Optimum Design of Structures for Earthquake Loading by a Cellular Evolutionary Algorithm and Neural Networks

2012 ◽  
pp. 306-322 ◽  
Author(s):  
Saeed Gholizadeh
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Optimum Design ◽  
Optimization Algorithm ◽  
Time History ◽  
Network System ◽  
Hybrid Neural Network ◽  
Neural Network System ◽  
Nonlinear Time History

The present chapter deals with optimum design of structures for earthquake induced loads by taking into account nonlinear time history structural response. As the structural seismic optimization is a time consuming and computationally intensive task, in this chapter, a methodology is proposed to reduce the computational burden. The proposed methodology consists of an efficient optimization algorithm and a hybrid neural network system to effectively predict the nonlinear time history responses of structures. The employed optimization algorithm is a modified cellular genetic algorithm which reduces the required generation numbers compared with the standard genetic algorithm. Also, the hybrid neural network system is a combination of probabilistic and generalized regression neural networks. Numerical results demonstrate the computational merits of the proposed methodology for seismic design optimization of structures.

The Health Monitoring of Bridges using Artificial Neural Networks

2001 ◽  
Vol 17 (3) ◽  
pp. 157-166
Author(s):  
Pei-Ling Liu ◽  
Shyh-Jang Sun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Monitoring System ◽  
Health Monitoring ◽  
Network System ◽  
Bridge Structure ◽  
Constant Mass ◽  
Simulation Data ◽  
Neural Network System ◽  
The Neural Networks

ABSTRACTThis study develops a neural network system to monitor the safety of a bridge structure. A truck of constant mass is driven at constant speed through the target bridge. Then, the maximal and minimal values of the bridge elongations are processed by a monitoring system to evaluate the current condition of the bridge. The monitoring system is composed of parallel backpropagation neural networks. Each neural network monitors a part of the bridge. The neural networks are trained using simulation data. The numerical example shows that the monitoring system is effective in the damage detection of the bridge.

scholarly journals Gas Turbine and Sensor Fault Diagnosis With Nested Artificial Neural Networks

2004 ◽  
Author(s):  
N. Xiradakis ◽  
Y. G. Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gas Turbine ◽  
Diagnostic System ◽  
Network System ◽  
Fault Recovery ◽  
Sensor Fault ◽  
Engine Component ◽  
Neural Network System

Accurate gas turbine diagnosis relies on accurate measurements from sensors. Unfortunately, sensors are prone to degradation or failure during gas turbine operations. In this paper a stack of decentralised artificial neural networks are introduced and investigated as an approach to approximate the measurement of a failed sensor once it is detected. Such a system is embedded into a nested neural network system for gas turbine diagnosis. The whole neural network diagnostic system consists of a number of feedforward neural networks for engine component diagnosis, sensor fault detection and isolation; and a stack of decentralised neural networks for sensor fault recovery. The application of the decentralised neural networks for the recovery of any failed sensor has the advantage that the configuration of the nested neural network system for engine component diagnosis is relatively simple as the system does not take into account sensor failure. When a sensor fails, the biased measurement of the failed sensor is replaced with a recovered measurement approximated with the measurements of other healthy sensors. The developed approach has been applied to an engine similar to the industrial 2-shaft engine, GE LM2500+, whose performance and training samples are simulated with an aero-thermodynamic modelling tool — Cranfield University’s TURBOMATCH computer program. Analysis shows that the use of the stack of decentralised neural networks for sensor fault recovery can effectively recover the measurement of a failed sensor. Comparison between the performance of the diagnostic system with and without the decentralised neural networks shows that the sensor recovery can improve the performance of the neural network engine diagnostic system significantly when a sensor fault is present.

MODELING OF INPUT-OUTPUT RELATIONSHIPS FOR ELECTRON BEAM BUTT WELDING OF DISSIMILAR MATERIALS USING NEURAL NETWORKS

2014 ◽  
Vol 13 (03) ◽  
pp. 1450016 ◽  
Author(s):  
M. N. JHA ◽  
D. K. PRATIHAR ◽  
A. V. BAPAT ◽  
V. DEY ◽  
MAAJID ALI ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Electron Beam ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Input Output ◽  
Swarm Optimization ◽  
Butt Welding

Electron beam butt welding of stainless steel (SS 304) and electrolytically tough pitched (ETP) copper plates was carried out according to central composite design of experiments. Three input parameters, namely accelerating voltage, beam current and weld speed were considered in the butt welding experiments of dissimilar metals. The weld-bead parameters, such as bead width and depth of penetration, and weld strength in terms of yield strength and ultimate tensile strength were measured as the responses of the process. Input-output relationships were established in the forward direction using regression analysis, back-propagation neural network (BPNN), genetic algorithm-tuned neural network (GANN) and particle swarm optimization algorithm-tuned neural network (PSONN). Reverse mapping of this process was also conducted using the BPNN, GANN and PSONN approaches, although the same could not be done from the obtained regression equations. Neural networks were found to tackle the problems of both forward and reverse mappings efficiently. However, neural networks tuned by the genetic algorithm and particle swarm optimization algorithm were seen to perform better than the BPNN in most of the cases but not all.

scholarly journals Neural Network System Design for Predicting MIN Reliability

2020 ◽  
Vol 9 (7) ◽  
pp. 1216-1221
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
System Design ◽  
Interconnection Networks ◽  
Network System ◽  
Multistage Interconnection Networks ◽  
Number Of Layers ◽  
Neural Network System ◽  
Improved Performance ◽  
Number Of Iterations

fforts have been made to examine and study different path and multi-path Multistage Interconnection Networks (MIN) possessing regular or irregular topology. Numerous strategies for establishing fault-tolerance in MINs have also been studied. These studies have provided us help to understand the strength and weakness of the existing static and dynamic and regular and irregular MINs. Application of Neural Networks leads to the development of MINs with improved performance and study of its Reliability In this paper ANN based system has been developed which will help in the study of metrics required for enhancing and predicting the reliability of MINs. In this paper Number of iterations are conducted to improve the ANN based system to predict the reliability of MINs by changing the number of neurons and the number of layers.

Application of Neural Network in Letter Recognition Using the Perceptron Method

2021 ◽  
Vol 11 (02) ◽  
pp. 53-65
Author(s):  
Bagus Suteja
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Letter Recognition ◽  
Network System ◽  
Learning Method ◽  
Neural Network System

Modeling with neural networks is the learning and adjustment of an object. The perceptron method is a learning method with supervision in a neural network system. In designing a neural network that needs to be considered is the number of specifications that will be identified. A neural network consists of a number of neurons and a number of inputs. To identify some letters, it takes several neurons to distinguish them. These neurons will generate a combination value that is used to identify the letters. so that the resulting network must have parameters that can be set by changing through the rules of learning with supervision.

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