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Marano presentation

  1. 1. Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  2. 2. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  3. 3. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  4. 4. parametric identification of nonlinear devices for seismic protection using soft computing techniques BASE ISOLATION IsolatorsProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  5. 5. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  6. 6. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  7. 7. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  8. 8. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  9. 9. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  10. 10. parametric identification of nonlinear devices for seismic protection using soft computing techniques NTC/08 - EN 15129Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  11. 11. parametric identification of nonlinear devices for seismic protection using soft computing techniques ( mx ( t ) + g x , x, t , Θ = f ( t ) && & )Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  12. 12. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  13. 13. parametric identification of nonlinear devices for seismic protection using soft computing techniques Experimental vs analytical responce force f exp ( t ) ( f a x, x, t ,ϑ & )Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  14. 14. parametric identification of nonlinear devices for seismic protection using soft computing techniques f exp ( t ) Design vector x (t ) ϑ x (t ) & minimize tend ∫ abs ( f ( t ) − f ( x, x, t,ϑ ) ) dt exp & e OF ϑ = ( ) tstart tend ∫ abs ( f ( t ) ) dt tstart expProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  15. 15. parametric identification of nonlinear devices for seismic protection using soft computing techniques tend ∫ abs ( f ( t ) ) dt tstart exp tend ∫ abs ( f ( t ) − f ( x, x, t,ϑ ) ) dt tstart exp & eProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  16. 16. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  17. 17. parametric identification of nonlinear devices for seismic protection using soft computing techniques To be identified && ( t ) − µ ( 1 − y 2 ( t ) ) y ( t ) + y ( t ) = sin ( ω f t ) y &Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  18. 18. parametric identification of nonlinear devices for seismic protection using soft computing techniques Searching for a more reliable mathematical models of the investigated systems… Mathematical model of Experimental set-up the system Simulated system Measured system response response Features from the Features from the simulated response measured response Minimize the Evaluate difference correlation New set of system   Reliable model parametersProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  19. 19. parametric identification of nonlinear devices for seismic protection using soft computing techniques Non-classical algorithms: they deal with socially, phisically and/or biologically inspired paradigms (Perry et al., 2006) In this field, the most adopted is soft computing algorhitmProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  20. 20. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  21. 21. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  22. 22. genetic algorhitms (GA) GA’s are based on Darwin’s theory of evolution Reproduction Competition surviving Selection Evolutionary computing evolved in the 1960’s. GA’s were created by John Holland in the mid-70’s.Nuove prospettive del monitoraggio strutturale GiuseppeCarlo Marano Politecnico di Bari
  23. 23. GA scheme PRIMA GENERAZIONE Generazione dopo generazione, la popolazione evolve verso una soluzione ottima. GENITORE 1 GENITORE 2 SECONDA GENERAZIONEGli algoritmi genetici GENITORE 1 GENITORE 2 TERZA GENERAZIONEvengono utilizzati per risolvereuna varietà di problemi per cui inormali metodi di ottimizzazionerisultano poco appropriati (discontinuità, nondifferenziabilità, forti non linearità etc.) GENITORE 1 GENITORE 2 Nuove prospettive del monitoraggio strutturale Giuseppe Carlo Marano Politecnico di Bari
  24. 24. Particle Swarm Optimization Nuove prospettive del monitoraggio strutturale Giuseppe Carlo Marano Politecnico di Bari
  25. 25. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  26. 26. parametric identification of nonlinear devices for seismic protection using soft computing techniques Test Load Velocity Test Type stroke Cycle (kN) (mm/s) (±mm) 7No.1 20 92 (20%) 3 50 Constitutive law test2 750 20 230 (50%) 33 750 20 460 (100%) 34 Damping efficiency test 750 17 460 (100%) 10Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  27. 27. parametric identification of nonlinear devices for seismic protection using soft computing techniques M is the effective mass  Cα is the damping coefficient My + Cα y = p && & sgn[·] is the signum function α is the damping law exponent K1 is the elastic stiffness My + Cα y α = p && & p is the time-varying force M is the effective mass Cα is the damping coefficient My + Cα sgn [ y ] y + K1 y = p α sgn[·] is the signum function && & & α is the damping law exponent K1 is the elastic stiffness K2 and K0 are two constants My + Cα sgn [ y ] y + ( K 2 y 2 + K1 y + K 0 ) = p α && & & M is the effective mass  C1 is the internal damping coefficient  Cα is the damping coefficient My + C1 y + Cα sgn [ y ] y + K1 y = p α sgn[·] is the signum function && & & & α is the damping law exponent K1 is the elastic stiffness p is the time-varying forceProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  28. 28. parametric identification of nonlinear devices for seismic protection using soft computing techniques Non-classical Identification methods Algorithm Short description DEA01 A DEA whose mutation operator is given by Eq.(1) and with binomial crossover as in Eq.(6) DEA02 A DEA whose mutation operator is given by Eq. (2) and with binomial crossover as in Eq.(6) DEA03 A DEA whose mutation operator is given by Eq.(3) and with binomial crossover as in Eq.(6) DEA04 A DEA whose mutation operator is given by Eq.(4) and with binomial crossover as in Eq.(6) DEA05 A DEA whose mutation operator is given by Eq. (5) and with binomial crossover as in Eq.(6) A DEA with adaptive mutation – as in Eq.(8) – and a free-parameter crossover given by Eq. DEA06 (10) A PSOA whose velocity model is Eq.(11), with inertia weight as in Eq.(13), social and PSOA01 cognitive factors as in Eq.(14) A PSOA in which the velocity updating rule (based on the use of the constriction factor) is PSOA02 given by Eq.(15) A PSOA based on the use of chaotic maps (so-called chaotic PSOA) for both inertia weight PSOA03 and acceleration factors PSOA04 A PSOA with passive congregation in which the velocity updating rule is given by Eq.(19) A modified multi-species real-coded genetic algorithm with specialized operators for each MGAR subpopulation, see [17] and [18]Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  29. 29. parametric identification of nonlinear devices for seismic protection using soft computing techniques Objective Function results obtained using a linear viscous Test Mean Max Min Std Test 1 0.324322 0.324322 0.324322 0 Test 2 0.363997 0.363997 0.363997 2.8E-16 Test 3 0.272685 0.272685 0.272685 1.68E-16 Test 4 0.297829 0.297829 0.297829 1.68E-16 Objective Function results obtained using a generalized viscous Test Mean Max Min Std Test 1 0.254494 0.254494 0.254494 4.26E-14 Test 2 0.332256 0.332257 0.332256 1.39E-07 Test 3 0.264244 0.26426 0.264243 2.99E-06 Test 4 0.28234 0.28234 0.28234 2.45E-09Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  30. 30. parametric identification of nonlinear devices for seismic protection using soft computing techniques Mechanical Model: Generalized viscous- linear elastic Test Mean Max Min Std Test 1 0.162356 0.163188 0.162077 0.000298 Test 2 0.203976 0.204116 0.203949 3.45E-05 Test 3 0.153384 0.153388 0.153384 7.23E-07 Test 4 0.127699 0.127699 0.127699 1.41E-12 Mechanical Model: Generalized viscous- quadratic elastic Test Mean Max Min Std Test 1 0.173636 0.254494 0.158448 0.022962 Test 2 0.208454 0.21712 0.203949 0.006284 Test 3 0.160706 0.26426 0.153025 0.026845 Test 4 0.12752 0.127699 0.126207 0.00049Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  31. 31. parametric identification of nonlinear devices for seismic protection using soft computing techniques Mechanical Model: Linear viscous Test Type N.1 Test Type N.2 Test Type N.3 Test Type N.4 Parameters v=92mm/s v=230mm/s v=460mm/s v=460mm/s M (mean) - [kg] 0 0 0 0 M (max) - [kg] 0 0 0 0 M (min) - [kg] 0 0 0 0 C (mean) - [kN/ 6.308518 9.955068 2.950677 3.599261 (mm/s)] C (max) - [kN/ 6.308518234 9.955068455 2.95067697 3.599260974 (mm/s)] C (min) - [kN/ 6.308518234 9.955068455 2.95067697 3.599260974 (mm/s)] C (std) - [kN/(mm/s)] 3.32E-14 0 1.93E-15 3.15E-14Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  32. 32. parametric identification of nonlinear devices for seismic protection using soft computing techniques Mechanical Model: Fractional viscous Test Type N.1 Test Type N.2 Test Type N.3 Test Type N.4Parameters v=92mm/s v=230mm/s v=460mm/s v=460mm/sM (mean) - [kg] 1.75E-14 1.45E-11 0 0M (max) - [kg] 8.74059E-13 7.26404E-10 0 0M (min) - [kg] 0 0 0 0M (std) - [kg] 1.24E-13 1.03E-10 0 0C (mean) - [kN/(mm/s) ^ α] 321.4664 101.8108 20.93332 60.02495C (max) - [kN/(mm/s)] 321.4663828 102.5398101 22.44238445 60.02544199C (min) - [kN/(mm/s)] 321.4663828 101.058709 20.75427774 60.01439848C (std) - [kN/(mm/s)^ α ] 1.05E-10 0.254748 0.284589 0.001661α (mean) 0.121515 0.456479 0.647184 0.472998α (max) 0.121514934 0.458176548 0.648755563 0.473033897α (min) 0.121514934 0.454813372 0.634798957 0.472996579α (std) 6.82E-14 0.00058 0.002352 5.61E-06Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  33. 33. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  34. 34. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  35. 35. parametric identification of nonlinear devices for seismic protection using soft computing techniques (a) (b) (c) (d)Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  36. 36. parametric identification of nonlinear devices for seismic protection using soft computing techniques Maxwell C K OF Test 1 7.3107 139.2401 0.2882 Test 2 4.0123 259.4377 0.1784 Test 3 3.3335 205.6275 0.2869 Generalized Maxwell C K   OF 132.0147 267.8712 0.33 1.0006 0.1269 Test 1 31 122.1587 358.5821 0.33 1.0060 0.1240 Test 2 60 119.2544 277.8710 0.33 1.0017 0.1535 Test 3 33 Generalized Voight C K   OF 24.1856 0.7847 0.6932 2.0000 0.2300 Test 1 Test 2 1.0213 1.1889 1.2407 2.0000 0.1816 Test 3 4.7018 52.2115 0.9249 0.4756 0.3079 Voight C K OF Test 1 6.4963 12.6587 0.2877 Test 2 3.5944 15.5999 0.2049 Test 3 3.0240 12.8350 0.3074Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  37. 37. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  38. 38. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  39. 39. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  40. 40. parametric identification of nonlinear devices for seismic protection using soft computing techniques 50 mm 70 mm 104 mmProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  41. 41. parametric identification of nonlinear devices for seismic protection using soft computing techniquesf BW ( t ) = kα x (t ) + ( 1 − α ) kz (t )& & & (z ( t ) = x ( t ) − β x (t ) z (t ) η −1 z (t ) − γ x (t ) z (t ) & η )Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  42. 42. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  43. 43. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  44. 44. parametric identification of nonlinear devices for seismic protection using soft computing techniques 400 - 1220 KN vertical load Test k α β γ η OF 50 mm 2.0812 0.4174 0.0078 -0.0065 1.8350 0.0961 70 mm 2.0522 0.3216 0.0018 -0.0015 2.0297 0.0783 140 mm 3.8513 0.2241 0.0276 -0.0202 1.4126 0.0710 Test k α β γ η OF 50 mm 2.579205 0.408575 0.14214 -0.11051 1.064357 0.09611 70 mm 2.880737 0.361206 0.030589 -0.02775 1.765703 0.078308 140 mm 3.926685 0.219169 0.041263 -0.02879 1.270448 0.07104Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  45. 45. parametric identification of nonlinear devices for seismic protection using soft computing techniques f BW ( t ) = kα x (t ) + ( 1 − α ) kz (t )z (t ) = x(t ) − β x(t ) z (t )& & & ( η −1 z (t ) − x(t ) z (t ) & η )Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  46. 46. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  47. 47. parametric identification of nonlinear devices for seismic protection using soft computing techniques Model K    BW 5 b1 2.0577 0.4237 0.0018 -0.0016 2.3154parametrsBW 4 1.3588 0.0697 5.8117e- 2.7033parametrs b3 005BW 5 2.0577 0.4237 0.0018 -0.0018 2.3154parametrs b2“forced “Prof. Giuseppe Carlo MARANOTechnical University of BARI, Italy
  48. 48. parametric identification of nonlinear devices for seismic protection using soft computing techniquesProf. Giuseppe Carlo MARANOTechnical University of BARI, Italy

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