Seismic reliability assessment of RC structures including soil-structure interaction using wavelet weighted least squares support vector machine

An efficient metamodeling framework in conjunction with the Monte-Carlo Simulation (MCS) is introduced to reduce the computational cost in seismic reliability assessment of existing RC structures. In order to achieve this purpose, the metamodel is designed by combining weighted least squares support vector machine (WLS-SVM) and a wavelet kernel function, called wavelet weighted least squares support vector machine (WWLS-SVM). In this study, the seismic reliability assessment of existing RC structures with consideration of soil–structure interaction (SSI) effects is investigated in accordance with Performance-Based Design (PBD). This study aims to incorporate the acceptable performance levels of PBD into reliability theory for comparing the obtained annual probability of non-performance with the target values for each performance level. The MCS method as the most reliable method is utilized to estimate the annual probability of failure associated with a given performance level in this study. In WWLS-SVM-based MCS, the structural seismic responses are accurately predicted by WWLS-SVM for reducing the computational cost. To show the efficiency and robustness of the proposed metamodel, two RC structures are studied. Numerical results demonstrate the efficiency and computational advantages of the proposed metamodel for the seismic reliability assessment of structures. Furthermore, the consideration of the SSI effects in the seismic reliability assessment of existing RC structures is compared to the fixed base model. It shows which SSI has the significant influence on the seismic reliability assessment of structures.

[1]  Paolo Gardoni,et al.  Probabilistic Demand Models and Fragility Curves for Reinforced Concrete Frames , 2006 .

[2]  Amr S. Elnashai,et al.  The effect of material and ground motion uncertainty on the seismic vulnerability curves of RC structure , 2006 .

[3]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[4]  Luis Esteva,et al.  Reliability functions for earthquake resistant design , 2001, Reliab. Eng. Syst. Saf..

[5]  A. Basudhar,et al.  Adaptive explicit decision functions for probabilistic design and optimization using support vector machines , 2008 .

[6]  J. Lysmer,et al.  Finite Dynamic Model for Infinite Media , 1969 .

[7]  Hongbo Zhao Slope reliability analysis using a support vector machine , 2008 .

[8]  Li Zhang,et al.  Wavelet support vector machine , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Manolis Papadrakakis,et al.  Innovative seismic design optimization with reliability constraints , 2008 .

[10]  J. G. Macgregor,et al.  Variability of Mechanical Properties of Reinforcing Bars , 1985 .

[11]  Jorge E. Hurtado,et al.  Filtered importance sampling with support vector margin: A powerful method for structural reliability analysis , 2007 .

[12]  Bruce R. Ellingwood,et al.  Seismic Risk Assessment of Gravity Load Designed Reinforced Concrete Frames Subjected to Mid-America Ground Motions , 2009 .

[13]  Gaviphat Lekutai,et al.  Adaptive Self-Tuning Neuro Wavelet Network Controllers , 1997 .

[14]  Hao Zhang,et al.  A support vector density-based importance sampling for reliability assessment , 2012, Reliab. Eng. Syst. Saf..

[15]  R. Park,et al.  Flexural Members with Confined Concrete , 1971 .

[16]  Ricardo O. Foschi,et al.  SEISMIC STRUCTURAL RELIABILITY USING DIFFERENT NONLINEAR DYNAMIC RESPONSE SURFACE APPROXIMATIONS , 2009 .

[17]  Masanobu Shinozuka,et al.  Simulation of Nonstationary Random Process , 1967 .

[18]  Qian Liu,et al.  Weighted least squares support vector machine local region method for nonlinear time series prediction , 2010, Appl. Soft Comput..

[19]  Kuan-Yu Chen,et al.  Forecasting systems reliability based on support vector regression with genetic algorithms , 2007, Reliab. Eng. Syst. Saf..

[20]  Ricardo O. Foschi,et al.  Structural optimization for performance-based design in earthquake engineering: Applications of neural networks , 2009 .

[21]  J. G. Macgregor,et al.  Statistical Descriptions of Strength of Concrete , 1979 .

[22]  K. P. Jaya,et al.  Embedded foundation in layered soil under dynamic excitations , 2002 .

[23]  Enrico Zio,et al.  Failure and reliability prediction by support vector machines regression of time series data , 2011, Reliab. Eng. Syst. Saf..

[24]  Johan A. K. Suykens,et al.  Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.

[25]  A. Ang,et al.  Mechanistic Seismic Damage Model for Reinforced Concrete , 1985 .

[26]  Qi Wu,et al.  Hybrid model based on wavelet support vector machine and modified genetic algorithm penalizing Gaussian noises for power load forecasts , 2011, Expert Syst. Appl..

[27]  José Alí Moreno,et al.  Fast Monte Carlo reliability evaluation using support vector machine , 2002, Reliab. Eng. Syst. Saf..

[28]  John P. Wolf,et al.  Some cornerstones of dynamic soil-structure interaction , 2002 .

[29]  K. Phoon,et al.  Characterization of Geotechnical Variability , 1999 .

[30]  Barbara Ferracuti,et al.  Response Surface with random factors for seismic fragility of reinforced concrete frames , 2010 .

[31]  L. G. Jaeger,et al.  Dynamics of structures , 1990 .

[32]  Ia.S. Ufliand Oscillations of elastic bodies with finite conductivity in a transverse magnetic field , 1963 .

[33]  李洪双,et al.  SUPPORT VECTOR MACHINE FOR STRUCTURAL RELIABILITY ANALYSIS , 2006 .

[34]  Joel P. Conte,et al.  Two-Dimensional Nonlinear Earthquake Response Analysis of a Bridge-Foundation-Ground System , 2008 .

[35]  Zhiwei Guo,et al.  Application of Least Squares Support Vector Machine for Regression to Reliability Analysis , 2009 .

[36]  Wei Wang,et al.  Reliability analysis using radial basis function networks and support vector machines , 2011 .

[37]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[38]  Richard Kronland-Martinet,et al.  Analysis of Sound Patterns through Wavelet transforms , 1987, Int. J. Pattern Recognit. Artif. Intell..

[39]  Bo-Suk Yang,et al.  Wavelet support vector machine for induction machine fault diagnosis based on transient current signal , 2008, Expert Syst. Appl..

[40]  Esin Dogantekin,et al.  An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier , 2011, Expert Syst. Appl..

[41]  Michael H. Scott,et al.  Krylov Subspace Accelerated Newton Algorithm: Application to Dynamic Progressive Collapse Simulation of Frames , 2010 .

[42]  Gavin C. Cawley,et al.  Fast exact leave-one-out cross-validation of sparse least-squares support vector machines , 2004, Neural Networks.

[43]  Gavin C. Cawley,et al.  Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[44]  Bruce R. Ellingwood,et al.  Seismic fragilities for non-ductile reinforced concrete frames – Role of aleatoric and epistemic uncertainties , 2010 .

[45]  李洪双,et al.  SUPPORT VECTOR MACHINE FOR STRUCTURAL RELIABILITY ANALYSIS , 2006 .

[46]  Murat Saatcioglu,et al.  Strength and Ductility of Confined Concrete , 1992 .

[47]  K. Grace,et al.  Probabilistic Reliability: An Engineering Approach , 1968 .

[48]  B. Ellingwood,et al.  Fragility assessment of building structural systems in Mid‐America , 2007 .

[49]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[50]  James L Noland,et al.  Computer-Aided Structural Engineering (CASE) Project: Decision Logic Table Formulation of ACI (American Concrete Institute) 318-77 Building Code Requirements for Reinforced Concrete for Automated Constraint Processing. Volume 1. , 1986 .

[51]  Qi Wu,et al.  Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system , 2010, J. Comput. Appl. Math..

[52]  Mohsen Khatibinia,et al.  Optimal design of structures for earthquake loads by a hybrid RBF-BPSO method , 2008 .

[53]  S. Gholizadeh,et al.  OPTIMAL DESIGN OF STRUCTURES SUBJECTED TO TIME HISTORY LOADING BY SWARM INTELLIGENCE AND AN ADVANCED METAMODEL , 2009 .

[54]  Bruce R. Ellingwood,et al.  The Role of Fragility Assessment in Consequence-Based Engineering , 2005 .

[55]  J. C. Cluley,et al.  Probabilistic Reliability: an Engineering Approach , 1968 .

[56]  Saeed Rahati Quchani,et al.  Evolutionary model selection in a wavelet-based support vector machine for automated seizure detection , 2011, Expert Syst. Appl..

[57]  Paolo Gardoni,et al.  Seismic Fragility and Confidence Bounds for Gravity Load Designed Reinforced Concrete Frames of Varying Height , 2008 .

[58]  He Ting-tin Slope reliability analysis using support vector machine , 2013 .

[59]  M. M. Yao,et al.  Dynamic wave-soil-structure interaction analysis in the time domain , 2005 .