Development of Failure Probability Analysis Method for. Concrete Piers of Multi-span Continuous Bridges using

Similar documents
Generalization of 2-Corner Frequency Source Models Used in SMSIM

Co-ordinated s-convex Function in the First Sense with Some Hadamard-Type Inequalities

, g. Exercise 1. Generator polynomials of a convolutional code, given in binary form, are g. Solution 1.

REINFORCED CONCRETE. Reinforced Concrete Design. A Fundamental Approach - Fifth Edition

Math 32B Discussion Session Week 8 Notes February 28 and March 2, f(b) f(a) = f (t)dt (1)

Chapter Gauss Quadrature Rule of Integration

MA10207B: ANALYSIS SECOND SEMESTER OUTLINE NOTES

Table of Content. c 1 / 5

Acceptance Sampling by Attributes

Lecture 1 - Introduction and Basic Facts about PDEs

f (x)dx = f(b) f(a). a b f (x)dx is the limit of sums

Expectation and Variance

Section 11.5 Estimation of difference of two proportions

MATH20812: PRACTICAL STATISTICS I SEMESTER 2 NOTES ON RANDOM VARIABLES

Method: Step 1: Step 2: Find f. Step 3: = Y dy. Solution: 0, ( ) 0, y. Assume

ANALYSIS AND MODELLING OF RAINFALL EVENTS

Applications of Definite Integral

On the Co-Ordinated Convex Functions

6.1 Definition of the Riemann Integral

University of Sioux Falls. MAT204/205 Calculus I/II

CHM Physical Chemistry I Chapter 1 - Supplementary Material

Composite Strut and Tie Model for Reinforced Concrete Deep Beams

Continuous Random Variables

A Brief Review on Akkar, Sandikkaya and Bommer (ASB13) GMPE

Gauss Quadrature Rule of Integration

Gauss Quadrature Rule of Integration

More Properties of the Riemann Integral

INTEGRATION. 1 Integrals of Complex Valued functions of a REAL variable

1 Probability Density Functions

Shear and torsion interaction of hollow core slabs

Precalculus Spring 2017

5 Probability densities

Normal Distribution. Lecture 6: More Binomial Distribution. Properties of the Unit Normal Distribution. Unit Normal Distribution

Section 4.4. Green s Theorem

20 MATHEMATICS POLYNOMIALS

New Expansion and Infinite Series

4 7x =250; 5 3x =500; Read section 3.3, 3.4 Announcements: Bell Ringer: Use your calculator to solve

University of Texas MD Anderson Cancer Center Department of Biostatistics. Inequality Calculator, Version 3.0 November 25, 2013 User s Guide

Hyers-Ulam stability of Pielou logistic difference equation

Applications of Definite Integral

38.2. The Uniform Distribution. Introduction. Prerequisites. Learning Outcomes

Songklanakarin Journal of Science and Technology SJST R1 Thongchan. A Modified Hyperbolic Secant Distribution

NUMERICAL INTEGRATION. The inverse process to differentiation in calculus is integration. Mathematically, integration is represented by.

Predict Global Earth Temperature using Linier Regression

Lecture 3 Gaussian Probability Distribution

MATH34032: Green s Functions, Integral Equations and the Calculus of Variations 1. 1 [(y ) 2 + yy + y 2 ] dx,

Estimation of Binomial Distribution in the Light of Future Data

AP Calculus AB Unit 4 Assessment

Problem. Statement. variable Y. Method: Step 1: Step 2: y d dy. Find F ( Step 3: Find f = Y. Solution: Assume

Student Activity 3: Single Factor ANOVA

Chapter 5 : Continuous Random Variables

Solving Radical Equations

Read section 3.3, 3.4 Announcements:

Delay Variability at Signalized Intersections

ASSESSING SPECTRAL SHAPE-BASED INTENSITY MEASURES FOR SIMPLIFIED FRAGILITY ANALYSIS OF MID-RISE REINFORCED CONCRETE BUILDINGS

Quadrature Rules for Evaluation of Hyper Singular Integrals

Hybrid Group Acceptance Sampling Plan Based on Size Biased Lomax Model

MATH Final Review

Time Truncated Two Stage Group Sampling Plan For Various Distributions

Some Aspects of Non-Orthogonal Stagnation-Point Flow towards a Stretching Surface

CHAPTER 4a. ROOTS OF EQUATIONS

On the Scale factor of the Universe and Redshift.

The steps of the hypothesis test

THIELE CENTRE. Linear stochastic differential equations with anticipating initial conditions

Application of the theory of compound cores for the assessment of stress pattern in the cross section of a strengthened beam column

1B40 Practical Skills

Construction and Selection of Single Sampling Quick Switching Variables System for given Control Limits Involving Minimum Sum of Risks

Formula for Trapezoid estimate using Left and Right estimates: Trap( n) If the graph of f is decreasing on [a, b], then f ( x ) dx

Chapter 4. Additional Variational Concepts

CS667 Lecture 6: Monte Carlo Integration 02/10/05

Green s Theorem. (2x e y ) da. (2x e y ) dx dy. x 2 xe y. (1 e y ) dy. y=1. = y e y. y=0. = 2 e

ON THE INEQUALITY OF THE DIFFERENCE OF TWO INTEGRAL MEANS AND APPLICATIONS FOR PDFs

Driving Cycle Construction of City Road for Hybrid Bus Based on Markov Process Deng Pan1, a, Fengchun Sun1,b*, Hongwen He1, c, Jiankun Peng1, d

(h+ ) = 0, (3.1) s = s 0, (3.2)

MARKOV MODEL: Analyzing its behavior for Uncertainty conditions

Math 113 Fall Final Exam Review. 2. Applications of Integration Chapter 6 including sections and section 6.8

Review of Calculus, cont d

Parabola and Catenary Equations for Conductor Height Calculation

Part I: Basic Concepts of Thermodynamics

THE EXISTENCE OF NEGATIVE MCMENTS OF CONTINUOUS DISTRIBUTIONS WALTER W. PIEGORSCH AND GEORGE CASELLA. Biometrics Unit, Cornell University, Ithaca, NY

#A42 INTEGERS 11 (2011) ON THE CONDITIONED BINOMIAL COEFFICIENTS

1 Bending of a beam with a rectangular section

Chapter 3 Single Random Variables and Probability Distributions (Part 2)

Review Topic 14: Relationships between two numerical variables

Lesson 1: Quadratic Equations

Probability-Based Seismic Assessments: Implementing Wide-Range Nonlinear Dynamic Analysis Methods

Line Integrals and Entire Functions

The Double Integral. The Riemann sum of a function f (x; y) over this partition of [a; b] [c; d] is. f (r j ; t k ) x j y k

STRENGTH AND FATIGUE LIFE OF CARBON/EPOXY LAMINATES UNDER BIAXIAL LOADING

Linear Inequalities: Each of the following carries five marks each: 1. Solve the system of equations graphically.

Chapter 9: Inferences based on Two samples: Confidence intervals and tests of hypotheses

ENGI 3424 Engineering Mathematics Five Tutorial Examples of Partial Fractions

AP CALCULUS Test #6: Unit #6 Basic Integration and Applications

DETERMINATION OF MECHANICAL PROPERTIES OF NANOSTRUCTURES WITH COMPLEX CRYSTAL LATTICE USING MOMENT INTERACTION AT MICROSCALE

Solutions to Assignment 1

THERMAL EXPANSION COEFFICIENT OF WATER FOR VOLUMETRIC CALIBRATION

arxiv: v1 [math.ca] 21 Aug 2018

The study of dual integral equations with generalized Legendre functions

Chapter 3. Vector Spaces

1 The Riemann Integral

Transcription:

Development o Filure Probbility Anlysis Method or Conrete Piers o Multi-spn Continuous Bridges using the Probbilisti Cpity Spetrum Method Je Shin CHOI, Je Kwn KIM ABSTRACT When erthqukes our, strutures sustin dmges. But it is impossible to estimte dmges extly whih our in the strutures beuse o the unertinty o mterils nd ground motions. Thereore, probbilisti methods or estimting dmges o the strutures hve been suggested ltely. In this study, probbilisti pity spetrum method expnding estblished pity spetrum method is suggested nd this method is pplied to lultion o the ilure probbility o the multi-spn ontinuous bridges. While estblished pity spetrum method doesn't relet the unertinty o mterils nd ground motions, probbilisti pity spetrum method onsiders diretly the unertinty o the strength o the onrete in the pity spetrum nd the unertinty o the ground motion in the demnd spetrum. While the perormne point ppers one point using estblished pity spetrum method, using probbilisti pity spetrum method we obtin the probbility o the perormne point. Thereore, the ilure probbility o the bridges is lulted when erthqukes hving speii return period our. keywords : Multi-spn Continuous Bridges, Reinored Conrete Piers, Probbilisti Cpity Spetrum Method, Filure Probbility, Perormne Point Je Shin CHOI, Grdute Student, Dept. o Civil, Urbn & Geosystem Engineering, Seoul Ntionl University, Seoul, 151-742 Je Kwn KIM, Proessor, Dept. o Civil, Urbn & Geosystem Engineering, Seoul Ntionl University, Seoul, 151-742

1. INTRODUCTION Strutures ontin the unertinty bout severl tors. And yet, the struturl nlysis whih hs been rried out till now doesn t relet tht inlusively. Atully, it is resonble to relet the unertinty in the struturl designs nd i the knowledge is tken dvntge o, eonomil eiieny nd serviebility re hieved. Thereore, onsidering the unertinty o the onrete nd ground motion, the method whih the ilure probbility o bridges is lulted with under erthqukes is suggested in this pper. 2. PROBABILISTIC CAPACITY SPECTRUM METHOD Figure 1. Proedure o Probbilisti Cpity Spetrum Method

2.1 THE DAMAGE STATES In the deinition o the dmge sttes o the onrete s piers, the existing dmge levels bsed on the urvture dutility re used nd tht is deined s ollows. µ φ φ m = (1) φ y Where, φ : The Curvture t the estimted ross-setion m φ : The Curvture. o the Yield y Dmge Stte Desription Curvture Dutility No Dmge Negligible µ < 1.0 φ Light Moderte Severe Collpse Light rking & Prtil splling Dmge minly t one side Dmge minly t two opposite sides Dmge through entire ross-setion 1.0 < µ φ < 3.0 3.0 < µ φ < 5.0 5.0 < µ φ < 13.0 13.0 < µ φ Tble 1. The Deinition o the Dmge Stte (Priestley,1994) 2.2 PROBABILISTIC CAPACITY SPECTRUM When the erthqukes our, the motions o the bridges n be divided into longitudinl nd trnsverse motions. In this study, two pity spetrums whih desribe longitudinl nd trnsverse motions o the bridges re onstruted.

The seleted model o the bridge hs the one ixed pier tht stnds in the middle o the bridge nd the ross-setion o the piers is n ext squre. Reerring to the longitudinl motion, the piers behve like the ntilever bem. Also,. the MDOF (Multiple-Degree-O- Freedom) is simpliied into SDOF (Single Degree-O-Freedom) bout the trnsverse motion by ssuming the deormtion shpe o the superstruture be equl to the sine urve. The pity urve o the pier is onstruted by the struturl nlysis. The pity urve o the longitudinl diretion ppers biliner (s shown in Figure 2.) nd tht o the trnsverse diretion pper tri-liner (s shown in Figure 3). P u P y αk e K e d y d u Figure 2. Biliner Cpity Curve o the Pier (Longitudinl diretion) P u K 3 P y K 2 K 1 d y d u Figure 3. Cpity Curve o the Pier (Trnsverse diretion) And this urve is trnsormed into pity spetrum using reltive equtions o the ADRS (Aelertion-Displement Response Spetrum) [M.Shinozuk, 2001]

The untion o the pity spetrum is desribed s ollows. S = F, I, H, W, S ) (2) ( e t d Where, F is the strength o the onrete, I e is eetive moment inerti o the pier, H is height o the pier nd W t is the weight o the superstrutures. The pity spetrum through the struturl nlysis desribes the men vlue beuse o using the verge vlue o the strength o the onrete. To desribe PDF (Probbility Density Funtion) o the pity spetrum, the eqution o the pity spetrum is ombined with the PDF o the strength o the onrete. I the PDF o the strength o the onrete is ssumed to hve norml distribution, the PDF o the pity spetrum is desribed using the PDF o the strength o the onrete nd is derived s ollowing proedures. F = 1 ( S, I, H, W, S ) (3) e t d 1 1 = (4) r 2 ( ) exp[ ( ) ] F σ 2π 2 σ 1 1 1 d = (5) σ 2π 2 σ ds r 2 ( s ) exp[ ( ) ] S Where, F : Rndom Vrible o the Strength o the Conrete : The Required Strength o the Conrete (Men Vlue o the Conrete ) r 2.3 PROBABILISTIC DEMAND SPECTRUM The shpe o the demnd spetrum ollows the stndrdized demnd spetrum o the UBC ode (s shown in Figure 4).

Figure 4. The Stndrdized Demnd Spetrum o the UBC ode Inelsti demnd spetrum is obtined by deresing elsti demnd spetrum using the redution oeiients (s shown in Figure 5) Figure 5. Inelsti Demnd Spetrum Beuse the stndrd demnd spetrum o the UBC ode doesn t relet the unertinty o the ground motion, the men nd stndrd devition o the ground elertion re derived through the hzrd nlysis bsed on the erthquke dt nd re pplied to the demnd spetrum by using the reltion o the ground elertion nd spetrl elertion. I the PDF o the ground elertion is ssumed to log-norml distribution, the PDF o the demnd spetrum is derived s ollowing proedures. g C = 1 ( S, SR ( orsr ), S ) (6) v d = 1 1 1 σ 2π 2 σ (7) ln ln m 2 ( ) exp[ ( ln( _ )) ] C

g 1 1 1 1 d = (8) ds 2 ( s ) exp[ ( ln( _ )) ] s σ 2π 2 σ ln ln m 2.4 CALCULATION OF THE FAILURE PROBABILITY The probbility o the perormne point existing t the speii displement is obtined s ollowing eqution. ps ( ) = d 0 sdmx 0 0 g ds s s g ds ds s s d (9) To lulte the ilure probbility, the dmge sttes deined by the urvture dutility t setion 2.1 re trnsormed into the spetrl displement nd the probbility o the perormne point within the rnge o the ollpse stte is dded. For exmple, i the rnge o the ollpse stte is between displement nd s d mx, the ilure probbility is derived s ollows. p sdmx = 0 sdmx 0 0 g ds ds s s d g ds ds s s d ()

3. APPLICATION & RESULTS Item Vlue(m) Cross Setion 2.5*2.5 Height 14 Tble 2. The Properties o the Bridge Return Period : 00 yers Item vlue Men ( g ) 0.12 Stndrd devition ( g ) 0.4 Tble 3. The Properties o the Erthquke 3.1 RESULTS Dmge Level Cumultive Probbility Light 9.6 Moderte 9.46 Severe 8.95 Collpse 2.25 Relibility Index β = 3. 48 Tble 4. Longitudinl Diretion

Dmge Level Cumultive Probbility Light 6.57 Moderte 5.63 Severe 3.52 Collpse 7 2.65 Relibility Index β = 5. 0 Tble 5. Trnsverse Diretion 3.2 COMPARISON Cross-Setion : 2.5m*2.5m, H=15m Dmge Level Cumultive Probbility Light 6.0 Severe 1.57 Relibility Index β = 3. 6 Tble 6. Longitudinl Diretion (H. N. Cho, 2002) Cltrns' Bridges Dmge Dt Dmge Level Cumultive Probbility Minor 1.468 Moderte 5 7.86 Mjor 5 1.917 Collpse 7 2.05 Relibility Index β = 5. 1 Tble 7. Trnsverse Diretion (M. Shinozuk, 2001)

4. CONCLUSIONS In this study, the results tht re similr with existing results re quired using the Probbilisti Cpity Spetrum Method. It is the new pproh tht the onepts whih re unrelibility or ll o the strength o the onrete nd ground motion re dded to the Cpity Spetrum Method to obtin responses o strutures under erthqukes. While Cpity Spetrum Method shows one response beuse o not onsidering the unertinty, Probbility Cpity Spetrum Method shows not only the probbility o eh dmge stte but lso ilure probbility o the bridges. ACKNOWLEDGEMENTS This study ws supported prtly by the und o the Kore Erthquke Reserh Center (KEERC), prtly by the und o the Kore Bridge Design & Engineering Reserh Center (KBRC) nd prtly by the und o the BK21 Progrm o Kore Ministry o Edution. The uthors wish to express their grtitude or the support reeived. REFERENCES Alredo H-S. Ang, Wilson H. Tng, Probbility Conepts in Engineering Plnning nd Design Vol.,, John Willey & Son, 1975 Arthur H. Nilson, Design o Conrete Strutures, MGrw-Hill, 1997 Asdour H. Hdjin, A generl rmework or risk-onsistent seismi design, Erthquke Engineering nd Struturl Dynmis, Vol.31, pp.601-626, 2002 C. Allin. Cornell, "Engineering Seismi Risk Anlysis", Bulletin o the Seismologil Soiety o Ameri, Vol.58, No 5, pp.1583-1606, 1968 Jk R. Benjmin, C. Allin Cornell, Probbility, Sttistis, nd Deision or Civil Engineers, MGrw-Hill, 1970

M. Shinozuk, M. Q. Feng, H. Kim, T. Uzw, T. Ued, "Sttil Anlysis o Frgility Curves", MCEER Tehnil Report, 2001 "Seismi evlution nd retroit o onrete buildings", ATC-40 Report Vol.1, Applied Tehnology Counil, 1996