The Application of PSO in Structural Damage Detection: An Analysis of the Previously Released Publications (2005–2020)

Ghannadi, Parsa; Kourehli, Seyed Sina; Mirjalili, Seyedali

doi:10.3221/igf-esis.62.32

Cited by 28 publications

(13 citation statements)

References 96 publications

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“…The ANN model was employed to predict f ov , while the particle swarm optimization ( PSO ) algorithm [ 39 , 40 ] was introduced to optimize the hyperparameters of the ANN-PM . This method has been confirmed as an effective approach for hyperparameter optimization [ 41 ]. After that, the training set was inputted into the ANN-PM , and it was trained based on the optimal hyperparameters.…”

Section: Ann-based Predictive Model For F Ovmentioning

confidence: 99%

A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN

Xiao,

Li,

Lin

et al. 2024

Sensors

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To address the uncertainty of optimal vibratory frequency fov of high-speed railway graded gravel (HRGG) and achieve high-precision prediction of the fov, the following research was conducted. Firstly, commencing with vibratory compaction experiments and the hammering modal analysis method, the resonance frequency f0 of HRGG fillers, varying in compactness K, was initially determined. The correlation between f0 and fov was revealed through vibratory compaction experiments conducted at different vibratory frequencies. This correlation was established based on the compaction physical–mechanical properties of HRGG fillers, encompassing maximum dry density ρdmax, stiffness Krd, and bearing capacity coefficient K20. Secondly, the gray relational analysis algorithm was used to determine the key feature influencing the fov based on the quantified relationship between the filler feature and fov. Finally, the key features influencing the fov were used as input parameters to establish the artificial neural network prediction model (ANN-PM) for fov. The predictive performance of ANN-PM was evaluated from the ablation study, prediction accuracy, and prediction error. The results showed that the ρdmax, Krd, and K20 all obtained optimal states when fov was set as f0 for different gradation HRGG fillers. Furthermore, it was found that the key features influencing the fov were determined to be the maximum particle diameter dmax, gradation parameters b and m, flat and elongated particles in coarse aggregate Qe, and the Los Angeles abrasion of coarse aggregate LAA. Among them, the influence of dmax on the ANN-PM predictive performance was the most significant. On the training and testing sets, the goodness-of-fit R2 of ANN-PM all exceeded 0.95, and the prediction errors were small, which indicated that the accuracy of ANN-PM predictions was relatively high. In addition, it was clear that the ANN-PM exhibited excellent robust performance. The research results provide a novel method for determining the fov of subgrade fillers and provide theoretical guidance for the intelligent construction of high-speed railway subgrades.

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Section: Ann-based Predictive Model For F Ovmentioning

confidence: 99%

A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN

Xiao,

Li,

Lin

et al. 2024

Sensors

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“…For instance, several researchers have employed moth-flame [46], salp swarm [47], multiverse [48], whale [49], YUKI [50,51], wild horse [52] and slime mold [53] algorithms to solve the inverse problem of damage detection. However, conventional optimization methods such as simulated annealing (SA), particle swarm optimization (PSO) [54], and GA are still often used in damage identification problems. During the last two decades, the application of the SA algorithm is not limited to damage detection problems but also has other functions in terms of SHM, such as optimal sensor placement, system identification, and FEM updating.…”

Section: Introductionmentioning

confidence: 99%

A review of the application of the simulated annealing algorithm in structural health monitoring (1995-2021)

Ghannadi

Kourehli

Mirjalili

2023

Frattura Ed Integrità Strutturale

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In recent years, many innovative optimization algorithms have been developed. These algorithms have been employed to solve structural damage detection problems as an inverse solution. However, traditional optimization methods such as particle swarm optimization, simulated annealing (SA), and genetic algorithm are constantly employed to detect damages in the structures. This paper reviews the application of SA in different disciplines of structural health monitoring, such as damage detection, finite element model updating, optimal sensor placement, and system identification. The methodologies, objectives, and results of publications conducted between 1995 and 2021 are analyzed. This paper also provides an in-depth discussion of different open questions and research directions in this area.

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“…In the bridge health monitoring system, one of the most important techniques is to identify structural damage. The structural damage detection methods could be regarded as the core of SHM strategies [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Multiple Damaged Cables Identification in Cable-Stayed Bridges Using Basis Vector Matrix Method

Ren

Zhu

2023

Sensors

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A new damaged cable identification method using the basis vector matrix (BVM) is proposed to identify multiple damaged cables in cable-stayed bridges. The relationships between the cable tension stiffness and the girder bending strain of the cable-stayed bridge are established using a force method. The difference between the maximum bending strains of the bridges with intact and damaged cables is used to obtain the damage index vectors (DIXVs). Then, BVM is obtained by the normalized DIXV. Finally, the damage indicator vector (DIV) is obtained by DIXV and BVM to identify the damaged cables. The damage indicator is substituted into the damage severity function to identify the corresponding damage severity. A field cable-stayed bridge is used to verify the proposed method. The three-dimensional finite element model is established using ANSYS, and the model is validated using the field measurements. The validated model is used to simulate the strain response of the bridge with different damage scenarios subject to a moving vehicle load, including one, two, three, and four damaged cables with damage severity of 10%, 20%, and 30%, respectively. The noise effect is also discussed. The results show that the BVM method has good anti-noise capability and robustness.

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The Application of PSO in Structural Damage Detection: An Analysis of the Previously Released Publications (2005–2020)

Cited by 28 publications

References 96 publications

A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN

A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN

A review of the application of the simulated annealing algorithm in structural health monitoring (1995-2021)

Multiple Damaged Cables Identification in Cable-Stayed Bridges Using Basis Vector Matrix Method

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