A hybrid symmetry–PSO approach to finding the self-equilibrium configurations of prestressable pin-jointed assemblies

Chen, Yao; Yan, Jerry; Feng, Jian; Sareh, Pooya

doi:10.1007/s00707-019-02586-6

Cited by 38 publications

(7 citation statements)

References 51 publications

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“…erefore, the mechanical performance analysis and bearing capacity design of the bridge prestressed under fatigue load have become an important index to judge the advanced level of bridge construction field, and there have been studies on the application of neural network algorithms to the determination of prestress loss [2]. On the other hand, how to combine intelligent algorithm and bearing capacity technology to establish a modern and efficient measurement system of prestress loss under fatigue load has become an important development direction [3]. Up to now, a lot of data have been produced in the comprehensive performance analysis and bearing capacity test of the bridge prestress under fatigue load [4].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Determination of Bridge Prestress Loss under Fatigue Load Based on PSO‐BP Neural Network

Wang

2021

Computational Intelligence and Neuroscience

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During the service period of a prestressed concrete bridge, as the number of cyclic loads increases, cumulative fatigue damage and prestress loss will occur inside the structure, which will affect the safety, durability, and service life of the structure. Based on this, this paper studies the loss of bridge prestress under fatigue load. First, the relationship between the prestress loss of the prestressed tendons and the residual deflection of the test beam is analyzed. Based on the test results and the main influencing factors of fatigue and creep, a concrete fatigue and creep calculation model is proposed; then, based on the static cracking check calculation method and POS-BP neural network algorithm, a prestressed concrete beam fatigue cracking check model under repeated loads is proposed. Finally, the mechanical performance of the prestressed concrete beam after fatigue loading is analyzed, and the influence of the fatigue load on the bearing capacity of the prestressed concrete beam is explored. The results show that the bridge prestress loss characterization model based on the POS-BP neural network algorithm has the advantages of high calculation efficiency and strong applicability.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Determination of Bridge Prestress Loss under Fatigue Load Based on PSO‐BP Neural Network

Wang

2021

Computational Intelligence and Neuroscience

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“…In terms of natural optimization methods for form-finding of tensegrities, Koohestani (2012), Lee and Lee (2014), SthenlyGan (2015), and Uzun (2017) optimized the special objective functions via the genetic algorithm. Chen et al (2012) used ant colony optimization to find the self-equilibrium shapes of tensegrities, while Chen et al (2020) suggested the hybrid symmetry particle swarm optimization for form-finding problems of pin-jointed systems included tensegrities. Moreover, the stochastic numerical method of Monte Carlo was used by Li et al (2010) in form-finding problems of tensegrities, the most recent research utilized linear matrix inequalities (LMIs) formulation to find the self-equilibrium shape of tensegrity structures was carried out by Yafeng (2021).…”

Section: Introductionmentioning

confidence: 99%

A combination of genetic algorithm and dynamic relaxation method for practical form-finding of tensegrity structures

Bui

Kawabata

Chau

2022

Advances in Structural Engineering

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A combination of Genetic algorithm (GA) and Dynamic Relaxation (DR) method is presented for practical form-finding of tensegrity structures. Several approaches of dynamic relaxation method are reviewed for form-finding of tensegrity structures, and the negative points of DR method where the final form is sensitive to the initial form and cannot satisfy the geometrical constraint conditions are illustrated. The genetic algorithm is then proposed to obtain the initial configuration for DR method for finding the final shapes satisfying all constraint conditions. Several types of tensegrity structures are performed to point out the efficiency of proposed combination method.

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“…It is acknowledged that lightweight and tunable stiffness are the two basic characteristics of tensegrity structures due to the fact that prestress stiffens the structure and all components are axially loaded. Accordingly, since the concept of "tensegrity" arose in the early 1950s, it has received vast interest among engineers and scientists throughout domains such as aerospace [1], biology [2][3][4], mathematics [5], architecture [6], robotics [7][8][9], and civil engineering [10][11][12][13][14] to materials [15].…”

Section: Introductionmentioning

confidence: 99%

“…e study in [31] investigated the optimal prestress design of tensegrity grid structures by introducing the Interior-Point Method into the formfinding process. e study in [14] determined the feasible prestress modes for cable-strut structures on the basis of particle swarm algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimal Prestress Investigation on Tensegrity Structures Using Artificial Fish Swarm Algorithm

Feng

Zhang

Luo

et al. 2020

Advances in Civil Engineering

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To obtain the optimal uniform prestress of a tensegrity structure with geometric configuration given, a novel method is developed for prestress design of tensegrity structures by utilizing the artificial fish swarm algorithm (AFSA). In the beginning, the form-finding process is implemented by solving a linear homogeneous system concerning the self-equilibrium system. The issue is subsequently performed as a minimum problem by regulating the value of an objective function where the unilateral condition and the stress uniformity condition are entirely considered. The AFSA is adopted to search for the global minimum, leading to a set of initial prestresses that guarantee all the above conditions. Two illustrative examples have been fully studied to prove the accuracy and efficiency of the presented approach in prestress design of tensegrities according to the practical requirements. Furthermore, the numerical examples investigated in this paper confirm that the AFSA has explicit advantages of rapid convergence and overcoming the local minima.

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A hybrid symmetry–PSO approach to finding the self-equilibrium configurations of prestressable pin-jointed assemblies

Cited by 38 publications

References 51 publications

[Retracted] Determination of Bridge Prestress Loss under Fatigue Load Based on PSO‐BP Neural Network

[Retracted] Determination of Bridge Prestress Loss under Fatigue Load Based on PSO‐BP Neural Network

A combination of genetic algorithm and dynamic relaxation method for practical form-finding of tensegrity structures

Optimal Prestress Investigation on Tensegrity Structures Using Artificial Fish Swarm Algorithm

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