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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 (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 (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 (2000): 291–308. http://dx.doi.org/10.1016/s0168-874x(99)00044-x.

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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 (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 (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 (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 (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 (2000): 743–54. http://dx.doi.org/10.1016/s0045-7825(99)00359-x.

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10

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 pres

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11

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

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12

Bureerat, Sujin, and Jumlong Limtragool. "Performance enhancement of evolutionary search for structural topology optimisation." Finite Elements in Analysis and Design 42, no. 6 (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 (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 (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 sh

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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 (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

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17

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

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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 (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,

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19

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 (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 th

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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 (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 significan

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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 (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 in

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24

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

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25

Cervera, E., and J. Trevelyan. "Evolutionary structural optimisation based on boundary representation of NURBS. Part II: 3D algorithms." Computers & Structures 83, no. 23-24 (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 (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 (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

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28

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 (2005): 29–38. http://dx.doi.org/10.1016/j.advengsoft.2003.10.008.

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29

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 (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 apply

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30

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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31

Rajagopal, S., and R. Ganguli. "Conceptual design of UAV using Kriging based multi-objective genetic algorithm." Aeronautical Journal 112, no. 1137 (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

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32

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

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34

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 b

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36

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 (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 exp

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38

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 pr

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39

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 (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

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40

Arnold, Will, Ed Clark, and Giancarlo Torpiano. "Solar Gate, Hull – form through function." Structural Engineer 97, no. 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 work

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41

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 (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 moni

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42

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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43

Showkatbakhsh, Milad, and Mohammed Makki. "Multi-Objective Optimisation of Urban Form: A Framework for Selecting the Optimal Solution." Buildings 12, no. 9 (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 t

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44

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 (2005): 85–101. http://dx.doi.org/10.1080/10286600500126280.

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45

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 (2016): 185–202. http://dx.doi.org/10.1007/s00163-016-0230-7.

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46

Larsson, Magnus, Alex Kaiser, and Ulf Arne Girhammar. "Conflict and Compromise in multi-storey timber architecture." Architectural Research Quarterly 19, no. 3 (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-impose

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47

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 (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 bas

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48

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 (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 ri

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49

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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50

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

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