An evaluation of effective design parameters on earthquake performance of RC buildings using neural networks

Abstract The extent of building damage is related to the features of the structural system, which have many parameters. In particular, it is difficult to determine the extent to which structural parameters affect structural performance to identify the main parameters that may cause damage. In the present study, changes in the quality of a load-bearing system and reinforced concrete (RC) structure materials during an earthquake were determined. The related structural parameters were determined by considering the structural damage parameters observed in earthquakes: concrete compressive strength, yield and ultimate strength of steel, transverse reinforcement, infill wall ratio, short column, strong column–weak beam, and shear wall ratio. A total of 256 RC buildings with between 4 and 7 floors were modeled, and pushover analysis was applied to each to obtain the building capacity curves. A performance assessment was performed predicated on the basic criteria of the Turkish Earthquake Code (TEC-2007), which was revised in parallel with FEMA-356. In addition, the influence of the structural parameters was determined using a set of Artificial Neural Network (ANN) algorithms, and a parametric study was performed accordingly. The load-bearing system and material were discussed by matching the findings obtained from the study with the documented damage from previous earthquakes. The effect of each parameter tested in this study had various affecting ratios on the earthquake performance of the structure. It was found that shear wall ratio and short column formation are the most significant structural components that affect performance. The compressive strength of concrete and transverse reinforcement were determined to be the least significant parameters. In addition, the ANN determined the structural performance with quite satisfactory rate. The earthquake performance estimation percentages of the selected ANN algorithms varied between 91.68% and 98.47% depending on the algorithm type and other parameters of the ANN modeling.

[1]  John B. Mander,et al.  SEISMIC DESIGN OF BRIDGE PIERS , 1984 .

[2]  Thomas Paulay An estimation of displacement limits for ductile systems , 2002 .

[3]  Andrew S. Whittaker,et al.  Performance of reinforced concrete buildings during the August 17, 1999 Kocaeli, Turkey earthquake, and seismic design and construction practise in Turkey , 2003 .

[4]  Mehmet Inel,et al.  Effects of plastic hinge properties in nonlinear analysis of reinforced concrete buildings , 2006 .

[5]  M. Hakan Arslan,et al.  Application of ANN to evaluate effective parameters affecting failure load and displacement of RC buildings , 2009 .

[6]  G C Lee,et al.  NEURAL NETWORKS TRAINED BY ANALYTICALLY SIMULATED DAMAGE STATES , 1993 .

[7]  James L. Rogers,et al.  SIMULATING STRUCTURAL ANALYSIS WITH NEURAL NETWORK , 1994 .

[8]  Mehmet Inel Modeling ultimate deformation capacity of RC columns using artificial neural networks , 2007 .

[9]  M. H. Arslan,et al.  What is to be learned from damage and failure of reinforced concrete structures during recent earthquakes in Turkey , 2007 .

[10]  Thomas Paulay,et al.  The displacement capacity of reinforced concrete coupled walls , 2002 .

[11]  Muhammad N. S Hadi Neural networks applications in concrete structures , 2003 .

[12]  Matjaž Dolšek,et al.  The effect of masonry infills on the seismic response of a four-storey reinforced concrete frame — a deterministic assessment , 2008 .

[13]  Piotr Omenzetter,et al.  Prediction of seismic-induced structural damage using artificial neural networks , 2009 .

[14]  W M Jenkins APPROXIMATE ANALYSIS OF STRUCTURAL GRILLAGES USING A NEURAL NETWORK. , 1997 .

[15]  Mehmet Metin Kose,et al.  Parameters affecting the fundamental period of RC buildings with infill walls , 2009 .

[16]  Hayri Baytan Ozmen,et al.  Re-evaluation of building damage during recent earthquakes in Turkey , 2008 .

[17]  Mehmet M Kose Prediction of transfer length of prestressing strands using neural networks , 2007 .

[18]  Fereidoun Amini,et al.  Neural Network for Structure Control , 1995 .

[19]  Gary R. Consolazio Iterative Equation Solver for Bridge Analysis Using Neural Networks , 2000 .

[20]  A. Tashakori,et al.  Optimum design of cold-formed steel space structures using neural dynamics model , 2002 .

[21]  Kevin Swingler,et al.  Applying neural networks - a practical guide , 1996 .

[22]  Mervyn J. Kowalsky,et al.  Aspects of drift and ductility capacity of rectangular cantilever structural walls , 1998 .

[23]  Yüksel Özbay,et al.  Prediction of force reduction factor (R) of prefabricated industrial buildings using neural networks , 2007 .

[24]  Rui Pinho,et al.  Detailed assessment of structural characteristics of Turkish RC building stock for loss assessment models , 2008 .

[25]  Sundaramoorthy Rajasekaran,et al.  Predictions of design parameters in civil engineering problems using SLNN with a single hidden RBF neuron , 2002 .

[26]  Zekeriya Polat,et al.  Yield state investigation of reinforced concrete frames from a new point of view , 2005 .

[27]  Amr S. Elnashai,et al.  Analysis of the damage potential of the Kocaeli (Turkey) earthquake of 17 August 1999 , 2000 .

[28]  Triantafyllos Makarios,et al.  Optimum definition of equivalent non-linear SDF system in pushover procedure of multistory r/c frames , 2005 .

[29]  Abhijit Mukherjee,et al.  Prediction of Buckling Load of Columns Using Artificial Neural Networks , 1996 .

[30]  김지은,et al.  모멘트-곡률 관계에 기초한 철근콘크리트 보의 비선형 해석 = Nonlinear analysis of RC beams based on moment-curvature relations , 2002 .

[31]  Ahmet Yakut,et al.  Re-examination of damage distribution in Adapazarı: Structural considerations , 2005 .

[32]  Adem Doǧangün,et al.  Performance of reinforced concrete buildings during the May 1, 2003 Bingöl Earthquake in Turkey , 2004 .

[33]  Guido Bugmann,et al.  NEURAL NETWORK DESIGN FOR ENGINEERING APPLICATIONS , 2001 .

[34]  Michel Bruneau,et al.  Building damage from the Marmara, Turkey earthquake of August 17, 1999 , 2002 .

[35]  R. Park,et al.  Stress-Strain Behavior of Concrete Confined by Overlapping Hoops at Low and High Strain Rates , 1982 .