Application of artificial neural networks to evaluation of ultimate strength of steel panels

Yang, Pu; Ehsan, Mehdi

doi:10.1016/j.engstruct.2005.12.009

Cited by 73 publications

(29 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, it offers some advantages such as self-learning, noise-tolerance, and good predicting power. Therefore, this type of neural network has been used extensively and successfully in various applications (e.g., [18][19][20]). For the sake of its simplicity and effectiveness, this study also chooses the back-propagation network model as the tool for estimating peak ground acceleration in the specified seismic problem.…”

Section: Fundamentals Of Neural Network Model and Genetic Algorithmmentioning

confidence: 99%

Neural computing with genetic algorithm in evaluating potentially hazardous metropolitan areas result from earthquake

Kerh

Gunaratnam

Chan

2009

Neural Comput & Applic

View full text Add to dashboard Cite

In this study, neural network models improved by genetic algorithm were employed to estimate peak ground acceleration (PGA) at seven metropolitan areas in the island of Taiwan, which is frequently subject to earthquakes. By considering a series of historical seismic records, and using the seismic design value in the current building code as the evaluation criteria, two metropolitan areas, Taichung and Chiayi, were identified by computational results as having higher estimated horizontal PGAs than the recommended design values. The approach implemented in this study provides a new and good basis for solving this type of seismic problems in the region studied.

show abstract

Section: Fundamentals Of Neural Network Model and Genetic Algorithmmentioning

confidence: 99%

Neural computing with genetic algorithm in evaluating potentially hazardous metropolitan areas result from earthquake

Kerh

Gunaratnam

Chan

2009

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…This neural network model has various engineering applications because of its simplicity and effectiveness. The detailed principle, operational logic routine, and transfer function of this multi-layered neural network can be found in numerous related studies [30][31][32]. The basic equations for the back-propagation neural network model can be written as follows:…”

Section: Neural Network Modelmentioning

confidence: 99%

Incorporating global search capability of a genetic algorithm into neural computing to model seismic records and soil test data

Kerh

Mosallam

2015

Neural Comput & Applic

View full text Add to dashboard Cite

In this study, a genetic algorithm with global searching capability was incorporated into a neural network calculating process to obtain a highly reliable model for predicting peak ground acceleration, which is the key element in evaluating earthquake response and in establishing a seismic design standard. In addition to three seismic parameters (i.e. local magnitude, focal distance, and epicentre depth), this study included two geological conditions (i.e. standard penetration test value and shearwave velocity) in the input to reflect the site response adequately. Based on the earthquake records and soil test data from 86 checking stations, within 24 seismic subdivision zones in the Taiwan area, the computational results show that using a combination of a neural network and genetic algorithm can achieve a higher performance compared with solely using a neural network model. Furthermore, a weight-based model was developed for predicting peak ground acceleration at an unmonitored site to represent each subdivision zone. The results show that three subdivision zones have higher horizontal peak ground accelerations than the seismic design value as required in the building code. The obtained information might be helpful in relevant engineering applications for the studied region, and the proposed method for treating this type of nonlinear seismic data might be applicable in other areas of interest worldwide.

show abstract

“…The artificial neural network (ANN) techniques, however, present a reliable and simplified modelling procedure for studying the behaviour of joints in fire where various parameters can be considered in the modelling process. The applicability and usefulness of neural networks in studying the behaviour of structural steel elements (AlKhaleefi, 2002;Sakla, 2004;Guzelbey et al, 2006a;Guzelbey et al, 2006b;Pala, 2006;Pala and Caglar, 2006;Pu and Mesbahi, 2006;Hozjan et al, 2007;AlJabri and Al-Alawi, 2007;Shahin and Elchalakani, 2008;Al-Jabri et al, 2009) has increased in recent years. This mainly due to their simplicity, user friendly, and can overcome many of the shortcomings of traditional regression techniques, analyzing noisy data, incomplete data and data with outliers.…”

Section: Introductionmentioning

confidence: 99%

An advanced ANN model for predicting the rotational behaviour of semi-rigid composite joints in fire using the back-propagation paradigm

Al‐Jabri

Al-Alawi

2010

International Journal of Steel Structures

View full text Add to dashboard Cite

This paper describes an artificial neural networking (ANN) model developed to predict the behaviour of semi-rigid composite joints at elevated temperature. Three different semi-rigid composite joints were selected, two flexible end-plates and one flush end-plate. Seventeen different parameters were selected as input parameters representing the geometrical and mechanical properties of the joints as well as the joint's temperature and the applied loading, and used to model the rotational capacity of the joints with increasing temperatures. Data from experimental fire tests were used for training and testing the ANN model. Results from nine experimental fire tests were evaluated with a total of 280 experimental cases. The results showed that the R 2 value for the training and testing sets were 0.998 and 0.97, respectively. This indicates that results from the ANN model compared well with the experimental results demonstrating the capability of the ANN simulation techniques in predicting the behaviour of semi-rigid composite joints in fire. The described model can be modified to study other important parameters that can have considerable effect on the behaviour of joints at elevated temperatures such as temperature gradient, axial restraints, etc.

show abstract

Application of artificial neural networks to evaluation of ultimate strength of steel panels

Cited by 73 publications

References 8 publications

Neural computing with genetic algorithm in evaluating potentially hazardous metropolitan areas result from earthquake

Neural computing with genetic algorithm in evaluating potentially hazardous metropolitan areas result from earthquake

Incorporating global search capability of a genetic algorithm into neural computing to model seismic records and soil test data

An advanced ANN model for predicting the rotational behaviour of semi-rigid composite joints in fire using the back-propagation paradigm

Contact Info

Product

Resources

About