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294 mutation. Elitist strategy means that the best individual has been found will always be kept in the population. The simple genetic algorithm with elitist strategy is convergence^. For many multi-parameters optimization problems, some algorithms have to calculate the grad of the function. But in nonlinear optimization problems, the grad of function can not be expressed analytically, and can only be calculated approximately. This will not only increase the calculating cost but also depress the precision of the solution. Simplex is a multi-dimensions local optimization algorithm which does not calculate the grad of the objective function. It has the advantages of easy implement, good searching ability in local area and simplicity of operation on computers. Simple genetic algorithm is one of the widely used optimization algorithms. But has the disadvantage of low searching efficiency problem, especially for multi-dimension and high precision demanded problems. Many efforts have been put by researchers to speed up the convergence of the genetic algorithm. For example, Cui 3 has obtained some improvement by combing GA with simulated annealing algorithm. In order to improve the search capability, a hybrid optimization algorithm was developed in this paper which combines the genetic algorithm and the simplex. In the proposed algorithm, the convergence is guaranteed by genetic algorithm, and simplex can speed up the optimization. Further more, the validity of this hybrid algorithm was checked by seven test functions and compared with other two algorithms. The result shows that the proposed hybrid algorithm has high searching efficiency. 2. GENETIC ALGORITHM - SIMPLEX Premature is the main factor that affects the speed of convergence of GA. To cope with it, genetic algorithm-simplex is presented, and the main procedure is shown in Fig 1. 2.1 Objective Function and Initial Population In many actual engineering optimization problems, observed data are used to identify the parameters. For example, as the observed data are Fc(i), i=l,2,-,M, M is the number of the parameters that to be identified in the model, the objective function can be defined as: in which, F L (i) is the theoretic value. If the model space is B, its size is K, the objective of optimization is to find a special model, and its M parameters meet the condition, (2.1) $ ^ s (2.2) where s is convergence precision, is a pre-chosen, small positive number.
295 Genetic algorithm starts from a group of individuals, namely population, and its size is noted as N. Then the primal parameters of model can be given by: S, = S imin + ^(Staax - Si rai n) (i=l,2,...,M) (2.3) in which, S imax and Si min are respectively the upper and lower limits of parameter S, that need to be identified. ^ is a uniform distributed random number between 0 and 1. 2.2 Genetic Operation 2.2.1 Selection After the initial population was obtained, the objective function of every model can be calculated by (2.1). Objective function is the direct express of the quality of the model, and is the basic standard to distinguish the individuals. There are many methods to express the quality of individual in genetic algorithm, and the relative fitness used in the paper is (2.4) where T is a constant, £ is a random distributed variable, and £e[0,l]. If ^\|/, <
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Proceedings of the International Co
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vw:. PREFACE Dunng a time when eart
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OPENING ADDRESS By Prof. Yuchen Liu
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Vll scientific support in our call
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IX INTERNATIONAL ADVISORY COMMITTEE
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XI Engineering Seismology and Geote
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Xlll Progress in the Seismic Design
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XV Crustal Deformation Measurement
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Legal Impediments There are many im
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expectation of (i) expanding the fr
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10 Some applications of wireless te
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12 Smart Materials Smart materials,
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14 research and education in a mult
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19 On September 16, 1994, buildings
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21 Some people were injured while t
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23 tallest buildings in Hong Kong w
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25 Figure 12. Tsim Sha Tsui East an
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27 OTHER ONGOING WORKS Soil-pile-st
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29 Figure 18. The pounding experime
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31 ACKNOWLEDGMENTS The writers are
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35 Table 1. Records Used in the Dev
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37 Date 8/19/1976 10/5/1977 12/16/1
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39 (2) Here Y is the ground motion
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41 1 2 3 4567810 20 3040 SO 100 200
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43 of the resultant horizontal comp
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45 200 006 005 004 003 002 Rock, Mw
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47 en o_ KOCAELI DATA (Random Hor C
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Atkinson G.M., Boore D.M., Some Com
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52 BACKGROUND AND SCOPES In order t
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configurations of cylinder, and it
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l 56 X xT / ^ V^—Ê-^/i / X\ - yX
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58 59-74 (in Japanese) [7] ISHIKAWA
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60 accuracy and efficiency, have no
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62 f •c J — c. p «3 no Fig. 2
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64 VARIOUS TESTS AND DISSEMINATION
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66 response.. The post-earthquake i
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71 with the potential for self-diag
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73 Semi-active Hydraulic (SHD) Cont
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75 Table 1 Buildings currently unde
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77 and the inside of the damper cyl
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79 Figure 16. MR damper installatio
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81 sensors. New algorithms must be
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83 Kobon T. (1998). Mission and per
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87 for a period range less than 3.0
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89 tens or more than a hundred acce
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91 In order to validate the conclus
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93 Distance (km) """"--Âcc. (g) Ma
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95 CONCLUSION In this paper the imp
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ENGINEERING SEISMOLOGY AND GEOTECHN
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100 analyses. This paper reviews th
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102 A comparison of spectral amplit
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104 the high speed with which FFT c
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106 8. Gold, B. and C.M. Rader (197
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108 Displacement Curves", Dept. of
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I 000,000 100,000 Number of uniform
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Response and Fourier Spectra of Dig
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114 10 3 10 2 r | I lll| I | I | I
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116 analysis. Other techniques incl
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It has been found that G max applie
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120 PALEOLIQUEFACTION STUDIES IN ME
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Jardine, R.J., Potts, D.M., StJohn,
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124 has received more attention. In
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126 and log A tQ (/) is the mean so
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128 O 10 s Frequency (Hz) Frequency
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130 geometrical coefficient is near
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132 optimizing method in many engin
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134 Type-2 Fig.5 Three dimensional
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136 plane plane sliding x-z section
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141 1. System Definition define sys
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143 f«l-.J WJjfc >. « Relations -
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145 DS-5 Response Simulation across
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147 CM 1 CM 2 CM 3 CM-4 CM-5 CM-6 H
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151 Number of Pixels (Frequency) Th
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153 Red: possible impacted areas Gr
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155 Low reliability -(water-reflect
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Modem or Router Dial-up ISP The Int
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from observed images Fig 5 shows th
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163 Picked up building from image p
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167 evaluated by Seed's approximate
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169 R(z,N)is reached to 1 or more,
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171 subsoil and combined with the n
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174 defined target. Performance-bas
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176 (probability of being in or exc
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178 start to dominate the hazard at
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180 MAX STORY DUCTILITY vs NORM. ST
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185 Decision Making Support Tool' U
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189 31 Array Liujiaxia Reservoir Ar
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191 IV STATION YM kCED INTERVALS OF
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193 at different stations, the diff
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197 PROBLEMS AND CHALLENGES Earthqu
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199 Database s*n/ar_- ^ " Applicati
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201 o Fig. 7 VRML authoring - model
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SMART MATERIALS AND SMART STRUCTURE
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209 vector of measured control forc
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211 To examine the effect of the co
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213 from Fig. 2, we can get the res
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Proceedings of the Intel-national C
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217 where gjî = g(X fcTl , F^+ît
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219 3 360 Deg. Fault Normal (Jan. 1
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221 Earthquake Sylmar 90 Sylinar 90
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225 0.6965, and 0.7094 Hz, which ar
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227 Two displacement sensors are po
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229 14% to 45% (under El Centre ear
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233 g ^ w (4) Notice that the stabi
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235 modes. An energy drop will be o
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237 PPF controller. The second mode
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241 In order to determine the appar
Page 259 and 260: 243 RESULTS AND DISCUSSIONS Figure
Page 261 and 262: 245 100 150 200 250 300 Magnetic Fl
Page 263 and 264: Proceedings of the International Co
Page 265 and 266: 249 WOLFE, MASRI, CAFFREY Synthetic
Page 267 and 268: 251 WOLFE, MASRI, CAFFREY Transform
Page 269 and 270: 253 WOLFE MASRI,CAFFREY in parallel
Page 271: STRUCTURAL ANALYSIS AND DESIGN
Page 274 and 275: 258 separating the above three effe
Page 276 and 277: 260 200 300 400 SHAKE Computed RSD^
Page 278 and 279: 262 where H b is the building heigh
Page 280 and 281: 264 6. ACKNOWLEDGEMENTS The work de
Page 282 and 283: 266 The objectives of this paper pr
Page 284 and 285: 268 The comparison between abovemen
Page 286 and 287: 270 determine the span of the state
Page 288 and 289: the assumption of the bi-linear for
Page 290 and 291: 274 There are several outstanding e
Page 292 and 293: 276 buildings with different concre
Page 294 and 295: 278 160,000 - 140,000 _^ d Concrete
Page 296 and 297: 280 responded in a fairly similar m
Page 298 and 299: 282 CONCLUSIONS In this paper, the
Page 300 and 301: 284 Li B, Park, R. and Tanaka, H. (
Page 302 and 303: For simplicity, a linear distributi
Page 304 and 305: equation can be re- written as: 288
Page 306 and 307: 290 DESIGN BASE SHEAR Equating the
Page 308 and 309: 292 by using the computer program S
Page 312 and 313: 296 2.3 Premature Judgment and Comb
Page 314 and 315: 298 (3) F 3 : De Jone's F 5 3 (Shek
Page 316 and 317: 300 3.3 Test Conclusions From the t
Page 318 and 319: 30; excitation. Also, the software
Page 320 and 321: 304 concrete frames as documented i
Page 322 and 323: 30€ NEURAL NETWORKS Development I
Page 324 and 325: 308 ACKNOWLEDGEMENTS The research r
Page 326 and 327: 310 In order to ensure the entire s
Page 328 and 329: 312 Examples of Seismic Design In K
Page 330 and 331: 314 earthquake load can be reduced
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Page 335 and 336: 319 broken. Therefore, joint elemen
Page 337 and 338: 321 the highest probability to get
Page 341 and 342: 325 DESIGN SPECIFICATIONS Overview
Page 343 and 344: 327 Strength Degradation The streng
Page 345 and 346: I 329 file £dit Vww Select Constru
Page 349 and 350: 333 (1) (2) Where f t and _/£ are
Page 351 and 352: 335 JL dalt (10) Where Solution of
Page 353 and 354: 337 Fig.9 Distribution of first pri
Page 357 and 358: 341 DISPLACMENT-BASED NSF In this p
Page 359 and 360: 343 Define the displacement of the
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345 EXAMPLE The nonlinear static an
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347 REFERENCE Code for seismic desi
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350 including Hong Kong are conside
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352 structural engineers. Although
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354 4) Structural characteristics R
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356 maintain economy of design but
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358 R C3&OC11 L8C34 USC3 •••
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363 DISPLACEMENT CALCULATION FOR GR
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365 As shown in the figure, a model
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367 history of a design earthquake
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371 reflects the structure's useful
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373 have a membership degree betwee
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375 of initialization of structure,
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377 REFERENCE Zadeh, L A (1965), "F
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.0,(r+l,,)] (0 + A (3) 383 -l.y)] (
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385 The first example is come from
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387 REFERENCES Leroueil, S. (2001).
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391 maximum friction force and ther
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393 the stiffness of the bracing an
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395 splacemen (m) Bared —Braced/D
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399 Figure 1: Two-surface Model Def
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401 time, one must integrate over t
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403 CONCLUSIONS In the previous sec
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407 Response Control by Magneto-Rhe
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409 s e a ooo j| soo y 0 c l83Hz ,5
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411 *fU O A 20 7 HA I , 2 4A .'.-%-
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415 Wind Velocity (t J Relative Dis
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417 Fig. 5 4ttractor (time lag=l) F
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419 concluded that the real time re
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422 (LQR/LQG), H M and the instanta
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424 damping ratio provided by the c
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426 by Eqn. 2.9 and Eqn. 2.12, resp
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428 Fig.5.4 shows the comparison of
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Innovation is driving control devic
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432 STRUCTURAL ENERGY DURING VIBRAT
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434 Equilibrium Price of Power With
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436 CONCLUSION The scope of this re
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438 The effectiveness of the seismi
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440 anti-symmetnc mode excited by t
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442 Base Isolation System The base
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444 each member along or adjacent t
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446 2 PROJECT PARTICIPANTS Concerni
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448 3.1.2 Investigations at Seyhan
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450 Fig. 4.2: principle drawing of
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452 no TMD — TMD with optimal tun
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454 In this paper, a stochastic opt
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456 The dynamical programming equat
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458 control strategy. Model reducti
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460 (AMDs) and the proposed control
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465 Specimen 1 Specimen 1 represent
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467 increased the beam moment stren
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469 3 4@8 ~T / C 4 #5 #3 stirrups 8
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473 Phase 2: The sliding of the bot
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475 4.2 Effect of Coefficient of Fr
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477 -ZETA=Q 05 -ZETA=010 -ZETA=015
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479 031 en 03 0029 P |Q 28 ~*—MR=
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482 stiffness or coefficient of vis
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484 solved for the Hamilton Jacobi-
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486 posed to be the accelerations o
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488 vlaxacccleiation [my O O O O
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490 INTRODUCTION In recent years, t
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492 Since earthquake motion has bee
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494 confirmed in the X direction. B
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496 yielding metallic dampers, and
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498 where the coefficients a m and
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500 modal damping ratio of 2% in ea
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502 CONCLUDING REMARKS The paper re
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504 semi-active and hybrid control
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506 Iiz2i(t)l
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508 defined as 114 - [ J* [•] 2 d
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Spencer, B.F., Jr. and Sain, M.K. (
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512 with the rapid success of seism
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Figure 3 summarizes the static push
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CONCLUSIONS 516 This paper presents
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518 1 OS h : 0 6 5 5 04 Transverse
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520 TABLE 1 REPRESENTATIVE MULTISTO
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522 obtained by simply averaging a
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524 levels of peak ground accelerat
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526 8 CONCLUSIONS This paper gives
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531 350.6m 142.7m .0130') i (468')
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533 cation corresponds to a node of
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535 listed separately. Vertical mod
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539 in which H = [H(t l ), H(t 2 ),
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541 identification results of struc
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543 history to be polluted. In the
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548 environment constraints and the
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550 where F is the fundamental matr
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552 drift ratio was determined as Y
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554 were observed in the vision-sys
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556 The most well-known Bayesian so
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558 achieved by the EKF estimates I
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560 0 2 4 5 3 10 12 14 16 18 20 0 2
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562 CONCLUSION In this study, the r
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564 Figure 1 - Prototype wireless s
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566 will vary from that of the ARX
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568 A 11 25" 11 25" 1 T 1 11 25* !
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570 CONCLUSION The realization of a
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572 the structural response and ret
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574 The ratios of base shear at dif
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576 SECTION A-A COLUMN DETAIL AT TO
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578 Stress Strain Fig. 6 Superelast
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580 Genetic Algorithm(GA) and the M
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582 IDENTIFICATION ALGORITHM State
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584 the simulated structural respon
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586 GA-MCF, the number of particles
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588 instrumentation to monitor and
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590 If we compare this peak value w
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592 2.3 Sampling rate Structural mo
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594 Any remote station that has the
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596 or receives environmental infor
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598 Comparisons of Proposed E-Monit
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600 LCD display Earthquake Warning
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605 Kishwaukee River Bridge consist
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607 the table, the monthly maximum
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609 3.2 L VDTData Analysis LVDT sen
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structural response energy with fre
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615 Table 1: Fundamental natural fr
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617 JUSTS rs s." WUUR. 4 20O2 IS 44
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1-1 INDEX OF CONTRIBUTORS Volumes I
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Ths OOO F X ^s dje foe return 01 ê
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