Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30455
Neural Network Evaluation of FRP Strengthened RC Buildings Subjected to Near-Fault Ground Motions having Fling Step

Authors: Alireza Mortezaei, Kimia Mortezaei


Recordings from recent earthquakes have provided evidence that ground motions in the near field of a rupturing fault differ from ordinary ground motions, as they can contain a large energy, or “directivity" pulse. This pulse can cause considerable damage during an earthquake, especially to structures with natural periods close to those of the pulse. Failures of modern engineered structures observed within the near-fault region in recent earthquakes have revealed the vulnerability of existing RC buildings against pulse-type ground motions. This may be due to the fact that these modern structures had been designed primarily using the design spectra of available standards, which have been developed using stochastic processes with relatively long duration that characterizes more distant ground motions. Many recently designed and constructed buildings may therefore require strengthening in order to perform well when subjected to near-fault ground motions. Fiber Reinforced Polymers are considered to be a viable alternative, due to their relatively easy and quick installation, low life cycle costs and zero maintenance requirements. The objective of this paper is to investigate the adequacy of Artificial Neural Networks (ANN) to determine the three dimensional dynamic response of FRP strengthened RC buildings under the near-fault ground motions. For this purpose, one ANN model is proposed to estimate the base shear force, base bending moments and roof displacement of buildings in two directions. A training set of 168 and a validation set of 21 buildings are produced from FEA analysis results of the dynamic response of RC buildings under the near-fault earthquakes. It is demonstrated that the neural network based approach is highly successful in determining the response.

Keywords: Neural Network, Seismic Evaluation, FRP, near-fault ground motion

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1415


[1] P. Somerville, “The characteristics and quantification of near-fault ground motion”, Proceedings of the FHWA/NCEER Workshop on the National Representation of Seismic Ground Motion for New and Existing Highway Facilities. Burlingame, California, May 1997.
[2] J. F. Hall, T. H. Heaton, M. W. Halling and D. J. Wald, “Near-source ground motion and its effects on flexible buildings”, Earthquake Spectra 1995, 11: 569-605.
[3] P. Somerville, “Characterization of near field ground motions””, U.S.-Japan Workshop: Effects of Near-Field Earthquake Shaking, San Francisco, March 2000.
[4] W. D. Iwan, M. A. Moser and C. Y. Peng, “Some observations on strong-motion earthquake measurements using a digital accelerograph", Bulletin of the Seismological Society of America 1985; 75: 1225–1246.
[5] W. D. Iwan and X. D. Chen, “Important near-field ground motion data from the Landers earthquake”, In Proceedings of the 10th European Conference on Earthquake Engineering, Vienna, Austria, 1994.
[6] A. Shepherd, Second-Order Methods for Neural Networks Fast andReliable Training Methods for Multi-Layer Perceptrons, ISBN 3-540-76100-4 Springer-Verlag Berln Heidelberg, 1997, New York.
[7] A. Mortezaei, H. R. Ronagh and A. Kheyroddin, “Seismic evaluation of FRP strengthened RC buildings subjected to near-fault ground motions having fling step”,Composite Structures, 92(5): 1200-1211.