An Enhancing Particle Swarm Optimization Algorithm (EHVPSO) for damage identification in 3D transmission tower

Minh, Hoang-Le; Khatir, Samir; Wahab, Magd Abdel; Cuong-Le, Thanh

doi:10.1016/j.engstruct.2021.112412

Cited by 42 publications

(7 citation statements)

References 46 publications

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“…Since direct inversion is subject to the under-determinedness challenge, optimization-based inverse analysis has been investigated [6,10], the objective of which is to minimize the difference between experimental measurement and model prediction in the damage parametric space. Both deterministic optimization [6] and stochastic optimization [10][11][12] have been used. These algorithms have achieved good results in some damage identification cases.…”

Section: Introductionmentioning

confidence: 99%

Structural damage identification using piezoelectric impedance sensing with enhanced optimization and enriched measurements

Zhang¹,

Dupont²,

Wang³

et al. 2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

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Fault parameters in a structure can be identified by matching measurements with model predictions in the parametric space. As high frequency measurements are preferred to uncover small-size damage, piezoelectric impedance/admittance active interrogation has shown promising aspects. Nevertheless, the amount of useful measurement information is generally insufficient to pinpoint damage, and the inverse identification is underdetermined. In this research, we develop a combinatorial enhancement to tackle these challenges. A tunable piezoelectric impedance sensing procedure is developed in which an adaptive inductance element is integrated with the piezoelectric transducer, which will lead to enriched measurement data for the same damage. Subsequently, an intelligent multi-objective particle swarm optimization approach is synthesized to inversely identify the damage location and severity. Case studies are conducted to highlight the accuracy of the damage identification.

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Section: Introductionmentioning

confidence: 99%

Structural damage identification using piezoelectric impedance sensing with enhanced optimization and enriched measurements

Zhang¹,

Dupont²,

Wang³

et al. 2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

View full text Add to dashboard Cite

show abstract

“…[32,33] Due to its high prediction performance and low computational cost, machine learning has been successfully implemented in biology, [34] chemistry, [25,35,36] drug development, [37] medicine, [38,39] and many optimization processes. [40][41][42] Despite the long history of data-driven machine learning methods in these fields, data-driven research in the field of porous materials, and specifically nanostructured aerogels, only rose to prominence recently. [43] As an example, Goodarzi et al employed various machine learning models, namely Gaussian processes, K-nearest neighbor, and radius nearest neighbor for predicting the thermal conductivity of selected aerogels.…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based model for predicting the material properties of nanostructured aerogels

Tafreshi

Saadatnia

Ghaffari‐Mosanenzadeh

et al. 2022

SPE Polymers

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Data-driven modeling in material science rose to prominence in the last decade, and various supervised and unsupervised machine learning techniques have been employed for material development and deriving insights for decision-making purposes. In this context, machine learning can have prominent importance in the field of nanostructured aerogels for accelerated materials design and material properties prediction. Current attempts rely only on experimental approach, which have inherent shortcomings, including inefficiency due to the prolonged synthesis process, and necessity of analyzing microstructure and properties. In order to address the challenges associated with the traditional experimental approach, in this study, an artificial neural network (ANN) is employed to predict the material properties of nanostructured aerogels. Polyimide (PI) organic aerogels are selected for this purpose.Through understanding the contributing material and processing factors in PI aerogel synthesis, a dataset is prepared. Data preprocessing is performed, and through hyperparameter tuning, ANN is constructed and trained for a given dataset. Various material properties are predicted, including compressive modulus, density, and porosity. Results show that ANN is trained with high accuracy, which demonstrates the versatility and accuracy of model in materials properties prediction. This study can therefore pave the way for establishing a platform for data-driven materials innovation.

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“…couplers [3], beam-like [4] and plate-like structures [5]) to large scale structures, (e.g. buildings [6], bridges [7], pipelines [8], oil and gas platforms [9], railways [10], tunnels [11], dams [12], transmission towers [13], wind turbine structures [14], and offshore structures [15]) in order to make satisfactory decisions on structural maintenance, repair, and rehabilitation. SHM is the process of applying a damage detection approach to evaluate the health condition of civil, mechanical, and aerospace in-service structures.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Secure Storage Using Blockchain for PCA-FRF Sensor Data of Plate-Like Structures

et al. 2022

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Structural Health Monitoring (SHM) systems have widely been used to guarantee the safe functioning of electrical, mechanical, civil, and aerospace engineering assets. It is required to improve the SHM systems from different aspects in terms of smartification, performance, beneficiation, sustainment, automation, cost-effectivity, and safety using cutting-edge technologies. Blockchain is currently the most revolutionary technology in computer science. Implementation of Blockchain in different fields of academia and industry has recently given very good results. However, application of Blockchain in SHM is still in the infancy stage. Therefore, many challenges are still ahead. One of the main potential applications of Blockchain is to secure the sensor data. In this study, a local Blockchain approach is developed as a secure storage using the extracted Principal Components (PCs) of Frequency Response Function (FRF) data obtained from modal analysis of a plate-like structure. In general, Secure Hash Algorithm (SHA) is one of the most practical hash functions with efficient performance which has been employed in Bitcoin. Therefore, in this research, SHA-256 is considered to generate the hash for each block. To the best of our knowledge, this is the first attempt to develop a secure storage for SHM data using Blockchain.

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An Enhancing Particle Swarm Optimization Algorithm (EHVPSO) for damage identification in 3D transmission tower

Cited by 42 publications

References 46 publications

Structural damage identification using piezoelectric impedance sensing with enhanced optimization and enriched measurements

Structural damage identification using piezoelectric impedance sensing with enhanced optimization and enriched measurements

Machine learning‐based model for predicting the material properties of nanostructured aerogels

Implementation of a Secure Storage Using Blockchain for PCA-FRF Sensor Data of Plate-Like Structures

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