Prediction of Target Displacement of Reinforced Concrete Frames Using Artificial Neural Networks

Iman Kameli; Mahmoud Miri; Ali Raji

doi:10.4028/www.scientific.net/AMR.255-260.2345

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Experimental Study on Three-Dimensional Base-Isolated Model with 3DIB
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The Influence of Column-to-Beam Strength Ratio on Seismic Performance of Concrete Filled Tube Moment-Resistant Frames
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Dynamic Characteristics of Base-Isolated Steel Frame under Seismic Ground Motion
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Prediction of Target Displacement of Reinforced Concrete Frames Using Artificial Neural Networks
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Correlation between Damage Indexes in Knee Bracing Frames Subjected to Earthquakes
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 255-260Prediction of Target Displacement of Reinforced...

Prediction of Target Displacement of Reinforced Concrete Frames Using Artificial Neural Networks

Abstract:

In this paper, the application of artificial neural network (ANN) in predicting seismic response of reinforced concrete (RC) frames with masonry infilled walls is investigated. The objective of this research is to predict roof displacement and base shear (ANN outputs) in the target displacement. The total of 855 database were prepared for modeling neural network using finite element method (FEM) by changing six parameters (the input parameters of ANN) including the number of bays, the number of stories, thickness of masonry infill, infilled wall ratio, existence of soft story and design spectral acceleration. A training set of 513 prepared database were used as training data and the validation set of 342 database were used as validation data in the next step. In the present study, two ANNs were trained; a multilayer perseptron (MLP) with Levenberg–Marquardt (LM) back propagation algorithms and a Radial Basis function (RBF), both with different structures and the best structure for each of them was obtained. The performance of ANNs was evaluated using mean square error (MSE) and correlation coefficient (R²) criteria. Results indicate that using both MLP and RBF ANNs for predicting target displacement have been appropriate and have low error as well as high speed. Furthermore, RBF network has a higher speed in training process of data compared to MLP network.

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Periodical:

Advanced Materials Research (Volumes 255-260)

Pages:

2345-2349

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.255-260.2345

Citation:

Cite this paper

Online since:

May 2011

Authors:

Iman Kameli, Mahmoud Miri, Ali Raji

Keywords:

Masonry Infills, Neural Network (NN), Reinforced Concrete Frame, Target Displacement

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References

[1] Dolsek, M., Fajfar, P., "The effect of masonry infills on the seismic response of a four storey reinforced concrete frame_a deterministic assessment", Eng Struct30 (2008).

DOI: 10.1016/j.engstruct.2008.01.001

Google Scholar

[2] Paulay, T. priestely, M. J. N. "Seismic Design of Reinforced Concrete and Masonry Buildings", John wiley & Sons, Inc, New york, NY, USA ( 1992).

Google Scholar

[3] Ghaboussi, J. Garett, J.H. Wu, X. "Knowledge-based modeling of material behaviour with neural networks", J.Engng Mech., ASCE 117,132-153 (1991).

Google Scholar

[4] Li, Q.S. Liu, D.K. Fang, J.Q. Jeary, A.P. Wong, C.K. "damping in buildings: its neural network model and AR model", Engineering Structures 221216-1223 (2000).

DOI: 10.1016/s0141-0296(99)00050-4

Google Scholar

[5] Arslan MH An evaluation of effective design parameters on earthquake performance of RC buildings using neural networks Engineering Structures 32 1888_1898 (2010).

DOI: 10.1016/j.engstruct.2010.03.010

Google Scholar

[6] Standard No. 2800, Iranian Code of Practice for Seismic Resistant Design of Building. 3rd Revision, Building and Housing Research Centre, Iran. (2005).

Google Scholar

[7] Issue No. 120, (Iranian Concrete Code (ABA)) Iranian Management Organization, 9th edition,Tehran, Iran (2007).

Google Scholar

[8] International Institute of Earthquake Engineering and Seismology, Guideline for Seismic Rehabilitation of Existing Building, 1, Tehran, Iran (2002).

Google Scholar

[9] Federal Emergency Management Agency. November Prestandard and Commentary for the Seismic Rehabilitation of Buildings. FEMA-356. Washington, DC (2000).

Google Scholar

[10] NZSEE. Assessment and improvement of the structural performance of buildings in earthquakes. Study Group Draft. New Zealand Society for Earthquake Engineering; (2005).

Google Scholar

[11] Caglar N, Elmas M, Neural networks in 3-dimensional dynamic analysisof reinforced concrete buildings, Construction and Building Materials 22 788–80008 (2008).

DOI: 10.1016/j.conbuildmat.2007.01.029

Google Scholar