Seismic Fragility Using Response Surface Methodology
Essay by adityajj • February 4, 2018 • Thesis • 6,361 Words (26 Pages) • 851 Views
Seismic Fragility Analysis Using Response Surface Methodology
Course Project Report
by
Aditya Jhunjhunwala
(Roll No. 130040006)
[pic 1]
Department of Civil Engineering
Indian Institute of Technology Bombay
Mumbai 400076 (India)
Abstract
Seismic risk assessment of buildings is important for calculating the loss of functionality of the building in the event of an earthquake. Seismic fragility functions for a building are an important part of the process of risk assessment as they present the probability of a damage level at given intensity of earthquake.
Conventional methods for developing fragility functions are based on use of Monte Carlo simulation with a non-linear model of the building. Monte Carlo technique usually requires a relatively large number of simulations in order to obtain a sufficiently reliable estimate of the fragilities, and it becomes computationally impractical to simulate the required thousands of non-linear analyses. The use of Response Surface Methodology in connection with the Monte Carlo simulations simplifies the process. A response surface predicts the structural response calculated from complex non-linear dynamic analyses. Computational cost required in a Monte Carlo simulation will be significantly reduced since the simulation is performed on a polynomial response surface function, rather than a complex non-linear model.
The methodology is applied to develop fragility functions of a low rise 2D concrete moment resisting frame detailed as per modern seismic code i.e. as per capacity design principles. Response surface equations for predicting peak drift are generated and used in the Monte Carlo simulation.
Table of Contents
Chapter 1 Introduction 1
Chapter 2 Conventional Fragility Functions 2
2.1 Analytical Fragility Curves 3
2.1.1 Elastic Spectral Analysis Method 4
2.1.2 Nonlinear Static Analysis Method 4
2.1.3 Non-linear Time History Analysis Method 5
2.2 Probabilistic Seismic Demand Model 6
2.3 Limit States or Capacity 7
2.4 Issues with the Conventional Method 7
Chapter 3 Metamodels 8
3.1 Metamodels 8
3.2 Experimental Design 10
3.2.1 Factorial Design 11
3.2.2 Central Composite Design (CCD) 12
3.3 Model Choice and Model Fitting 13
3.3.1 Response Surfaces 13
Chapter 4 Model and Analysis 15
4.1 Model Details 15
4.2 Analysis Method 16
Chapter 5 Results and Discussions 21
References 29
List of Figures
Figure 2-1 : Example fragility curve [1] 2
Figure 2-2 : Demand and capacity spectra probabilistic distribution 5
Figure 2-3 : PSDM in lognormal space [1] 6
Figure 3-1: design for two and three factors ( are the coded variables) 11[pic 2][pic 3]
Figure 4-1 : Details of sections of the moment resisting frame 15
Figure 4-2 : Process of computing seismic fragility function using metamodel 17
Figure 4-3 : Input data points 18
Figure 4-4 : Response surface metamodel. The error term is not depicted here, only is shown 20[pic 4]
Figure 5-1 : for 25 input data points for 30 ground motions 21[pic 5]
Figure 5-2 : in log space 21[pic 6]
Figure 5-3 : vs. 23[pic 7][pic 8]
Figure 5-4 : vs. input parameters obtained from response surface model 25[pic 9]
Figure 5-5 : Fragility functions for different damage state 26
Figure 5-6 : Actual vs. predicted 27[pic 10]
Figure 5-7 : Sorted actual vs. sorted predicted 27[pic 11]
List of Tables
Table 3-1 : Steps involved in development of metamodel 9
Table 3-2 : Techniques for metamodeling 9
Table 3-3 : Combination of techniques for metamodeling 9
Table 4-1 : Input parameters 18
Table 4-2 : Ground motions detail 19
Table 4-3 : IDR values for various limit states of low-rise moment resisting buildings designed and detailed as per modern seismic codes (Capacity design) [2] 20
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