Non-Destructive Inspection of Rebar Corrosion in Concrete Structures by Using BEM and GA

2008 ◽  
Vol 33-37 ◽  
pp. 1289-1292 ◽  
Author(s):  
Koichi Minagawa ◽  
Keisuke Hayabusa ◽  
M. Ridha ◽  
Kenji Amaya ◽  
Shigeru Aoki
Keyword(s):  
Genetic Algorithm ◽  
Inverse Problem ◽  
Boundary Element Method ◽  
Concrete Structure ◽  
Inverse Analysis ◽  
Computational Time ◽  
Analysis Method ◽  
Rebar Corrosion ◽  
Non Destructive ◽  
Inverse Analysis Method

An inverse problem is analyzed where corrosion of rebars is detected from a small number of potential data measured at the surface of concrete structure. Because the shape and number of corrosion in rebars are not known in advance, usual inverse analysis method in which the shape and number of corroded part are assumed is not available. In this research, the genetic algorithm (GA) is employed without any assumption. The fitness in the multi-step GA is defined as the inverse of difference between experimental and numerical potential values, and is evaluated by the boundary element method (BEM). To reduce the computational time, the net elements, which have been recently developed by the authors for corrosion analysis of net structures, is used together with the multi-step GA. It is shown by a simulation that the multi-step GA with net elements are successfully employed in the inverse analysis.

scholarly journals Multi-step Inverse Analysis for Rebar Corrosion using Genetic Algorithm and Boundary Element Method

2008 ◽  
Vol 57 (6) ◽  
pp. 282-287 ◽  
Author(s):  
Koichi Minagawa ◽  
Keisuke Hayabusa ◽  
Kazuhiro Suga ◽  
M. Ridha ◽  
Kenji Amaya ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Boundary Element Method ◽  
Boundary Element ◽  
Inverse Analysis ◽  
Rebar Corrosion ◽  
Element Method

Cazacu and Barlat Criterion Identification Using the Cylindrical Cup Deep Drawing Test and the Coupled Artificial Neural Networks – Genetic Algorithm Method

2012 ◽  
Vol 504-506 ◽  
pp. 637-642 ◽  
Author(s):  
Hamdi Aguir ◽  
J.L. Alves ◽  
M.C. Oliveira ◽  
L.F. Menezes ◽  
Hedi BelHadjSalah
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Deep Drawing ◽  
Inverse Analysis ◽  
Computational Time ◽  
Ann Model ◽  
Drawing Test ◽  
Artificial Neural ◽  
Deep Drawing Test ◽  
Cylindrical Cup

This paper deals with the identification of the anisotropic parameters using an inverse strategy. In the classical inverse methods, the inverse analysis is generally coupled with a finite element code, which leads to a long computational time. In this work an inverse analysis strategy coupled with an artificial neural network (ANN) model is proposed. This method has the advantage of being faster than the classical one. To test and validate the proposed approach an experimental cylindrical cup deep drawing test is used in order to identify the orthotropic material behaviour. The ANN model is trained by finite element simulations of this experimental test. To reduce the gap between the experimental responses and the numerical ones, the proposed method is coupled with an optimization procedure based on the genetic algorithm (GA) to identify the Cazacu and Barlat’2001 material parameters of a standard mild steel DC06.

Development of Inverse Analysis of Heat Conduction and Thermal Stress for Elbow: Part I

2013 ◽  
Author(s):  
Seiji Ioka ◽  
Shiro Kubo ◽  
Mayumi Ochi ◽  
Kiminobu Hojo
Keyword(s):  
Heat Conduction ◽  
Thermal Stress ◽  
Transfer Function ◽  
Surface Temperature ◽  
Outer Surface ◽  
Inverse Analysis ◽  
Temperature History ◽  
Analysis Method ◽  
Inner Surface ◽  
Inverse Analysis Method

Thermal fatigue may develop in piping elbow with high temperature stratified flow. To prevent the fatigue damage by stratified flow, it is important to know the distribution of thermal stress and temperature history in a pipe. In this study, heat conduction inverse analysis method for piping elbow was developed to estimate the temperature history and thermal stress distribution on the inner surface from the outer surface temperature history. In the inverse analysis method, the inner surface temperature was estimated by using the transfer function database which interrelates the inner surface temperature with the outer surface temperature. Transfer function database was calculated by FE analysis in advance. For some patterns of the temperature history, inverse analysis simulations were made. It was found that the inner surface temperature history was estimated with high accuracy.

An Inverse Analysis Method for Design Optimization With Both Statistical and Fuzzy Uncertainties

2006 ◽  
Author(s):  
Liu Du ◽  
Kyung K. Choi
Keyword(s):  
Design Optimization ◽  
Inverse Analysis ◽  
Random Variables ◽  
Mean Value ◽  
Performance Measure ◽  
Distribution Functions ◽  
Analysis Method ◽  
Fuzzy Variables ◽  
Analysis And Design ◽  
Inverse Analysis Method

Structural analysis and design optimization have recently been extended to consider various uncertainties. If the statistical data for the uncertainties are sufficient to construct the input distribution function, the uncertainties can be treated as random variables and RBDO is used; otherwise, the uncertainties can be treated as fuzzy variables and PBDO is used. However, many structural design problems include both uncertainties with sufficient data and uncertainties with insufficient data. For these problems, RBDO will yield an unreliable design since the distribution functions of uncertainties are not believable. On the other hand, treating the random variables as fuzzy variables and invoking PBDO may yield too conservative design with a higher optimum cost. This paper proposes a new design formulation using the performance measure approach (PMA). For the inverse analysis, this paper proposes a new most probable/possible point (MPPP) search method called maximal failure search (MFS), which is an integration of the enhanced hybrid mean value method (HMV+) and maximal possibility search (MPS) method. Some mathematical and physical examples are used to demonstrate the proposed inverse analysis method and design formulation.

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