scholarly journals DAMAGE DETECTION IN A COMPOSITE BEAM WITH A DOUBLE EDGE CRACK USING FINITE ELEMENT ANALYSIS

2011 ◽  
Vol 5 (1) ◽  
pp. 41-51
Author(s):  
Ramanamurthy E.V.V ◽  
Chandrasekaran K
2012 ◽  
Author(s):  
Norhisham Bakhary

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


2014 ◽  
Vol 578-579 ◽  
pp. 839-845
Author(s):  
Meng Zhou Lv ◽  
Han Chen Guo ◽  
Feng Tian

Effective flange width’s value of U-section steel-encased concrete composite beam was studied by ANSYS. Based on the static equivalent principle, the general rules of effective flange width changed with load are summarized by finite element analysis and considering the effect of 4 factors: width-span ratio, loading form, concrete thickness, and material strength including concrete strength and steel strength. The analysis results show that wide-span ratio and load form has a greater impact on the effective flange width; concrete thickness and material strength’s effects can be negligible.


2019 ◽  
Author(s):  
Pratik R. Patil ◽  
Aniket S. Ahire ◽  
M. L. J. Suman ◽  
S. M. Murigendrappa

2020 ◽  
Vol 4 (4) ◽  
pp. 185
Author(s):  
Mahendran Govindasamy ◽  
Gopalakrishnan Kamalakannan ◽  
Chandrasekaran Kesavan ◽  
Ganesh Kumar Meenashisundaram

This paper deals with detection of macro-level crack type damage in rectangular E-Glass fiber/Epoxy resin (LY556) laminated composite plates using modal analysis. Composite plate-like structures are widely found in aerospace and automotive structural applications which are susceptible to damages. The formation of cracks in a structure that undergoes vibration may lead to catastrophic events such as structural failure, thus detection of such occurrences is considered necessary. In this research, a novel technique called as node-releasing technique in Finite Element Analysis (FEA), which was not attempted by the earlier researchers, is used to model the perpendicular cracks (the type of damage mostly considered in all the pioneering research works) and also slant cracks (a new type of damage considered in the present work) of various depths and lengths for Unidirectional Laminate (UDL) ([0]S and [45]S) composite layered configurations using commercial FE code Ansys, thus simulating the actual damage scenario. Another novelty of the present work is that the crack is modeled with partial depth along the thickness of the plate, instead of the through the thickness crack which has been of major focus in the literature so far, in order to include the possibility of existence of the crack up to certain layers in the laminated composite structures. The experimental modal analysis is carried out to validate the numerical model. Using central difference approximation method, the modal curvature is determined from the displacement mode shapes which are obtained via finite element analysis. The damage indicators investigated in this paper are Normalized Curvature Damage Factor (NCDF) and modal strain energy-based methods such as Strain Energy Difference (SED) and Damage Index (DI). It is concluded that, all the three damage detection algorithms detect the transverse crack clearly. In addition, the damage indicator NCDF seems to be more effective than the other two, particularly when the detection is for damage inclined to the longitudinal axis of the plate. The proposed method will provide the base data for implementing online structural health monitoring of structures using technologies such as Machine Learning, Artificial Intelligence, etc.


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