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Neural network modeling of time-dependent creep deformations in masonry structures

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Abstract

Stresses and deformations in concrete and masonry structures can be significantly altered by creep. Thus, neglecting creep could result in un-conservative design of new structures and/or underestimation of the level of its effect on stress redistribution in existing structures. Brickwork has substantial creep strain that is difficult to predict because of its dependence on many uncontrolled variables. Reliable and accurate prediction models for the long-term, time-dependent creep deformation of brickwork structures are needed. Artificial intelligence techniques are suitable for such applications. A model based on radial basis function neural networks (RBFNN) is proposed for predicting creep and is compared to a multi-layer perceptron neural network (MLPNN) model recently developed for the same purpose. Accurate prediction of creep was achieved due to the simple architecture and fast training procedure of RBFNN model especially when compared to MLPNN model. The RBFNN model shows good agreement with experimental creep data from brickwork assemblages collected over the last 15 years.

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Abbreviations

AI:

Artificial intelligence

ANN:

Artificial neural networks

BiasJack(PE J ):

Jackknife bias for the prediction error of the creep compliance

d :

Distance from the center of the radial basis function (a measure of the Gaussian curve spread)

E(t0):

Material modulus of elasticity at time of load application t 0

J(t, t0):

Creep compliance between time t 0 and t

N :

Number of basis functions

n :

Total number of observations in the compliance matrix

PE J :

Prediction error in creep compliance

\( {\text{PE}}_{J}^{ - i} \) :

Prediction error calculated for the Jackknife reduced compliance matrix

J x :

Experimentally determined creep compliance

J p :

Predicted creep compliance

RH:

Relative humidity

RBFNN:

Radial basis function neural networks

SEJack(PE J ):

Jackknife standard error for the prediction error of the creep compliance

t :

Time of creep prediction

t 0 :

Time of load application

W 0 :

Bias parameter

W j :

Weight vectors at the output layer of the RBFNN

x :

Input parameter of the radial basis function

Y (x):

Output parameter of the radial basis function

\( \varepsilon_{\text{cr}} (t,t_{0} ) \) :

Creep strain between time t 0 and t

ε(t 0):

Instantaneous strain at time t 0

φ :

Radial basis function

ϕ(t, t0):

Creep coefficient between time t 0 and t

λ(t0):

A constant for creep prediction using modified Maxwell model

μ :

Center of the Gaussian function

σ :

Sustained stress

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Acknowledgments

This research is supported by (1) research grant to the third author from the Natural Science and Engineering Research Council (NSERC) of Canada, (2) The research grant for the first author from Smart Engineering Research Group, University Kebangsaan Malaysia. Special thanks to Mr. Dan Tilleman from the University of Calgary for performing the experimental tests for brickwork creep testing.

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Authors and Affiliations

  1. Civil and Structural Engineering Department, University Kebangsaan Malaysia, Bangi, Malaysia

    Ahmed El-Shafie

  2. Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada

    T. Abdelazim

  3. Department of Electrical and Computer Engineering, Royal Military College of Canada, Kingston, ON, Canada

    A. Noureldin

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  1. Ahmed El-Shafie
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  2. T. Abdelazim
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  3. A. Noureldin
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Correspondence to Ahmed El-Shafie.

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El-Shafie, A., Abdelazim, T. & Noureldin, A. Neural network modeling of time-dependent creep deformations in masonry structures. Neural Comput & Applic 19, 583–594 (2010). https://doi.org/10.1007/s00521-009-0318-3

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