Damage detection in a cantilever beam using noisy mode shapes with an application of artificial neural network-based improved mode shape curvature technique

2021 ◽  
Author(s):  
Sonu Kumar Gupta ◽  
Surajit Das
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Cantilever Beam ◽  
Mode Shape ◽  
Mode Shapes ◽  
Artificial Neural

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.

An effective method for damage assessment based on limited measured locations in skeletal structures

2020 ◽  
Vol 24 (1) ◽  
pp. 183-195 ◽  
Author(s):  
Parsa Ghannadi ◽  
Seyed Sina Kourehli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Degrees Of Freedom ◽  
Truss Structures ◽  
Mode Shapes ◽  
Analysis Model ◽  
Test Analysis ◽  
Modal Test ◽  
Artificial Neural

This article proposes a new damage detection method using Modal Test Analysis Model and artificial neural networks. A challenge in damage detection problems is lack of measured degrees of freedom, as well as limitations of attached sensors. Modal Test Analysis Model has been used in order to estimate unmeasured degrees of freedom. An experimental cantilever beam was used to show Modal Test Analysis Model’s efficiency in estimation of unmeasured mode shapes. To solve the inverse problem of damage detection, mode shapes estimated by Modal Test Analysis Model were used as inputs, and characteristics of the damage served as outputs of the artificial neural network. The sensitivity analysis carried out for each example showing the performance of artificial neural network after mode shape expansion was efficiently improved. Three numerical examples for plane and space truss structures are considered, in order to verify effectiveness of the proposed method. Results demonstrate a high accuracy of Modal Test Analysis Model and artificial neural network for structural damage detection.

A Comparison of Artificial Neural Network Learning Algorithms for Vibration-Based Damage Detection

2010 ◽  
Vol 163-167 ◽  
pp. 2756-2760 ◽  
Author(s):  
Goh Lyn Dee ◽  
Norhisham Bakhary ◽  
Azlan Abdul Rahman ◽  
Baderul Hisham Ahmad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Conjugate Gradient ◽  
Learning Algorithms ◽  
Mode Shapes ◽  
Neural Network Learning ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Artificial Neural

This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studied and their performances are compared. The algorithms are Levenberg-Marquardt (LM), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Conjugate Gradient with Powell-Beale Restarts (CGB), Polak-Ribiere Conjugate Gradient (CGP) and Fletcher-Reeves Conjugate Gradient (CGF) algorithms. The performances of these algorithms are assessed based on their generalisation capability in relating the vibration parameters (frequencies and mode shapes) with damage locations and severities under various numbers of input and output variables. The results show that Levenberg-Marquardt algorithm provides the best generalisation performance.

Prediction of Unmeasured Mode Shape Using Artificial Neural Network for Damage Detection

2013 ◽  
Vol 61 (1) ◽  
Author(s):  
L. D. Goh ◽  
N. Bakhary ◽  
A. A. Rahman ◽  
B. H. Ahmad
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Mode Shape ◽  
Health Monitoring ◽  
Spline Interpolation ◽  
Measured Data ◽  
Mode Shapes ◽  
Modal Data ◽  
Artificial Neural ◽  
Shape Data

Artificial neural networks (ANNs) have received much attention in the field of vibration–based damage detection since the 1990s, due to their capability to predict damage from modal data. However, the accuracy of this method is highly dependent on the number of measurement points, especially when the mode shape is used as an indicator for damage detection. With a high number of measurement points, more information can be fed to the ANN to detect damage; therefore, more reliable results can be obtained. Nevertheless, in practice, it is uneconomical to install sensors on every part of a structure; thus the capability of ANNs to detect damage is quite limited. In this study, an ANN is applied to predict the unmeasured mode shape data based on a limited number of measured data. To demonstrate the accuracy of the proposed method, the results are compared with the Cubic Spline interpolation (CS) method. A parametric study is also conducted to investigate the sensitivity of the number of measurement points to the proposed method. The results show that the ANN provides more reliable results compared to the CS method as it is able to predict the magnitude of mode shapes at the unmeasured points with a limited number of measurement points. The application of a two–stage ANN showed results with a high potential for overcoming the issue of using a limited number of sensors in structural health monitoring.

Damage Detection of Offshore Platform Mooring Line Using Artificial Neural Network

2018 ◽  
Author(s):  
Djoni E. Sidarta ◽  
Jim O’Sullivan ◽  
Ho-Joon Lim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Total Mass ◽  
Offshore Platform ◽  
Mooring Line ◽  
Ann Model ◽  
Station Keeping ◽  
Mooring Lines ◽  
Artificial Neural

Station-keeping using mooring lines is an important part of the design of floating offshore platforms, and has been used on most types of floating platforms, such as Spar, Semi-submersible, and FPSO. It is of great interest to monitor the integrity of the mooring lines to detect any damaged and/or failures. This paper presents a method to train an Artificial Neural Network (ANN) model for damage detection of mooring lines based on a patented methodology that uses detection of subtle shifts in the long drift period of a moored floating vessel as an indicator of mooring line failure, using only GPS monitoring. In case of an FPSO, the total mass or weight of the vessel is also used as a variable. The training of the ANN model employs a back-propagation learning algorithm and an automatic method for determination of ANN architecture. The input variables of the ANN model can be derived from the monitored motion of the platform by GPS (plus vessel’s total mass in case of an FPSO), and the output of the model is the identification of a specific damaged mooring line. The training and testing of the ANN model use the results of numerical analyses for a semi-submersible offshore platform with twenty mooring lines for a range of metocean conditions. The training data cover the cases of intact mooring lines and a damaged line for two selected adjacent lines. As an illustration, the evolution of the model at various training stages is presented in terms of its accuracy to detect and identify a damaged mooring line. After successful training, the trained model can detect with great fidelity and speed the damaged mooring line. In addition, it can detect accurately the damaged mooring line for sea states that are not included in the training. This demonstrates that the model can recognize and classify patterns associated with a damaged mooring line and separate them from patterns of intact mooring lines for sea states that are and are not included in the training. This study demonstrates a great potential for the use of a more general and comprehensive ANN model to help monitor the station keeping integrity of a floating offshore platform and the dynamic behavior of floating systems in order to forecast problems before they occur by detecting deviations in historical patterns.

Damage detection in steel-concrete composite bridge using vibration characteristics and artificial neural network

2019 ◽  
Vol 16 (9) ◽  
pp. 1247-1261 ◽  
Author(s):  
Zhi Xin Tan ◽  
David P. Thambiratnam ◽  
Tommy H. T. Chan ◽  
Meisam Gordan ◽  
Hashim Abdul Razak
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Vibration Characteristics ◽  
Composite Bridge ◽  
Artificial Neural

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

scholarly journals Crack Detection in Folded Plates With Back-Propagated Artificial Neural Network

2021 ◽  
Author(s):  
Can Gonenli ◽  
Oguzhan Das ◽  
Duygu Bagci Das
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Crack Detection ◽  
Structural Integrity ◽  
Engineering Structures ◽  
Detection Techniques ◽  
Detection Model ◽  
Artificial Neural ◽  
Destructive Methods

Abstract Engineering structures may face various damages such as crack, delamination, and fatigue in several circumstances. Localizing such damages becomes essential to understand the health of the structures since they may not be able to operate anymore. Among the damage detection techniques, non-destructive methods are considerably more preferred than destructive methods since damage can be located without affecting the structural integrity. However, these methods have several drawbacks in terms of detecting abilities, time consumption, cost, and hardware or software requirements. Employing artificial intelligence techniques could overcome such issues and could provide a powerful damage detection model if the technique is utilized correctly. In this study, the crack localization in flat and folded plate structures has been conducted by employing a Back-propagated Artificial Neural Network (BPANN). For this purpose, cracks with 18 different dimensions have been modeled in flat and four different folded structures by utilizing the Finite Element Method. The dataset required to perform the crack localization procedure includes the first ten natural frequencies of all structures as input variables. As output variables, the dataset contains a total of 500 crack locations for five structures. It is concluded that the BPANN can localize all cracks with an average accuracy of 95.12%.

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