Damage Detection based on the Natural Frequency shifting of a clamped rectangular plate model

Hilmy, Irfan; Wahab, Magd Abdel; Adesta, Erry Yulian Triblas; Firdaus, Tasnim

doi:10.1088/1742-6596/628/1/012034

Cited by 5 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the nature of engineering structures, preventive maintenance plays a very important role in assuring their overall integrity. Structural Health Monitoring methods should also consider, besides the degradation of the structure, the possibility of joint loosening [6], which is a type of damage that is difficult to model and even more difficult to detect by using traditional non-destructive methods such as ultrasonic methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of weak joints and damages for beams using machine learning

Tufiși¹,

Gillich²,

David³

et al. 2022

Vib. proced.

View full text Add to dashboard Cite

For maintaining the safe operation of structures, it is necessary to develop SHM methods that can detect not only the presence of cracks in the structure but also any alterations of its fastening conditions. The current paper presents a method for developing an Artificial Intelligent model that can detect if a beam is affected by transverse cracks and at the same time, by improper boundary conditions. To this aim, a cantilever steel beam is considered as the in the current study. The training data for the artificial neural network (ANN) is created using an original analytic method which allows calculating the natural frequency loss caused by the occurrence of transverse cracks even if the beam is improperly fastened. The intelligent model is trained by employing the MATLAB software and tested using data acquired from numerical simulations. The results show very high accuracy in determining the presence of transverse cracks, and the capability of detecting the presence and severity of improper clamping conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Considering the modal approach in damage detection, a large amount of data is usually necessary, making it difficult to establish the damage signature [5]. To overcome this shortcoming, machine learning methods can be applied to develop intelligent damage detection models [6,9].…”

Section: Introductionmentioning

confidence: 99%