Crack prediction in beam-like structure using ANN based on frequency analysis

Seguini, Meriem; Nedjar, Djamel; Boutchicha, Djilali; Khatir, Samir; Wahab, Magd Abdel

doi:10.3221/igf-esis.59.02

Cited by 10 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we proposed a comparison between the results using Bat algorithms, cuckoo search and YUKI and the results found in [46], where the ANN is used to predict the damage size in two beams (Beam I and Beam II) and using different hidden layer sizes (HLS), and the results indicate that the best regression is achieved with HLS = 8 for Beam I and HLS = 10 for Beam II. In their research, Seguini et al used the frequencies as an input and the damage size as an output.…”

Section: Resultsmentioning

confidence: 99%

A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF

Zenzen,

Ayadi,

Benaissa

et al. 2024

Comptes Rendus. Mécanique

View full text Add to dashboard Cite

In recent years, substantial investments in structural construction underscore the paramount importance of ensuring structural integrity for safety and dependability. Structural Health Monitoring (SHM) has emerged as a pivotal tool for assessing structural health, with an emphasis on damage detection, localisation, and quantification, particularly through vibration-based methods that exploit variations in modal properties as precursors to structural damage. This study presents an innovative methodology that synergistically combines Proper Orthogonal Decomposition and Radial Basis Function interpolation for predicting structural responses based on crack parameters. Additionally, the YUKI algorithm, leveraging population clustering for optimisation, is introduced. The approach is rigorously assessed through experimental analysis of two distinct beams (Beam I and Beam II) exhibiting varying crack depths. The results demonstrate the effectiveness of the POD-RBF-YUKI approach, indicating a notable level of accuracy and consistency. Comparative

show abstract

Section: Resultsmentioning

confidence: 99%

A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF

Zenzen,

Ayadi,

Benaissa

et al. 2024

Comptes Rendus. Mécanique

View full text Add to dashboard Cite

show abstract

“…ANN's are effective methods that can be applied in the field of Structural Health Monitoring, mainly by using the modal parameters of structures. Papers such as [17]- [19] describe the successful use of intelligent algorithms for the detection and quantification of cracks present in beams or plate like structures.…”

Section: Development Of the Artificial Neural Network (Ann)mentioning

confidence: 99%

Determining the position of two cracks in a cantilever beam using artificial neural networks

Pop¹,

Tufiși²,

Gillich³

2022

Vib. proced.

View full text Add to dashboard Cite

During functioning time, structures such as beams are subjected to a variety of loads caused by the working conditions and environment, which can lead to the development of cracks. The current research is concerned with detecting the presence and location of two transverse breathing cracks, in cantilever beams. Starting from the known fact that damages produce a stiffness degradation in structures altering their dynamic parameters, we performed modal simulations of damaged beams to determine their natural frequencies. By using the simulation data, we trained an artificial neural network (ANN), using the feedforward backpropagation algorithm, that is capable to detect the presence of the cracks, their position and for the case when the damages are in proximity, the model can determine if the cracks occur on the same face or opposite faces.

show abstract

“…Traditionally, analytical methods have provided valuable insights into the behavior of double-clamped beams subjected to external factors such as cracks [5]. One widely recognized technique employs the squared normalized modal curvature as a reliable indicator of crack presence [6].…”

Section: Introductionmentioning

confidence: 99%

Damage detection in variable temperature conditions using artificial intelligence

Aman,

Tufisi,

Gillich

et al. 2023

Vib. proced.

View full text Add to dashboard Cite

When considering damage detection using the natural frequencies of structures, small frequency drops can indicate either the presence of cracks or a temperature change. This change can lead to additional stress affecting the modal parameters for specific structures, making it much harder to detect, locate, and evaluate damage accurately. The current research aims to describe a method for detecting transverse cracks in beams, considering temperature variations. The considered beam is fixed at both ends, thus inducing axial forces when the temperature is increased. The influence of temperature is considered using adjustment coefficients developed for each vibration mode. This coefficient can be used to accurately calculate the natural frequency for an intact or damaged beam. An analytical method for determining the natural frequencies caused by the changing temperature and the presence of a transverse crack is described and used to generate data for training a feedforward artificial neural network (ANN). The ANN’s capability of determining the position of transverse cracks in double-clamped beams subjected to small temperature changes is proven by creating numerical simulations with known crack positions and thermal conditions for testing the developed method.

show abstract

Crack prediction in beam-like structure using ANN based on frequency analysis

Cited by 10 publications

References 19 publications

A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF

A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF

Determining the position of two cracks in a cantilever beam using artificial neural networks

Damage detection in variable temperature conditions using artificial intelligence

Contact Info

Product

Resources

About