Damage detection of truss structures by hybrid immune system and teaching–learning-based optimization

Shahrouzi, Mohsen; Sabzi, Amir-Hossein

doi:10.1007/s42107-018-0065-9

Cited by 20 publications

(8 citation statements)

References 28 publications

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“…Recently, the TLBO method has been hybridized with other optimization algorithms to address CHPED problems and improve overall efficiency [31]. For instance, TLBO algorithm incorporated with a genetic algorithm, particle swarm optimization, Gaussian random variables, opposition-based learning, adaptive grasshopper optimization, and immune system algorithm are presented in [32][33][34]. In this study, we have hybridized a new version of TLBO (which is inspired by enthusiasm learning mechanism) with particle swarm optimization (PSO) because of its noticeable performance and efficiency for finding optimal solutions in various fields of engineering [35].…”

Section: Motivations and Literature Reviewmentioning

confidence: 99%

A sequential hybridization of ETLBO and IPSO for solving reserve‐constrained combined heat, power and economic dispatch problem

Goudarzi

Fahad

et al. 2022

IET Generation Trans & Dist

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The explosive demand for electricity and ecological concerns has necessitated the operation of power networks in a more cost‐effective approach. In recent years, the integration of combined heat and power units has presented a potential answer to these problems; nevertheless, a new difficult challenge has emerged: finding an optimal solution for simultaneous dispatch of power and heat. Therefore, to tackle this problem, this work presents an intelligent sequential algorithm based on a hybridization of an enthusiasm‐aided teaching and learning‐based optimization algorithm (ETLBO) with an improved version of particle swarm optimization (IPSO). The proposed method can simultaneously minimize total generating costs while considering a variety of physical and operational limitations. In addition, this research designed an adaptive violation constraint management approach combined with the formulated hybridized optimization algorithm to ensure system constraints' safe preservation during the optimization process. Finally, the performance of the proposed method is compared to the recently developed metaheuristic algorithms as well as Knitro and IPOPT (industrially used optimization packages), in which the ETLBO‐IPSO outperforms all the other methods.

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Section: Motivations and Literature Reviewmentioning

confidence: 99%

A sequential hybridization of ETLBO and IPSO for solving reserve‐constrained combined heat, power and economic dispatch problem

Goudarzi

Fahad

et al. 2022

IET Generation Trans & Dist

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“…Material properties utilized for this example are given as follows: cross-section A = 0.0025 m 2 , mass density ρ = 2770 kg/m 3 , and Young’s modulus E = 69.8 GPa. Many researchers including Kaveh et al (2019), Bureerat and Pholdee (2018), Nguyen-Thoi et al (2018), Shahrouzi and Sabzi (2018), Kaveh and Zolghadr (2015), Pholdee and Bureeratand (2016), and Kaveh et al (2016) have attempted to address this problem.…”

Section: Numerical Examplesmentioning

confidence: 99%

An effective method for damage assessment based on limited measured locations in skeletal structures

Ghannadi

Kourehli

2020

Advances in Structural Engineering

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This article proposes a new damage detection method using Modal Test Analysis Model and artificial neural networks. A challenge in damage detection problems is lack of measured degrees of freedom, as well as limitations of attached sensors. Modal Test Analysis Model has been used in order to estimate unmeasured degrees of freedom. An experimental cantilever beam was used to show Modal Test Analysis Model’s efficiency in estimation of unmeasured mode shapes. To solve the inverse problem of damage detection, mode shapes estimated by Modal Test Analysis Model were used as inputs, and characteristics of the damage served as outputs of the artificial neural network. The sensitivity analysis carried out for each example showing the performance of artificial neural network after mode shape expansion was efficiently improved. Three numerical examples for plane and space truss structures are considered, in order to verify effectiveness of the proposed method. Results demonstrate a high accuracy of Modal Test Analysis Model and artificial neural network for structural damage detection.

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“…A category of SHM constitutes formulation of the damage detection as an inverse problem. It can further be solved by optimization algorithms such as meta‐heuristics 4–6 . However, this approach may require the prescribed damaged state and full data to model the structure for eigenvalue analysis.…”

Section: Introductionmentioning

confidence: 99%

“…It can further be solved by optimization algorithms such as meta-heuristics. [4][5][6] However, this approach may require the prescribed damaged state and full data to model the structure for eigenvalue analysis. Another major category includes signal processing techniques as critical component vibration-based SHM.…”

Section: Introductionmentioning

confidence: 99%

Identifying the dynamic characteristics of super tall buildings by multivariate empirical mode decomposition

Doroudi

Lavassani

Shahrouzi

et al. 2022

Structural Contr & Hlth

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Damage detection of truss structures by hybrid immune system and teaching–learning-based optimization

Cited by 20 publications

References 28 publications

A sequential hybridization of ETLBO and IPSO for solving reserve‐constrained combined heat, power and economic dispatch problem

A sequential hybridization of ETLBO and IPSO for solving reserve‐constrained combined heat, power and economic dispatch problem

An effective method for damage assessment based on limited measured locations in skeletal structures

Identifying the dynamic characteristics of super tall buildings by multivariate empirical mode decomposition

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