scholarly journals Structural Damage Identification Based on Rough Sets and Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Chengyin Liu ◽  
Xiang Wu ◽  
Ning Wu ◽  
Chunyu Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Rough Sets ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Structural Damage Detection ◽  
Element Analysis ◽  
Artificial Neural

This paper investigates potential applications of the rough sets (RS) theory and artificial neural network (ANN) method on structural damage detection. An information entropy based discretization algorithm in RS is applied for dimension reduction of the original damage database obtained from finite element analysis (FEA). The proposed approach is tested with a 14-bay steel truss model for structural damage detection. The experimental results show that the damage features can be extracted efficiently from the combined utilization of RS and ANN methods even the volume of measurement data is enormous and with uncertainties.

scholarly journals A REVIEW PAPER ON STRUCTURAL DAMAGE IDENTIFICATION OF CANTILEVER BEAM BY USING VIBRATION TECHNIQUE, FINITE ELEMENT ANALYSIS & ARTIFICIAL NEURAL NETWORK

2015 ◽  
Vol 3 (12) ◽  
pp. 125-128
Author(s):  
Aakanksha MohanraoGarud ◽  
V. G. Bhamre
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Structural Damage ◽  
Review Paper ◽  
Damage Identification ◽  
Frequency Measurement ◽  
Element Analysis ◽  
Structural Damage Identification ◽  
Artificial Neural

In this review paper structural damage identification work in cantilever beam is done by using the Artificial Neural Network as diagnostic parameter. The study is based on the concept that natural frequency is inversely proportional to the mass of the structure. Thus to regulate the proper condition of structure, periodical frequency measurement is necessary. But in dynamic conditions and in complicated structures frequency measurement is difficult, for the same we reviewed various papers to identify the structural damage using various methods. The factors which affects on the damage of structural parts like crack depth, crack location etc. is also discussed in this work. Natural frequency is measured with the help of fast fourier transform by various authors and artificial neural network is also used for identification of the damage in many papers. So in this review work we studied methods of structural damage identification such as vibrations, finite element analysis and artificial neural network.

Vibration–Based Damage Detection Of Slab Structure Using Artificial Neural Network

2012 ◽  
Author(s):  
Norhisham Bakhary
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Damage Detection ◽  
Ann Model ◽  
Element Analysis ◽  
Severity Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kertas kerja ini memaparkan kajian berkenaan keberkesanan Artificial Neural Network (ANN) dalam mengenal pasti kerosakan di dalam struktur. Data dari getaran seperti frekuensi semula jadi dan mod bentuk digunakan sebagai data masukan bagi ANN untuk meramalkan lokasi dan tahap kerosakan bagi struktur lantai. Analisis unsur terhingga (Finite Element Analysis) telah digunakan untuk memperoleh ciri–ciri dinamik bagi struktur–struktur rosak dan tidak rosak untuk ‘melatih’ model ‘neural network’. Senario kerosakan yang berbeza disimulasikan dengan mengurangkan kekukuhan elemen pada lokasi yang berbeza sepanjang struktur tersebut. Berbagai kombinasi data masukan bagi mod yang berbeza telah digunakan untuk memperolehi model ANN yang terbaik. Hasil kajian ini menunjukkan ANN mampu memberikan keputusan yang baik dalam meramal kerosakan pada struktur lantai tersebut. Kata kunci: Ramalan kerosakan struktur, Artificial Neural Network This paper investigates the effectiveness of artificial neural network (ANN) in identifying damages in structures. Global (natural frequencies) and local (mode shapes) vibration–based data has been used as the input to ANN for location and severity prediction of damages in a slab–like structure. A finite element analysis has been used to obtain the dynamic characteristics of intact and damaged structure to train the neural network model. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations along the structure. Several combinations of input variables in different modes have been used in order to obtain a reliable ANN model. The trained ANN model is validated using laboratory test data. The results show that ANN is capable of providing acceptable result on damage prediction of tested slab structure. Key words: Structural damage detection, artificial neural network

Effect of Attribute Reduction on Rough-Probabilistic Neural Network for Structural Damage Detection

2010 ◽  
Vol 163-167 ◽  
pp. 2482-2487
Author(s):  
Shao Fei Jiang ◽  
Zhao Qi Wu
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Data Storage ◽  
Structural Damage ◽  
Probabilistic Neural Network ◽  
Attribute Reduction ◽  
Measurement Data ◽  
Noise Tolerance ◽  
Structural Damage Detection ◽  
Proposed Model

In this paper, a new rough-probabilistic neural network (RSPNN) model, whereby rough set data and a probabilistic neural network (PNN) are integrated, is proposed. This model is used for structural damage detection, particularly for cases where the measurement data has many uncertainties. To verify the proposed method, an example is presented to identify both single and multi-damage case patterns. The effects of measurement noise and attribute reduction on the damage detection results are also discussed. The results show that the proposed model not only has good damage detection capability and noise tolerance, but also reduces data storage memory requirements.

scholarly journals Multiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7357
Author(s):  
Byungmo Kim ◽  
Chanyeong Kim ◽  
Seung-Hyun Ha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Mode Shape ◽  
Structural Damage ◽  
Mode Shapes ◽  
Estimation Accuracy ◽  
The Neural Network ◽  
Artificial Neural ◽  
Shape Data

A helideck is an essential structure in an offshore platform, and it is crucial to maintain its structural integrity and detect the occurrence of damage early. Because helidecks usually consist of complex lattice truss members, precise measurements are required for structural health monitoring based on accurate modal parameters. However, available sensors and data acquisition are limited. Therefore, we propose a two-step damage detection process using an artificial neural network. Based on the mode shape database collected from 137,400 damage scenarios by finite element analysis, the neural network in the first step was trained to estimate the mode shapes of the entire helideck model using the selected mode shape data obtained from the limited measuring points. Then, the neural network in the second step is consecutively trained to detect the location and amount of structural damage to individual parts. As a result, it is shown that the proposed procedure provides the damage detection capability with only a quarter of the entire mode shape data, while the estimation accuracy is sufficiently high compared to the single network directly trained using all mode shape data. It was also found that, compared to the network directly trained from the same data, the proposed technique tends to detect minor damages more accurately.

Sparse damage detection via the elastic net method using modal data

2021 ◽  
pp. 147592172110219
Author(s):  
Rongrong Hou ◽  
Xiaoyou Wang ◽  
Yong Xia
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Elastic Net ◽  
Sensitivity Matrix ◽  
Regularization Technique ◽  
Modal Data ◽  
Regularization Techniques ◽  
Net Method

The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.

Notch (stress concentration) factor estimation of a cylinder under internal pressure using different approaches

2021 ◽  
Vol 63 (5) ◽  
pp. 430-435
Author(s):  
Osman Atalay ◽  
Ihsan Toktas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stress Concentration ◽  
Mechanical Design ◽  
Computer Software ◽  
Element Analysis ◽  
Notch Stress ◽  
Artificial Neural ◽  
Effect Parameter ◽  
Stress Concentration Factors

Abstract Today, fluid transportation via pipes can be found in many sectors. Therefore, safe fluid transportation possesses critical importance. While working, transportation pipes are exposed to unwanted loads that culminate in stresses which cause deformation on the part geometry especially in sharp corners, holes or sudden cross-section change areas considered as notched. The notch effect parameter is considered in the mechanical design formulas. This study is interested in the notch factor that is estimated for a cylinder which undergoes an inner pressure. Some users can use false numerical values due to misreading or lack of attention. Because of this reason, graphs were converted to the numerical value by using computer software. In this study, Peterson’s chart was accepted as scientifically valid. Stress concentration factors were obtained by using four other approaches. These are regression, analytical, artificial neural network and finite element analysis. Among these models, high accuracy values were given by the artificial neural network model.

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