別紙41 (課程博士(英文))D誠e of submission (m0挑h day, year)

07 05血 2022

DeP観mentofA}'CI〕itect磁'e andSlude"11D Kulnbet

D199503

CiV丑 En部neehn弩へPplical】1's nal"eSuperYisoTS

Taiki sai加

SbojiNaka乞awa

MEIくAOUINABIL

Yukihiro Matsumoto

Abstract (Doctor)
Tltle ofThesis

Artiacia11nte11i今ence、Based opti塑ization ofstructural parameters toSimulate the seismic BehavioT ofBuildings
Approx.80o words
An AI'ti負Cia1 1nte11igence'based optimization of structural P紅ameters is elaborated i11 thisdissertation to perform a hybrid

Selsn)1Canalysis of bU丑ding S訂'udures、 This ori琴inalProblem、solving approach consists of combining advanta晉eous of both AI・ti金̲cia1 1nte11i今ence andConventional nonli11ear time hist0117 analysis (NT1塾), to increase 丑.1rther tbe accuracy of seislnic
response simulation.1Xuo optimization domains al!e investi旦ated: the 負τSt one targets speci丘C

Physicalparameters ofthe building model; tl)e second one pToposes new machine learnin芸 nlodels
Simulating speci丘C structuralc0血Ponent or 客roup ofcomponents, as surrogates to theiT analytical
Countelpalts. Both optimization outputs are then deployed 血 1)ybrid seismic analyses ofseveral
building lnodels to check tl〕e註' respective e缶Ciencies in improvil)宮 the accuracy of the seismic
response simulation. To this end, synthetic, experinlental, and 負eld data are used as references

In evaluation of seismic safety of buildin今S, systen〕 parameter identi{1Cation is crucial. Althougbmany matl)en)atica11nodels exist to evaluate the physical properties of building structures, thetremendous number of involved paramete玲 n〕ainta血S a certain degree of uncertainty ln thisresearch, a system parameter optimization procedure is developed usin曾 Response surface 入lethodand Bayesian optimization Technique. The developed pr0牙ram was succesS会.1Hy tested on realb{1ildings in the Aicl〕i pTefectuxe at eastern Japan, with a vie工入l to inc0生やorate it in lhe existino・Online seismie diagnosis system. The accuracy and tbe P里omptness ofthe opti口〕i乞ation )rocedure
make it ef6Cient for a Tea}・time system identi{1Cation

Simulating the structuralbehaviouT oftypical seislnic isolators U赴der a wide spectrun〕 ofrealistic

10ading conditions is sti11 not accuTately achieved by a sino'1e anab北ical nlodel. Deep learnino
networks predictin曾 the non'(i11ear hysteretic behavior of speciac triple pendulum beaぞino、(TPB),

Iead rubbeT bearing (LRB), and a fuH seislnic isolation layer, are developed, tested, and eventua11y
deployed in tbe proposed hybrid seismic analysis. Experi塑ental datasets were deTived 丘om aSbake、table test pr0牙ram of an isolated 丘Ve'st0117 bU丑d迦g speci血en, perforlned at the Hyogo
En部neerin今 Researcb center (翻'Defense) of八石ki, Japan' Data measured during 34 di丘erenttable
Inotions are processed to construct a TPB圧,RB dataset of 158/55 Samples. conventional NT1王AS

Were peTfor皿ed to geneTate syntbetic data for tbe case study offU11isolation layeT. colnparisonsWith reference e>甲eti血entavsynthetic data show'ed that the proposed bybrid analysis could
Silnulate accuTately the seismic tesponse of studied buildings. The 曾eneralization capability ofdeveloped sul・r0牙ate machine le紅nin曾 models on the substantial datasets used in this study,
revealed the bene負t of applyin牙皿achi11e leal'1血1套 to solve c0血Plex stTuctural en牙血eerin套Proble血S.