Form-finding of free-form tensegrity structures by genetic algorithm–based total potential energy minimization

2016 ◽  
Vol 20 (5) ◽  
pp. 784-796 ◽  
Author(s):  
Fatih Uzun
Keyword(s):  
Genetic Algorithm ◽  
Potential Energy ◽  
Free Form ◽  
Stable Form ◽  
Equilibrium Form ◽  
Form Finding ◽  
Tensegrity Structures ◽  
Computational Performance ◽  
Stability Of Structures

Free-form tensegrities are composed of randomly connected cable and strut elements. The complexity of these structures causes determination of their self-equilibrium form to be a formidable task. There can be an infinite number of solutions with different forms, but it is difficult to identify the best form in terms of stability. Based on the fact that stability of structures is inversely proportional to potential energy, a genetic algorithm minimization process is developed to determine the self-equilibrium form of free-form regular tensegrity structures. The capability of the form-finding process on determination of the most stable form with minimum potential energy is investigated using two main parameters of free-form regular tensegrities which are cable–strut length ratio at rest and number of strut elements. The computational performance of the proposed method is also tested using free-form tensegrities with different number of structural elements.

Novel Form-Finding of Tensegrity Structures Using Ant Colony Systems

2011 ◽  
Author(s):  
Yao Chen ◽  
Jian Feng ◽  
Yongfen Wu
Keyword(s):  
Travelling Salesman Problem ◽  
Ant Colony ◽  
Ant Colony System ◽  
Form Finding ◽  
Tensegrity Structures ◽  
Connectivity Patterns ◽  
Feasible Solutions ◽  
Ant Colony Systems ◽  
The Given

Tensegrity structures are drawing the attention of architects and engineers due to their remarkable configurations. They have inextensional mechanisms, yet they are stable. The determination of connectivity patterns of the compression bars and tension cables is a key to design tensegrity structures. In this paper, a discrete optimization model for the form-finding of tensegrity structures was developed, and converted into a modified travelling salesman problem (TSP). The ant colony system (ACS) was used to search for feasible solutions, where all the given nodes were taken as different cities in the network. To obtain optimized shapes of tensegrity structures with stable equilibriums and adequate stiffness, an objective function was introduced. Examples based on the geometries of some polyhedra were carried out using the proposed technique. Many different configurations of the assemblies which consist of cables and bars are transformed into interesting tensegrity structures. It concludes that this novel algorithm could be applicable to the form-finding of both regular and nonregular tensegrity structures.

A Genetic Algorithm Approach for 2-D Tensegrity Form Finding

2014 ◽  
Vol 17 (11) ◽  
pp. 1669-1679 ◽  
Author(s):  
Shirko Faroughi ◽  
Mehdi Abdollahi Kamran ◽  
Jaehong Lee
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Stiffness Matrix ◽  
Equilibrium Configuration ◽  
Positive Definite ◽  
Connectivity Matrix ◽  
Form Finding ◽  
Numerical Examples ◽  
Tensegrity Structures ◽  
Genetic Algorithm Approach

This paper presents a novel and versatile method for finding 2-D tensegrity structures form finding. Using this method, different possibilities for the geometry of 2-D tensegrity structures can be found with little information about the structure. As opposed to most existing procedures this method only needs the number of each member prototype, the number of tensegrity nodes and connectivity at each node to be known. The form finding is done by minimizing objective function, which considers the rank deficiencies of the geometry, the prestress coefficients and the semi-positive definite condition of the stiffness matrix. Genetic algorithm as the global search is taken into account first for generating the connectivity matrix, initial prestress coefficients and also minimizing the objective function. Several numerical examples are given to demonstrate the competence and robustness of the current study in searching new different possibility self-equilibrium configuration of tensegrity structures.

Form-finding and analysis of an alternative tensegrity dome configuration

2017 ◽  
Vol 20 (11) ◽  
pp. 1644-1657 ◽  
Author(s):  
Fatih Uzun
Keyword(s):  
Potential Energy ◽  
Energy Minimization ◽  
Minimum Potential Energy ◽  
Form Finding ◽  
Tensegrity Structures ◽  
Minimum Potential ◽  
Local Impact ◽  
Cable Dome ◽  
Minimization Approach ◽  
Cable Domes

Geiger domes are composed of cable and strut elements. This property of cable domes is the same as tensegrity structures, but in contraction to tensegrity structures, strut elements do not have a function that balances tension in cable elements with compression. In this study, a new cable dome configuration, that mimics the form of tensegrities, is proposed which is able to spread effect of an applied load into all elements of the dome and reduces its local impact. Form-finding and analysis of the Geiger and new dome configurations are performed based on the principle of minimum potential energy. Self-equilibrium forms with minimum potential energy are determined using genetic algorithms. The ability of genetic algorithm based potential energy minimization approach to perform form-finding of loaded or load free cable domes is investigated. Performance of the proposed configuration is tested and compared with the Geiger configuration under various loading conditions.

Determination of a unique configuration of free-form tensegrity structures

2011 ◽  
Vol 220 (1-4) ◽  
pp. 331-348 ◽  
Author(s):  
Hoang Chi Tran ◽  
Jaehong Lee
Keyword(s):  
Free Form ◽  
Tensegrity Structures

FIDDLE. Simultaneous Indexing and Structure Solution from Powder Diffraction Data using a Genetic Algorithm and Correlation Functions

2019 ◽  
Author(s):  
Carmen Guguta ◽  
Jan M.M. Smits ◽  
Rene de Gelder
Keyword(s):  
Genetic Algorithm ◽  
Powder Diffraction ◽  
Diffraction Data ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Simultaneous Optimization ◽  
Powder Diffraction Data ◽  
Structure Solution ◽  
Matching Technique

A method for the determination of crystal structures from powder diffraction data is presented that circumvents the difficulties associated with separate indexing. For the simultaneous optimization of the parameters that describe a crystal structure a genetic algorithm is used together with a pattern matching technique based on auto and cross correlation functions.<br>

scholarly journals Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form

2021 ◽  
Vol 19 (1) ◽  
pp. 205-213
Author(s):  
Hany W. Darwish ◽  
Abdulrahman A. Al Majed ◽  
Ibrahim A. Al-Suwaidan ◽  
Ibrahim A. Darwish ◽  
Ahmed H. Bakheit ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simultaneous Determination ◽  
Predictive Power ◽  
Principal Component Regression ◽  
Selection Procedure ◽  
Principal Component ◽  
Chemometric Methods ◽  
Full Spectrum ◽  
Azilsartan Medoxomil

Abstract Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted.

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