in Civil and Structural Engineering. In contrast, the Optimisation of Continua recently emerged as a critical asset for fostering the employment of Additive Manufacturing, as one can observe in several other industrial fields. With the purpose of filling the need for a systematic review both on the Topology Optimisation recent applications in structural steel design and on its emerging advances that can be brought from other industrial fields, this article critically analyses scientific publications from the year 2015 to 2020. Over six hundred documents, including Research, Review and Conference articles, added to Research Projects and Patents, attained from different sources were found significant after eligibility verifications and therefore, herein depicted. The discussion focused on Topology Optimisation recent approaches, methods, and fields of application and deepened the analysis of structural steel design and design for Additive Manufacturing. Significant findings can be found in summarising the state-of-the-art in profuse tables, identifying the recent developments and research trends, as well as discussing the path for disseminating Topology Optimisation in steel construction.

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6

Phillips, Andrew T. M. "Structural optimisation: biomechanics of the femur." Proceedings of the Institution of Civil Engineers - Engineering and Computational Mechanics 165, no. 2 (June 2012): 147–54. http://dx.doi.org/10.1680/eacm.10.00032.

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7

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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8

Jones, R., P. Chaperon, and M. Heller. "Structural optimisation with fracture strength constraints." Engineering Fracture Mechanics 69, no. 13 (September 2002): 1403–23. http://dx.doi.org/10.1016/s0013-7944(02)00006-1.

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9

Jones, R., D. Peng, P. Chaperon, S. Pitt, D. Abramson, and T. Peachey. "Structural optimisation with damage tolerance constraints." Theoretical and Applied Fracture Mechanics 43, no. 1 (March 2005): 133–55. http://dx.doi.org/10.1016/j.tafmec.2004.12.009.

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10

Suraweera, NP, and DN Ranasinghe. "Adaptive Structural Optimisation of Neural Networks." International Journal on Advances in ICT for Emerging Regions (ICTer) 1, no. 1 (March 26, 2009): 33. http://dx.doi.org/10.4038/icter.v1i1.450.

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11

Fredricson, Harald. "Structural topology optimisation: an application review." International Journal of Vehicle Design 37, no. 1 (2005): 67. http://dx.doi.org/10.1504/ijvd.2005.006089.

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12

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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13

Jiang, Wei, Weicheng Xie, and Shuai Sun. "Parametric Optimisation Analysis of Micro/Nano-Satellite Flywheels Based on the NSGA-Ⅱ Optimisation Algorithm." Aerospace 9, no. 7 (July 18, 2022): 386. http://dx.doi.org/10.3390/aerospace9070386.

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As the key mechanism of attitude control of micro/nano-satellites, the flywheel design is mostly based on empirical formulae that do not meet the requirements of lightweight and high-performance micro/nano-satellite platforms. In this paper, the structural shape of micro/nano-satellite flywheels is analysed, and a set of flywheel optimisation methods is proposed to realise the parametric optimisation analysis of the structural shape. First, the general principle of flywheel efficiency is introduced, the optimisation evaluation factor of flywheel design is proposed, and the parametric model of a flywheel structure is established by using the finite element secondary development technology, which can be used to quickly build a finite element model of different dimensions. Second, the optimisation model of flywheels is established while introducing the approximate model algorithm, greatly improving the optimisation efficiency. Considering the phenomenon that the genetic algorithm falls below local optimisation under a large parameter range, the method of initial optimisation is proposed to reduce the upper and lower limits of the optimisation parameters. Finally, the optimal shape of the flywheel is obtained by using the parametric optimisation model of the flywheel. The finite element analysis results show that the flywheel optimisation evaluation factor proposed in this work can effectively improve the comprehensive performance of the flywheel as the optimisation target, and the corresponding optimisation method can be well applied to the engineering application and design of micro/nano-satellite platforms. This can help guide the structural optimisation design of micro/nano-satellite platforms in the future.

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14

Ket Thein, Chung, and Jing‐Sheng Liu. "Effective structural sizing/shape optimisation through a reliability‐related multifactor optimisation approach." Multidiscipline Modeling in Materials and Structures 8, no. 2 (August 10, 2012): 159–77. http://dx.doi.org/10.1108/15736101211251194.

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15

Blumer, Hermann, and Renü Wölfl. "Optimisation of Timber Domes as Structural Forms." Structural Engineering International 10, no. 3 (August 2000): 185–88. http://dx.doi.org/10.2749/101686600780481491.

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16

Lewis, W. J. "Form-Finding: An Alternative to Structural Optimisation?" Computational Technology Reviews 11 (September 1, 2015): 121–49. http://dx.doi.org/10.4203/ctr.11.5.

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17

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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18

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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19

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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20

Coelho, Pedro G., Luís O. Faria, and João B. Cardoso. "Structural analysis and optimisation of press brakes." International Journal of Machine Tools and Manufacture 45, no. 12-13 (October 2005): 1451–60. http://dx.doi.org/10.1016/j.ijmachtools.2005.01.030.

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21

Twu, Sung-Ling, Maolin Tsai, Ramaswamy Subramanian, Robert V. Lust, and M. E. M. El-Sayed. "Structural optimisation with reliability-based durability constraints." International Journal of Vehicle Design 25, no. 1/2 (2001): 115. http://dx.doi.org/10.1504/ijvd.2001.001911.

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22

Yıldız, Betül Sultan, and Hüseyin Lekesiz. "Fatigue-based structural optimisation of vehicle components." International Journal of Vehicle Design 73, no. 1/2/3 (2017): 54. http://dx.doi.org/10.1504/ijvd.2017.082579.

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23

Lekesiz, Hüseyin, and Betül Sultan Yıldız. "Fatigue-based structural optimisation of vehicle components." International Journal of Vehicle Design 73, no. 1/2/3 (2017): 54. http://dx.doi.org/10.1504/ijvd.2017.10003398.

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24

Marti, K. "Stochastic structural optimisation with quadratic loss functions." Computers & Structures 88, no. 23-24 (December 2010): 1310–21. http://dx.doi.org/10.1016/j.compstruc.2008.12.010.

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25

Gavali, S. L., Y. P. Reddy, and K. N. Vijayakumar. "A genetic algorithm-based structural topology optimisation." International Journal of Design Engineering 11, no. 1 (2022): 27. http://dx.doi.org/10.1504/ijde.2022.127072.

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26

Koumar, Aushim, Tine Tysmans, Rajan Filomeno Coelho, and Niels De Temmerman. "An Automated Structural Optimisation Methodology for Scissor Structures Using a Genetic Algorithm." Applied Computational Intelligence and Soft Computing 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/6843574.

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We developed a fully automated multiobjective optimisation framework using genetic algorithms to generate a range of optimal barrel vault scissor structures. Compared to other optimisation methods, genetic algorithms are more robust and efficient when dealing with multiobjective optimisation problems and provide a better view of the search space while reducing the chance to be stuck in a local minimum. The novelty of this work is the application and validation (using metrics) of genetic algorithms for the shape and size optimisation of scissor structures, which has not been done so far for two objectives. We tested the feasibility and capacity of the methodology by optimising a 6 m span barrel vault to weight and compactness and by obtaining optimal solutions in an efficient way using NSGA-II. This paper presents the framework and the results of the case study. The in-depth analysis of the influence of the optimisation variables on the results yields new insights which can help in making choices with regard to the design variables, the constraints, and the number of individuals and generations in order to obtain efficiently a trade-off of optimal solutions.

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27

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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28

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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29

Lorimer, Tobias, and Tom Allen. "Concurrent Multi-Component Optimization of Stiffened-Plate Yacht Structures." Journal of Sailing Technology 7, no. 01 (December 16, 2022): 203–27. http://dx.doi.org/10.5957/jst/2022.7.10.203.

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Optimisation has become a necessary tool when designing competitive high-performance composite yachts. The inherently complex material characteristics of composite materials mean there must be careful consideration of laminate and structural design to withstand loading and meet efficiency demands. An optimisation scheme has been developed that allows structural engineers to generate globally optimised yacht structures. This paper investigates a more holistic approach to designing large multi-part composite structures using concurrent genetic algorithm optimisation tools, which will allow designers to understand the effect of optimisation on the decision-making and design processes. Specifically, how can better design decisions be influenced by incorporating tools that augment an experientially based complex composite structural design process? The tool developed concurrently optimises laminate architecture and the arrangement of and number of stiffeners that make up a stiffened-plate geometry such as that seen in a typical yacht structure.

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30

Chen, Hao, Chihua Lu, Zhien Liu, Cunrui Shen, and Menglei Sun. "Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights." Materials 15, no. 15 (August 3, 2022): 5339. http://dx.doi.org/10.3390/ma15155339.

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Tail-welded blanks (TWBs) are widely used in automotive bodies to improve the structural performance and reduce weight. The stiffness and modal lightweight design optimisation of TWBs for automotive doors was performed in this study. The finite element model was validated through physical experiments. An L27 (312) Taguchi orthogonal array was used to collect the sample points. The multi-objective optimisation problem was transformed into a single-objective optimisation problem based on the grey relational degree. The optimal combination of structural design parameters was obtained for a tail-welded door using the proposed method, and the weight of the door structure was reduced by 2.83 kg. The proposed optimisation method has fewer iterations and a lower computational cost, enabling the design of lightweight TWBs.

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31

MERKURYEVA, Galina, and Vitaly BOLSHAKOV. "SIMULATION-BASED FITNESS LANDSCAPE ANALYSIS AND OPTIMISATION OF COMPLEX PROBLEMS." Technological and Economic Development of Economy 21, no. 6 (November 23, 2015): 899–916. http://dx.doi.org/10.3846/20294913.2015.1107654.

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Widespread hard optimisation problems in economics and logistics are characterised by large dimensions, uncertainty and nonlinearity and require more powerful methods of stochastic optimisation that traditional ones. Simulation optimisation is a powerful tool for solving these problems. Moreover, fitness landscape analysis techniques provide an efficient approach to better selection of a suitable optimisation algorithm. The concept and techniques of fitness landscape analysis are described. A formalised scheme for simulation optimisation enhanced with fitness landscape analysis is given. Benchmark fitness landscape analysis is performed to find relations between efficiency of an optimisation algorithm and structural features of a fitness landscape. Case study in simulation optimisation of vehicle routing and scheduling is described. Various optimisation scenarios with application of the fitness landscape analysis are discussed and investigated.

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32

Grekavicius, Lukas, Jack Antony Hughes, Konstantinos Daniel Tsavdaridis, and Evangelos Efthymiou. "Novel Morphologies of Aluminium Cross-Sections through Structural Topology Optimization Techniques." Key Engineering Materials 710 (September 2016): 321–26. http://dx.doi.org/10.4028/www.scientific.net/kem.710.321.

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In the last decades, the deployment of aluminium and its alloys in civil engineering fields has been increased significantly, due to the material’s special features accompanied by supportive technological and industrial development. However, the extent of aluminium structural applications in building activities is still rather limited and barriers related to strength and stability issues prevent its wider use. In the context of the extrusion characteristic, appropriate design in aluminium cross-sections can overcome inherent deficiencies, such as the material’s low elastic modulus.This paper investigates a new breed of cross-sectional design for aluminium members employing pioneering structural topology optimisation techniques. Topology optimisation problems utilise the firmest mathematical basis, to account for improved weight-to-stiffness ratio and perceived aesthetic appeal of specific structural forms. The current study investigates the application of structural topology optimisation to the design of aluminium beam and column cross-sections. Through a combination of 2D and 3D approaches, with a focus on post-processing and manufacturability, ten unique cross-sectional profiles are proposed. Additionally, the variation of cross-section along the member is also investigated in order to identify correlation between 2D and 3D topology optimisation results. Conclusions attempt to highlight the advantageous characteristics of aluminium use as well as the potential benefits to the more widespread implementation of topology optimization within the utilization of aluminium in civil/structural engineering.

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33

Cho, S. H., C. Bil, and R. Adams. "Design and optimisation of the external load bearing carry through structure of a columned multi bubble fuselage." Aeronautical Journal 117, no. 1187 (January 2013): 97–108. http://dx.doi.org/10.1017/s0001924000007879.

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Abstract The blended wing-body configuration holds a major structural design challenge at the centre-body where the structure must support both wing bending loads and internal cabin pressure. A membrane approach is proposed which decouples the loads to allow their resistance by two independent structures: an inner membrane for cabin pressure and an outer structure to resist wing loads. A columned multi-bubble fuselage is proposed for the inner membrane structure, which are multispherical configuration to efficiently withstand the pressure loads. Considering this configuration, the carry-through structure can be designed and optimised. Finite element results show a significant reduction of stress level in this design over that for a conventional multi-bubble fuselage. Up to 30% weight reduction is achieved for a military cargo application that requires an extensive area with no structural interruption. For the outer carry-through structure, the topology and shape optimisations of finite element models were performed on the given design domain. The results from the shape and topology optimisations were complementary demonstrating a consistent design approach. The optimisation theory is briefly presented along with the results.

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34

Duarte, Grasiele Regina, Afonso Celso de Castro Lemonge, and Leonardo Goliatt da Fonseca. "An algorithm inspired by social spiders for truss optimisation problems." Engineering Computations 34, no. 8 (November 6, 2017): 2767–92. http://dx.doi.org/10.1108/ec-12-2016-0447.

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Purpose The purpose of this paper is to evaluate the performance of social spider algorithm (SSA) to solve constrained structural optimisation problems and to compare its results with others algorithms such as genetic algorithm, particle swarm optimisation, differential evolution and artificial bee colony. Design/methodology/approach To handle the constraints of the problems, this paper couples to the SSA an efficient selection criteria proposed in the literature that promotes a tournament between two solutions in which the feasible or less infeasible solution wins. The discussion is conducted on the competitiveness of the SSA with other algorithms as well as its performance in constrained problems. Findings SSA is a population algorithm proposed for global optimisation inspired by the foraging of social spiders. A spider moves on the web towards the position of the prey, guided by vibrations that occur around it in different frequencies. The SSA was proposed to solve problems without constraints, but these are present in most of practical problems. This paper evaluates the performance of SSA to solve constrained structural optimisation problems and compares its results with other algorithms such as genetic algorithm, particle swarm optimisation, differential evolution and artificial bee colony. Research limitations/implications The proposed algorithm has no limitations, and it can be applied in other classes of constrained optimisation problems. Practical implications This paper evaluated the proposed algorithm with a benchmark of constrained structural optimisation problems intensely used in the literature, but it can be applied to solve real constrained optimisation problems in engineering and others areas. Originality/value This is the first paper to evaluate the performance of SSA in constrained problems and to compare its results with other algorithms traditional in the literature.

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35

Hill, Raymond R., and Edward A. Pohl. "A structural taxonomy for metaheuristic optimisation search methods." International Journal of Metaheuristics 7, no. 2 (2019): 127. http://dx.doi.org/10.1504/ijmheur.2019.098261.

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Hill, Raymond R., and Edward A. Pohl. "A structural taxonomy for metaheuristic optimisation search methods." International Journal of Metaheuristics 7, no. 2 (2019): 127. http://dx.doi.org/10.1504/ijmheur.2019.10019607.

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37

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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38

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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39

Groumpos, P. P. "Structural modelling and optimisation of large scale systems." IEE Proceedings - Control Theory and Applications 141, no. 1 (January 1, 1994): 1–11. http://dx.doi.org/10.1049/ip-cta:19949441.

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40

Rayamajhi, Milan, Stephan Hunkeler, and Fabian Duddeck. "Geometrical compatibility in structural shape optimisation for crashworthiness." International Journal of Crashworthiness 19, no. 1 (September 9, 2013): 42–56. http://dx.doi.org/10.1080/13588265.2013.832720.

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41

Overton, I. M., C. A. J. van Niekerk, L. G. Carter, A. Dawson, D. M. A. Martin, S. Cameron, S. A. McMahon, et al. "TarO: a target optimisation system for structural biology." Nucleic Acids Research 36, Web Server (May 19, 2008): W190—W196. http://dx.doi.org/10.1093/nar/gkn141.

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42

Fragiadakis, Michalis, Nikos D. Lagaros, and Manolis Papadrakakis. "Performance-based earthquake engineering using structural optimisation tools." International Journal of Reliability and Safety 1, no. 1/2 (2006): 59. http://dx.doi.org/10.1504/ijrs.2006.010690.

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43

Husselmann, Alwyn V. "Data-parallel structural optimisation in agent-based models." ACM SIGEVOlution 7, no. 2-3 (August 17, 2015): 33–36. http://dx.doi.org/10.1145/2815474.2815481.

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44

Youn, Byeng D., Kyung K. Choi, and Jun Tang. "Structural durability design optimisation and its reliability assessment." International Journal of Product Development 1, no. 3/4 (2005): 383. http://dx.doi.org/10.1504/ijpd.2005.005948.

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45

Chen, Mohan, Dongqing Wu, Wanglin Chen, and Shihong Zhang. "Structural optimisation and electrochemical behaviour of AlCrN coatings." Thin Solid Films 612 (August 2016): 400–406. http://dx.doi.org/10.1016/j.tsf.2016.06.032.

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46

Yin, Ge, Xiao Xiao, and Fehmi Cirak. "Topologically robust CAD model generation for structural optimisation." Computer Methods in Applied Mechanics and Engineering 369 (September 2020): 113102. http://dx.doi.org/10.1016/j.cma.2020.113102.

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47

Al-Tamimi, Abdulsalam Abdulaziz, Henrique Almeida, and Paulo Bartolo. "Structural optimisation for medical implants through additive manufacturing." Progress in Additive Manufacturing 5, no. 2 (February 24, 2020): 95–110. http://dx.doi.org/10.1007/s40964-020-00109-7.

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Boettger, J. C., and S. B. Trickey. "Structural optimisation and properties of first row monolayers." Journal of Physics F: Metal Physics 16, no. 6 (June 1986): 693–706. http://dx.doi.org/10.1088/0305-4608/16/6/006.

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Kim, Hyunsun A., Paul J. Clement, and James L. Cunningham. "Investigation of cancellous bone architecture using structural optimisation." Journal of Biomechanics 41, no. 3 (2008): 629–35. http://dx.doi.org/10.1016/j.jbiomech.2007.09.036.

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Tonicello, Eric, Olivier Vassart, Riccardo Zanon, and Jean-Marc Franssen. "Structural Fire Design and Optimisation of a Building." Structural Engineering International 22, no. 4 (November 2012): 541–44. http://dx.doi.org/10.2749/101686612x13363929517776.

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