A multi-component PSO algorithm with leader learning mechanism for structural damage detection

Li, Xiaolin; Serra, Roger; Olivier, Julien

doi:10.1016/j.asoc.2021.108315

Cited by 19 publications

(3 citation statements)

References 23 publications

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“…Different variants of this algorithm can be obtained by modifying the way the particle velocities and accelerations are described. This approach provides optimal solutions based on threshold conditions [45][46][47]. The PSO algorithm Consider the input space dataset X = x 1 , x 2 , .…”

Section: Pso Algorithmmentioning

confidence: 99%

Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique

Ren,

Chen,

Yang

et al. 2024

Applied Sciences

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External corrosion poses a significant threat to the integrity and lifespan of buried pipelines. Accurate prediction of corrosion rates is important for the safe and efficient transportation of oil and natural gas. However, limited data availability often impacts the performance of conventional predictive models. This study proposes a novel composite modeling approach integrating kernel principal component analysis (KPCA), particle swarm optimization (PSO), and extreme learning machine (ELM). The key innovation lies in using KPCA for reducing the dimensionality of complex input data combined with PSO for optimizing the parameters of the ELM network. The model was rigorously trained on 12 different datasets and comprehensively evaluated using metrics such as the coefficient of determination (R2), standard deviation (SD), mean relative error (MRE), and root mean square error (RMSE). The results show that KPCA effectively extracted four primary components, accounting for 91.33% of the data variability. The KPCA-PSO-ELM composite model outperformed independent models with a higher accuracy, achieving an R2 of 99.59% and an RMSE of only 0.0029%. The model comprehensively considered various indicators under the conditions of limited data. The model significantly improved the prediction accuracy and provides a guarantee for the safety of oil and gas transport.

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Section: Pso Algorithmmentioning

confidence: 99%

Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique

Ren,

Chen,

Yang

et al. 2024

Applied Sciences

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“…Li et al [43] used the standard PSO-FEM to compare the performance of fitness functions using natural frequencies. Later, the authors proposed an algorithm based on multi-component PSO with a cooperative leader learning mechanism for structural damage detection and further compared with other recent optimization algorithms [44]. Alamdari et al [45] implemented FRFs in a damaged structure and a damage sensitive shape was generated by taking the derivatives of operational mode shapes with the anti-symmetric extension and shape signals that are normalized at different natural frequencies.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity and Efficiency of the Frequency Shift Coefficient Based on the Damage Identification Algorithm: Modeling Uncertainty on Natural Frequencies

2022

Self Cite

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Health surveillance in industries is an important prospect to ensure safety and prevent sudden collapses. Vibration Based Structure Health Monitoring (VBSHM) is being used continuously for structures and machine diagnostics in industry. Changes in natural frequencies are frequently used as an input parameter for VBSHM. In this paper, the Frequency Shift Coefficient (FSC) is used for the assessment of various numerical damaged cases. An FSC-based algorithm is employed in order to estimate the positions and severity of damages using only the natural frequencies of healthy and unknown (damaged) structures. The study focuses on cantilever beams. By considering the minimization of FSC, damage positions and severity are obtained. Artificially damaged cases are assessed by changes in its positions, the number of damages and the size of damages along with the various parts of the cantilever beam. The study is further investigated by considering the effect of uncertainty on natural frequencies (0.1%, 0.2% and 0.3%) in damaged cases, and the algorithm is used to estimate the position and severity of the damage. The outcomes and efficiency of the proposed FSC based method are evaluated in order to locate and quantify damages. The efficiency of the algorithm is demonstrated by locating and quantifying double damages in a real cantilever steel beam using vibration measurements.

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“…In Section 3, the running results of the BPSO algorithm on the simulation of a wall-following robot are analysed and compared with other optimization algorithms in terms of convergence speed, running time, and an optimal solution. Section 4 provides the concluding remarks and prospects (Li et al, 2022;Pervaiz et al, 2022;Wei et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

The Role of Block Particles Swarm Optimization to Enhance The PID-WFR Algorithm

Suwoyo

Abdurohman

et al. 2022

IJEC

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In the conventional Proportional Integral Derivation (PID) controller, the parameters are often adjusted according to the formulas and actual application. However, this empirical method will bring two disadvantages. First, testing the program takes much time and usually needs help to reach the optimal solution. Second, the PID parameters will not adapt to the new environment when the situation changes. This paper proposed a method by employing a Block Particles Swarm Optimization (BPSO) to enhance the conventional Proportional Integral Derivation (PID) algorithm to overcome the mentioned disadvantages. The genetic algorithm (GA) first optimized the PID parameters. However, its optimization time is relatively long. Then, a Block Particle Swarm Optimization (BPSO) algorithm is designed to solve the problem of long optimization time. This method was then applied to the wall-following robot problem by realistically simulating it to confirm the performance. After Compared with conventional methods, the proposed method shows a relatively stable solution.

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A multi-component PSO algorithm with leader learning mechanism for structural damage detection

Cited by 19 publications

References 23 publications

Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique

Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique

Sensitivity and Efficiency of the Frequency Shift Coefficient Based on the Damage Identification Algorithm: Modeling Uncertainty on Natural Frequencies

The Role of Block Particles Swarm Optimization to Enhance The PID-WFR Algorithm

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