Final Thesis de eloped by:
Ma in GRANIER
Di ec ed by:
Seyedmilad KOMARIZADEHASL
Jose TURMO CODERQUE
Mas e in:
S uc u al and Cons uc ion Enginee ing
Ba celona, Janua y 2025
Depa men o Ci il and En i onmen al Enginee ing
MASTER FINAL THESIS
Compa a i e Analysis o Low-Cos
and Comme cial Senso s in
S uc u al Moni o ing Unde
Seismic Simula ions
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Compa a i e Analysis o Low-Cos and Comme cial Senso s in S uc u al
Moni o ing Unde Seismic Simula ions
Abs ac :
S uc u al Heal h Moni o ing (SHM) plays a c i ical ole in ensu ing he sa e y and
longe i y o buildings, especially in ea hquake-p one egions. T adi ional SHM sys ems
o en ely on high-cos comme cial senso s, which can limi widesp ead adop ion due o
inancial cons ain s. This s udy conduc s a compa a i e analysis o low-cos and
comme cial senso s o e alua e hei e icacy in s uc u al moni o ing unde seismic
simula ion condi ions.
The p ima y objec i es a e o assess he accu acy, eliabili y, and cos -e ec i eness o
low-cos senso s ela i e o hei comme cial coun e pa s and o de e mine hei
sui abili y o la ge-scale SHM applica ions du ing seismic e en s.
An expe imen al se up was es ablished using a scaled s uc u al model subjec ed o
a ious seismic simula ions mimicking di e en ea hquake in ensi ies. Bo h low-cos
senso s and comme cial senso s we e s a egically ins alled on he model o cap u e
dynamic esponses. Da a collec ion in ol ed con inuous moni o ing o e mul iple
simula ion ials, ollowed by igo ous s a is ical analysis.
The esul s indica e ha low-cos senso s demons a e compa able pe o mance o
comme cial senso s in cap u ing essen ial s uc u al esponses, pa icula ly in mode a e
seismic condi ions. While some disc epancies we e no ed in high-in ensi y simula ions,
he low-cos senso s main ained accep able accu acy le els o p ac ical SHM pu poses.
Addi ionally, a signi ican educ ion in o e all sys em cos s was achie ed wi hou
subs an ial sac i ices in da a quali y. The s udy highligh s he po en ial o in eg a ing low-
cos senso s in o SHM amewo ks, p omo ing mo e accessible and sus ainable
moni o ing solu ions o di e se in as uc u al applica ions.
In conclusion, his esea ch suppo s he iabili y o low-cos senso s as e ec i e
al e na i es o comme cial senso s in s uc u al moni o ing unde seismic condi ions. The
indings ad oca e o hei adop ion in esou ce-cons ained en i onmen s, os e ing
enhanced s uc u al sa e y h ough a o dable and eliable moni o ing echnologies.
Fu u e wo k will explo e long- e m deploymen scena ios and he in eg a ion o ad anced
da a analy ics o u he op imize SHM sys ems.
Keywo ds: S uc u al Heal h Moni o ing, Low-Cos Senso s, Comme cial Senso s,
Seismic Simula ion, Senso Accu acy, Cos -E ec i eness
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Résumé:
Le con ôle de san é des s uc u es (CSS) joue un ôle c ucial pou assu e la sécu i é e
la longé i é des bâ imen s, en pa iculie dans les égions suje es aux emblemen s de
e e. Les sys èmes adi ionnels de sui i de san é s uc u elle eposen sou en su des
cap eu s comme ciaux coû eux, ce qui peu limi e leu adop ion géné alisée en aison de
con ain es inanciè es. Ce e é ude éalise une analyse compa a i e en e des cap eu s à
aible coû e des cap eu s comme ciaux a in d’é alue leu e icaci é dans la su eillance
s uc u elle sous des condi ions de simula ion sismique.
Les objec i s p incipaux son d’é alue la p écision, la iabili é e le appo coû -e icaci é
des cap eu s à aible coû pa appo à leu s homologues comme ciaux e de dé e mine
leu adéqua ion pou des applica ions de CSS à g ande échelle lo s d’é énemen s
sismiques.
Un disposi i expé imen al comple a é é é abli en u ilisan un modèle s uc u el à moind e
échelle soumis à di e ses simula ions sismiques miman di é en es in ensi és de séismes.
Des cap eu s à aible coû ainsi que des cap eu s de quali é comme ciale on é é ins allés
s a égiquemen su le modèle pou cap u e les éponses dynamiques. La collec e des
données a impliqué une su eillance con inue au cou s de plusieu s essais de simula ion,
sui ie d’une analyse s a is ique igou euse.
Les ésul a s indiquen que les cap eu s à aible coû démon en des pe o mances
compa ables à celles des cap eu s comme ciaux pou cap u e les éponses s uc u elles
essen ielles, en pa iculie dans des condi ions sismiques modé ées. Bien que ce aines
di e gences aien é é no ées lo s de simula ions à hau e in ensi é, les cap eu s à aible
coû on main enu des ni eaux de p écision accep ables pou des applica ions p a iques
de SHS. De plus, une éduc ion signi ica i e des coû s globaux du sys ème de con ôle a
é é éalisée, sans sac i ices subs an iels de la quali é des données. L’é ude me en lumiè e
le po en iel d’in ég a ion des cap eu s à aible coû dans les cad es de CSS, a o isan des
solu ions de su eillance plus accessibles e é olu i es pou di e ses applica ions
in as uc u elles.
En conclusion, ce e eche che sou ien la iabili é des cap eu s à aible coû en an
qu’al e na i es e icaces aux cap eu s comme ciaux dans le con ôle de san é des
s uc u es sous condi ions sismiques. Les ésul a s p éconisen leu adop ion dans des
en i onnemen s aux essou ces limi ées, a o isan une sécu i é s uc u elle acc ue g âce
à des echnologies de su eillance abo dables e iables. Les a aux u u s explo e on
des scéna ios de déploiemen à long e me e l’in ég a ion d’analyses de données a ancées
pou op imise da an age les sys èmes de CSS.
Mo s-clés: Su eillance de la san é s uc u elle, Cap eu s à aible coû , Cap eu s
comme ciaux, Simula ion sismique, P écision des cap eu s, Rappo coû -e icaci é
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Acknowledgemen s
I would i s like o hank he wo enginee ing schools ha enabled me o pu sue his
dual deg ee, p o ided me wi h excellen aining, and p epa ed me o he p o essional
wo ld in he bes possible way: he École Spéciale des T a aux Publics (ESTP) and he
Uni e si a Poli ècnica de Ca alunya (UPC).
Addi ionally, I would like o exp ess my g a i ude o my wo TFM supe iso s, José
Tu mo and Seyedmilad Koma izadehasl, o hei in aluable knowledge and
unwa e ing suppo .
Fu he mo e, I wan o hank he en i e p o essional eam a COPCISA, who allowed me
o ca y ou my TFM alongside my in e nship as an assis an cons uc ion manage . A
special hanks o Ped o Cas ellanos, Ma ina Gonzalez, and Ca los Ga o e.
Las ly, I would like o hank all he membe s o my amily, who ha e always suppo ed
and belie ed in me, e en while I was ab oad in an en i onmen ou side o my com o
zone.
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Table o Con en s
Lis o Figu es and Tables ................................................................................................ 7
Chap e 1: In oduc ion ..................................................................................................... 9
1.1 Backg ound ....................................................................................................... 9
1.2 P oblem S a emen .......................................................................................... 10
1.3 Resea ch Objec i es & Ques ions .................................................................. 10
1.4 Scope & Signi icance o he S udy ................................................................. 11
1.5 Thesis O ganiza ion ........................................................................................ 12
Chap e 2: Li e a u e Re iew ......................................................................................... 13
2.1 S uc u al Moni o ing Sys em ........................................................................ 13
2.1.1 Designing a SHM sys em ....................................................................... 13
2.2 Types o Senso s Used in SHM ...................................................................... 15
2.3 Low-Cos Senso s ........................................................................................... 16
2.4 Comme cial Senso s ....................................................................................... 17
2.5 Seismic Simula ions in SHM .......................................................................... 17
2.6 Summa y ......................................................................................................... 18
Chap e 3: Senso Speci ica ions and Compa a i e F amewo k .................................... 19
3.1 Low-Cos Senso ............................................................................................ 19
3.2 Comme cial Senso ........................................................................................ 20
3.3 Compa a i e F amewo k ................................................................................ 20
Chap e 4: Me hodology ................................................................................................. 22
4.1 Expe imen al Se up ........................................................................................ 22
4.2 Theo y ............................................................................................................. 23
4.2.1 S uc u al Dynamics ............................................................................... 25
4.2.2 F equency Domain Decomposi ion and Peak Picking me hod .............. 28
4.3 Resea ch Design ............................................................................................. 30
4.4 Selec ion o Senso s ....................................................................................... 30
4.5 Seismic Simula ion P ocedu es ...................................................................... 30
4.6 Da a Collec ion Me hods & Analysis Techniques ......................................... 31
4.6.1 Absolu e Rela i e E o ................................................................................. 32
4.6.2 Modal Assu ance C i e ion ............................................................................ 32
Chap e 5: Expe imen al Resul s .................................................................................... 33
5.1 Da a P esen a ion ............................................................................................ 33
5.1.1 Ea hquake 1 .................................................................................................. 33
5.1.2 Ea hquake 2 .................................................................................................. 35
5.1.3 Sine 1 ............................................................................................................. 37
5.1.4 Sine 2 ............................................................................................................. 38
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5.1.5 Sine 3 ............................................................................................................. 40
5.1.6 Impac ............................................................................................................ 42
5.2 Compa a i e Analysis .................................................................................... 44
Chap e 6: Discussion ..................................................................................................... 45
6.1 In e p e a ion o Findings ............................................................................... 45
6.2 Implica ions o S uc u al Heal h Moni o ing ............................................... 45
6.3 Limi a ions o he S udy ................................................................................. 45
Chap e 7: Sus ainabili y analysis and e hical implica ions ........................................... 46
Chap e 8: Conclusion .................................................................................................... 47
8.1 Summa y o Resea ch ..................................................................................... 47
8.2 Conclusions .................................................................................................... 47
8.3 Fu u e Wo k .................................................................................................... 47
Re e ences ...................................................................................................................... 48
Appendices ..................................................................................................................... 51
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Lis o Figu es and Tables
Figu e 1: Concep ual diag am o he u iliza ion o SHM ................................................. 9
Figu e 2: Concep ual diag am o a da a acquisi ion sys em ........................................... 14
Figu e 3: Concep ual diag am o da a low in acquisi ion sys ems ................................ 15
Figu e 4: Pic u e o he LARA sensing pa and i acquisi ion pa ............................... 19
Figu e 5: Pic u e o he PCB 3713B112G senso wi hou and wi h connec ion cable ... 20
Figu e 6: Pic u e o he expe imen al se up aken he 08/07/2027 ................................. 22
Figu e 7: Pic u e o he design d awing in Au oCAD ansla ed om Chinese ............. 23
Figu e 8: De o ma ion scheme o he s uc u e .............................................................. 24
Figu e 9: Fi s ou mode shapes o a simply suppo ed beam ....................................... 27
Figu e 10: Fi s ou na u al equencies o he Peak Picking me hod.......................... 29
Figu e 11: Line ime o he e en s which will occu o es he s uc u e ...................... 31
Figu e 12: Gene al o e iew o he LARA’s esponse .................................................. 33
Figu e 13: LARA’s esponse agains Ea hquake 1 exci a ion....................................... 34
Figu e 14: Comme cial peaks o equencies selec ed o “Ea hquake1” ..................... 34
Figu e 15: LARA’s peaks o equencies selec ed o “Ea hquake1” ........................... 34
Figu e 16: LARA’s esponse agains “Ea hquake2” exci a ion .................................... 35
Figu e 17: Comme cial peaks o equencies selec ed o “Ea hquake2” exci a ion .... 36
Figu e 18: LARA’s peaks o equencies selec ed o “Ea hquake2” exci a ion .......... 36
Figu e 19: LARA’s esponse agains “Sine1” exci a ion .............................................. 37
Figu e 20: Comme cial peaks o equencies selec ed o “Sine1” exci a ion ............... 37
Figu e 21: LARA’s peaks o equencies selec ed o “Sine1” exci a ion ..................... 38
Figu e 22: LARA’s esponse agains “Sine2” exci a ion .............................................. 39
Figu e 23: Comme cial peaks o equencies selec ed o “Sine2” exci a ion .............. 39
Figu e 24: LARA’s peaks o equencies selec ed o “Sine2” exci a ion .................... 39
Figu e 25: LARA’s esponse agains “Sine3” exci a ion .............................................. 40
Figu e 26: Comme cial peaks o equencies selec ed o “Sine3” exci a ion .............. 41
Figu e 27: LARA’s peaks o equencies selec ed o “Sine3” exci a ion .................... 41
Figu e 28: LARA’s esponse agains “Impac ” exci a ion ............................................. 42
Figu e 29: Comme cial peaks o equencies selec ed o “Impac ” exci a ion ............. 42
Figu e 30: LARA’s peaks o equencies selec ed o “Impac ” exci a ion ................... 43
Figu e 31: Pic u es om he expe imen al se up, ocus on he column connec ion ....... 46
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Table 1: Summa y o key aspec s in cu en SHM ......................................................... 10
Table 2: Summa y o iaxial MEMS senso s al eady exis ing [10] .............................. 12
Table 3: Selec ion c i e ia o choosing senso s [15] ..................................................... 14
Table 4: Compa a i e able o he cha ac e is ics o he senso s ................................... 21
Table 5: 4 i s eigen equencies o he senso s du ing “Ea hquake1” ......................... 35
Table 6: 4 i s mode shapes o he senso s du ing “Ea hquake1” ................................ 35
Table 7: 4 i s eigen equencies o he senso s du ing “Ea hquake2” ......................... 36
Table 8: 4 i s mode shapes o he senso s du ing “Ea hquake2” ................................ 37
Table 9: 4 i s eigen equencies o he senso s du ing “Sine1” .................................... 38
Table 10: 4 i s mode shapes o he senso s du ing “Sine1” ......................................... 38
Table 11: 4 i s eigen equencies o he senso s du ing “Sine2” .................................. 40
Table 12: 4 i s mode shapes o he senso s du ing “Sine2” ......................................... 40
Table 13: 4 i s eigen equencies o he senso s du ing “Sine3” .................................. 41
Table 14: 4 i s mode shapes o he senso s du ing “Sine3” ......................................... 42
Table 15: 4 i s eigen equencies o he senso s du ing “Impac ” ................................ 43
Table 16: 4 i s mode shapes o he senso s du ing “Impac ” ....................................... 43
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Chap e 1: In oduc ion
A e he de elopmen o cons uc ion indus y in he pas cen u y, many s uc u es such
as b idges, high- ise buildings and o he ypes o mo e common buildings such as
housings we e pu up and a e now daily used by human being. Indeed, he buildings,
which a e made o accommoda e human ac i i y, need o sa is y a se ies o unc ions
in ended o assu e sa e y, p o ec ion and he condi ioning needed o ealize any kind o
ac i i ies in i s.
1.1 Backg ound
The s uc u al sys em o hose buildings has h ee impo an unc ions. I has o be
e ically and ho izon ally s able, i also has o be esis an agains g a i y, ho izon al
ac ions like and clima e and heological ac ions and inally, i has o be igid enough
agains e ical and ho izon al ac ions [1].
Today’s exis ing s uc u es a e gene ally designed o a se ice li e in be ween 50 o 120
yea s, depending on he coun y and he buil asse .
Howe e , hey a e ine i ably exposed o he na u al en i onmen in which hey we e buil ,
i means ha po en ial ex e nal ho izon al ac ions such as wind o seismic ac ions can pu
in dange he s uc u e.
As i is gene ally done in he cha ac e iza ion o wind ac ion by measu ing he eal
p essu e g adien ac ing on a ealis ic s uc u e simula ion in wind unnels, i is also
Figu e 1: Concep ual diag am o he u iliza ion o SHM
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Accele ome e s a e essen ial o de ec ing ib a ions and dynamic esponses o s uc u es.
They measu e he a e o change in eloci y which allows o con ey he mo ion o he
s uc u es unde dynamic loads, such as wind, ea hquakes o e en a ic loads. Fo
example, he deploymen o accele ome e s on he Golden Ga e B idge helps moni o
ib a ions caused by wind and a ic [16].
S ain gauges a e employed o measu e he s ain in s uc u al componen s. These senso s
help de ec s ess dis ibu ion and de o ma ion, p o iding insigh s in o po en ial
weaknesses and a igue in ma e ials. Fo ins ance, s ain gauges we e used in he
moni o ing o he Millau Viaduc in F ance o assess he b idge’s load-bea ing capaci y
[17].
Displacemen senso s, including inclinome e s, a e used o measu e he displacemen and
angula il o s uc u es. Inclinome e s a e pa icula ly e ec i e in moni o ing landslides,
dam mo emen and he il o all buildings. A ele an example is he use o
inclinome e s o moni o he Leaning Towe o Pisa’s il co ec ion e o s [18].
The use o hese senso s in SHM enables he con inuous moni o ing o s uc u al
esponses, ea ly de ec ion o peni en ial ailu es, and he implemen a ion o imely
main enance s a egies, he eby enhancing sa e y and p olonging he li espan o
in as uc u e.
2.3 Low-Cos Senso s
Recen ad ancemen s in senso echnology ha e led o he de elopmen o low-cos
senso s o SHM applica ions. These senso s o e an economical al e na i e o
widesp ead moni o ing wi hou comp omising essen ial pe o mance cha ac e is ics.
Examples o low-cos senso s include Mic o-Elec o-Mechanical Sys ems (MEMS)
accele ome e s, ibe op ic senso s, and piezoelec ic senso s. MEMS accele ome e s,
such as ADXL345, p o ide cos -e ec i e ib a ion o moni o ing solu ions sui able o
widesp ead deploymen ac oss in as uc u e ne wo ks [19].
Fibe op ic senso s, like Fibe B agg G a ing (FBG) senso s, ha e seen cos educ ion
h ough mass p oduc ion and imp o ed manu ac u ing p ocesses [20]. Piezoelec ic
senso s, known o hei simplici y and cos -e ec i eness, a e used o de ec ib a ions
and p essu e changes in s uc u al componen s, exempli ied by he use o PZT senso s in
b idge moni o ing [21].
Addi ionally, he Low-cos Adap able Reliable Accele ome e (LARA) senso , o e s an
inno a i e and a o dable solu ion o eal- ime s uc u al moni o ing. LARA senso s a e
designed o deli e accu a e ib a ion da a wi h minimal ene gy consump ion, making
hem sui able o la ge-scale deploymen s in in as uc u e ne wo ks. A no able example
o hei applica ion is in he moni o ing o Teh an’s Milad Towe , whe e LARA senso s
a e u ilized o de ec s uc u al ib a ions and po en ial s ess poin s [22].
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Technological ad ancemen s in wi eless communica ion, da a p ocessing and ene gy
ha es ing ha e u he enhanced he capabili ies o low-cos senso s. These
imp o emen s allow o mo e ex ensi e and eal- ime da a collec ion, leading o mo e
in o med decision-making in SHM. In addi ion o, he in eg a ion o In e ne o Things
(IoT) echnologies acili a es emo e moni o ing and da a analy ics by educing he need
o manual inspec ions and lowe ing ope a ional cos s [23].
2.4 Comme cial Senso s
On ano he hand, comme cial-g ade senso s a e designed o high-pe o mance SHM
applica ions whe e accu acy, du abili y and eliabili y a e pa amoun . These senso s
include high-p ecision accele ome e s, lase displacemen senso s and ad anced ibe
op ic senso s.
High-end accele ome e s used in comme cial SHM sys ems, such as he PCB
Piezo onics accele ome e MEMS se ies, p o ide supe io sensi i i y and s abili y,
enabling accu a e de ec ion o e en he sligh es s uc u al mo emen s [24]. They ha e a
p ice o a ound $500 [25].
Lase displacemen senso s, like he Keyence LK-G se ies, o e non-con ac
measu emen capabili ies wi h excep ional p ecision, making hem ideal o moni o ing
c i ical in as uc u e such as b idges and high- ise buildings [26].
Ad anced ibe op ic senso s, such as hose de eloped by Luna Inno a ions, a e p ized
o hei immuni y o elec omagne ic in e e ence and abili y o ope a e in ha sh
en i onmen s, ensu ing consis en pe o mance o e ex ended pe iods [27].
Usually, he ea u e o comme cial senso s include ugged designs, high da a esolu ion
and ad anced signal p ocessing capabili ies. Thei eliabili y is c i ical o applica ions
whe e da a accu acy can in luence sa e y-c i ical decisions. In a same way, he cos o
hese senso s in in gene al much highe due o hei specialized ma e ials, sophis ica ed
designs and igo ous es ing s anda ds.
Cos conside a ions in ol e no only he ini ial pu chase p ice bu also ins alla ion,
calib a ion and main enance expenses. Fo now and despi e highe cos s, he long- e m
bene i s o deploying comme cial-g ade senso s in c i ical in as uc u e moni o ing
explains he jus i ica ion o he in es men h ough enhanced sa e y, educed down ime
and p olonged se ice li e o s uc u es.
2.5 Seismic Simula ions in SHM
Seismic simula ions play a c ucial ole in es ing and alida ing S uc u al Heal h
Moni o ing (SHM) sys ems. By eplica ing he dynamic e ec s o seismic e en s, hese
simula ions enable enginee s o e alua e how s uc u es and hei moni o ing sys ems
espond o ea hquake-induced s esses. This p oac i e app oach helps iden i y
ulne abili ies and imp o es he eliabili y o SHM sys ems unde eal-wo ld seismic
condi ions.
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Common me hodologies o seismic simula ions include nume ical modeling and
physical shake able es ing.
Nume ical modeling u ilizes ini e elemen analysis so wa e, such as GiD o SAP2000,
o simula e how s uc u es eac o a ious seismic loads. These models help p edic
s uc u al beha io , op imize senso placemen and e alua e sys em pe o mance unde
di e en ea hquake scena ios.
Shake able es ing in ol es placing scaled s uc u al models on a mo able pla o m ha
simula es g ound mo ions. Facili ies like UC San Diego La ge High-Pe o mance
Ou doo Shake Table (LHPOST) and he Uni e si a Poli ècnica de Ca alunya (UPC) in
Ba celona ha e been pi o al in conduc ing seismic es s on s uc u al models [28].
Addi ionally, ools such as OpenSees (Open Sys em o Ea hquake Enginee ing
Simula ion) p o ide open-sou ce pla o ms o seismic analysis, enabling de ailed
simula ions o s uc u al esponses. Hyb id simula ion echniques ha combine physical
es ing wi h nume ical models u he enhance he accu acy and e iciency o seismic
assessmen s.
Seismic simula ions a e indispensable o alida ing SHM sys ems, op imizing senso
ne wo ks, and de eloping mo e esilien in as uc u e. They enable enginee s o design
sa e s uc u es, educe ea hquake- ela ed isks, and ensu e ha SHM sys ems emain
e ec i e du ing and a e seismic e en s.
2.6 Summa y
This chap e e iews he key hemes su ounding SHM, including i s sys ems, senso
ypes, cos conside a ions, and seismic simula ions. S uc u al Heal h Moni o ing
in ol es s a egies o de ec and cha ac e ize damage in s uc u es such as b idges,
buildings, and ai c a , enhancing sa e y, p e en ing ailu es, and op imizing main enance
schedules.
E ec i e SHM design equi es cha ac e izing he s uc u e, iden i ying measu able
phenomena, selec ing sui able senso s, and using obus da a acquisi ion sys ems.
S a is ical me hods play a i al ole in diagnosing s uc u al heal h issues. Common SHM
senso s include accele ome e s o de ec ing ib a ions, s ain gauges o measu ing
s ess, and displacemen senso s like inclinome e s o moni o ing s uc u al il and
de o ma ion. Recen ad ancemen s in senso echnology ha e led o cos -e ec i e
solu ions such as low-cos MEMS accele ome e s enabling widesp ead SHM
deploymen .
On he o he hand, high-end comme cial senso s, including lase displacemen and
ad anced ibe op ic senso s, o e supe io p ecision and du abili y o c i ical
in as uc u e, jus i ying hei highe cos s. Seismic simula ions, including nume ical
modeling and shake able es ing, eplica e ea hquake impac s o alida e SHM sys ems.
Tools like nume ical analysis so wa e op imize senso esea ches and by he same way,
imp o e s uc u al esilience agains seismic e en s. These echnologies and me hods
collec i ely con ibu e o sa e , mo e eliable, and cos -e ec i e s uc u al moni o ing
sys ems.
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Chap e 3: Senso Speci ica ions and Compa a i e F amewo k
In his chap e , wo senso s will be analyzed mo e in de ails. Fi s he LARA one will be
chosen o play he ole o he low-cos senso . Howe e , as he comme cial senso used
o his hesis expe imen is common and unknown, a e y amous comme cial one will
be used o play he ole o he comme cial senso , namely he PCB 3713B112G.
3.1 Low-Cos Senso
The Low-cos Adap able Reliable Accele ome e (LARA) is a specialized solu ion
designed by UPC esea che s o add ess he inancial cons ain s associa ed wi h
adi ional S uc u al Heal h Moni o ing sys ems. SHM is i al o ensu ing he sa e y and
du abili y o ci il in as uc u e by de ec ing and cha ac e izing damage in i s ea ly s ages,
hus p e en ing cos ly epai s and ensu ing ope a ional sa e y.
Conce ning he ha dwa e pa , LARA is a low-cos iaxial accele ome e de eloped
using A duino echnology, op imized o SHM applica ions. I ha dwa e consis s o
se e al componen s. The MEMS Accele ome e Senso in LARA p o ides p ecise
accele a ion measu emen s in h ee axes. The A duino Mic ocon olle ac s as he co e
p ocessing uni esponsible o da a acquisi ion, signal p ocessing, and communica ion,
managing da a collec ion and synch oniza ion. Mul iplexe s and cables a e u ilized o
connec ing mul iple senso s and ensu ing eliable da a ansmission. The sys em is
powe ed by a ba e y-powe ed supply designed o ield deploymen , ensu ing con inuous
ope a ion. P o ec i e enclosu es sa egua d in e nal elec onics om en i onmen al
ac o s.
Conce ning he LARA so wa e a chi ec u e, LARA’s amewo k in eg a es a ious
componen s o e icien da a acquisi ion and p ocessing. The da a acquisi ion module
cap u es accele a ion da a om he MEMS senso a a sampling a e o 345 Hz, op imized
o s uc u al moni o ing. A synch oniza ion algo i hm ensu es p ecise ime alignmen o
da a om mul iple senso s using a imes amp mechanism wi h mic osecond esolu ion.
Noise il e ing and signal p ocessing algo i hms imp o e da a quali y, add essing issues
like high noise densi y. The da a esampling module adjus s da a sampling a es o
compa ibili y wi h equency domain analysis me hods. A da a ansmission p o ocol
acili a es da a ans e om he accele ome e o s o age o analysis sys ems, le e aging
In e ne o Things (IoT) echnology o emo e moni o ing.
Figu e 4: Pic u e o he LARA sensing pa and i acquisi ion pa
Sensing Pa
Acquisi ion Pa
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3.2 Comme cial Senso
On he o he hand, The PCB 3713B112G is a high-end iaxial MEMS accele ome e
designed o p ecision s uc u al moni o ing applica ions. I ha dwa e ea u es include
ad anced MEMS sensing elemen s ha deli e ul a-low noise densi y and high
sensi i i y. The accele ome e is in eg a ed wi h high-pe o mance signal condi ioning
elec onics o ensu e accu a e da a acquisi ion. I ope a es wi h a wide dynamic ange and
is enclosed in a obus , en i onmen ally sealed casing o du abili y in ha sh condi ions.
The de ice suppo s high-speed da a ansmission ia indus ial-g ade connec o s and
powe supply s abili y o con inuous long- e m moni o ing.
Mo eo e , he PCB 3713B112G employs sophis ica ed embedded so wa e o eal- ime
da a acquisi ion and p ocessing. I includes ad anced il e ing algo i hms o elimina e
noise and signal dis o ions, p o iding high- ideli y da a. The senso in eg a es seamlessly
wi h da a acquisi ion sys ems h ough indus y-s anda d p o ocols. I s so wa e suppo s
cus omizable da a sampling a es and au oma ic calib a ion ou ines o main ain
p ecision. Addi ionally, he senso o e s emo e moni o ing and diagnos ics h ough
secu e communica ion in e aces, ensu ing eliabili y in c i ical moni o ing scena ios.
3.3 Compa a i e F amewo k
To ge an idea o he p ice di e ence be ween hese wo senso s, i is impo an o
compa e hei amewo ks in o de o ind plausible explana ions o his cos gap o
a ound 2000€.
He eina e a able esuming he p incipal cha ac e is ics o he wo compa ed senso s.
Bo h o hem o e a obus p o ec ion and ha e he same measu emen ange bu i is
impo an o no ice ha he e a e some disc epancies which can explain a pa o he p ice
gap. Indeed, he LARA’s noise densi y is mo e han wice as much as he comme cial
one, indica ing lowe pe o mance in en i onmen s whe e low noise is c i ical. Mo eo e ,
he in e al o equency esponse o he comme cial senso is ou imes wide han he
LARA one, making i mo e sui able o applica ions equi ing de ec ion o highe
equency ib a ions. Meanwhile he comme cial senso o e s highe esolu ion and
highe sensi i i y, making i be e o small dynamic cha ges, he LARA senso has a
highe sampling a e, enabling i o cap u e mo e da a poin s in a gi en ime.
Figu e 5: Pic u e o he PCB 3713B112G senso wi hou and wi h connec ion cable
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I em
PCB 3713B112G
LARA
Type o Model
MEMS accele ome e
MEMS accele ome e
Dimension
20,3 x 20,3 x 20,3mm
40 x 40 x 5mm
Measu emen Range as accele ome e
± 2.0 g
± 2.0 g
Noise densi y as accele ome e
22,9µg/√Hz
51µg/√Hz
Resolu ion as accele ome e
250µg
31µg
F equency Response as accele ome e
0 – 250Hz
0 – 61Hz
Sampling Ra e as accele ome e
200 Hz
345 Hz
Sensi i i y as accele ome e
1000 mV/g
625 mV/g
Ope a ing empe a u e
-54oC o +121oC
-40oC o +85oC
Communica ion
In eg al cable
E he ne o USB
Powe Sou ce
In eg al cable
E he ne o USB
Powe Consump ion
≤ 6mA
200mA @ 5V
Wa e p oo
He me ic sealing
IP67 complian
Da a collec ion
In eg al cable
Wi eless
Table 4: Compa a i e able o he cha ac e is ics o he senso s
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Chap e 4: Me hodology
In his chap e , we will discuss he me hodology used o success ully conduc he
expe imen al s udy aimed a compa ing comme cial and low-cos senso s. To do so, he
expe imen al se up will de de ailed along wi h all he heo y equi ed o pe o m he da a
analysis.
4.1 Expe imen al Se up
The expe imen ook place du ing summe 2024 in China. This in ol es se ing up a h ee-
s o y minia u e building s uc u e a a scale o app oxima ely 1:10, equipped wi h a low-
cos senso and a comme cial one on each loo , as shown in he pic u e aken du ing he
execu ion o his expe imen below. Addi ionally, he s uc u e was placed on a shaking
able o subjec i o ea hquake-like mo emen s [29]. The shaking able will exci e he
s uc u e in a unique di ec ion, namely he Y-di ec ion in his case.
Figu e 6: Pic u e o he expe imen al se up aken he 08/07/2027
LARA 1 + Comme cial
LARA 2 + Comme cial
LARA 3 + Comme cial
LARA 4 + Comme cial
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As i can be easily seen, he e a e ou s o ies o senso s. Then, he e will be eigh sou ces
o da a collec ion which will ha e o be well o ganized o easie compu e hem o he
analysis. To do so, he low-cos senso s will ha e a numbe om 1 o 4, 1 co esponding
o he op one, and ou o he bo om one.
Now, o alk abou he layou o he s uc u e, i is impo an o e e o he design d awing
ile he Chinese enginee s ealized. I is a basic layou o a h ee-s o y building wi h ou
co ne columns made o an aluminum alloy and wi h he ollowing cha ac e is ics:
755𝑚𝑚 𝑙𝑜𝑛𝑔 / 50𝑚𝑚 𝑤𝑖𝑑𝑒 / 2𝑚𝑚 𝑡ℎ𝑖𝑐𝑘
As he spacing o he s o ies is abou 240mm, i he scale is abou 1:10, i would mean
ha he eal building ep esen ed should be a h ee-s o y building o 7,55m heigh , wi h
h ee s o ies o abou 2,50m heigh as i is illus a ed in he ollowing pic u e:
4.2 Theo y
In his pa , i will be explained all he heo e ical knowledge which is equi ed o ealize
he da a analysis o he low-cos and comme cial senso s. As he s uc u e is al eady well
de ined, i is now he ime o go u he and o de ine he cha ac e is ics o each elemen s
o he s uc u e wi h he ollowing scheme:
Figu e 7: Pic u e o he design d awing in Au oCAD ansla ed om Chinese
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By using he New on’s laws applied o he solid, he e he h ee slabs:
∑𝐹𝑒𝑥𝑡=𝑚𝑎
(1)
I is now possible o deduce ha he sys em o sol e om he h ee s o y building is:
𝑚1𝑢1(𝑡)=−𝑘1𝑢1(𝑡)−𝑐1𝑢1(𝑡)+𝑘2(𝑢2(𝑡)−𝑢1(𝑡))+𝑐2(𝑢2(𝑡)−𝑢1(𝑡))
(2)
𝑚2𝑢2(𝑡)=𝑘2(𝑢2(𝑡)−𝑢1(𝑡))+𝑐2(𝑢2(𝑡)−𝑢1(𝑡))−𝑘3(𝑢3(𝑡)−𝑢2(𝑡))−𝑐3(𝑢3(𝑡)−𝑢2(𝑡))
(3)
𝑚3𝑢3(𝑡)=𝑘3(𝑢3(𝑡)−𝑢2(𝑡))+𝑐3(𝑢3(𝑡)−𝑢2(𝑡))+𝑝3(𝑡)
(4)
As he s uc u e is ee o mo e and so he op slab is also ee, i can be assumed ha
𝑝3(𝑡)=0.
The equa ion (4) becomes:
𝑚3𝑢3(𝑡)=𝑘3(𝑢3(𝑡)−𝑢2(𝑡))+𝑐3(𝑢3(𝑡)−𝑢2(𝑡))
(5)
To acili a e he sol ing o he h ee equa ions sys em cons i u ed o equa ions (2), (3) and
(5), a ma ix o m is p esen ed as below:
Figu e 8: De o ma ion scheme o he s uc u e
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𝑀𝑈(𝑡)+𝐶𝑈(𝑡)+𝐾𝑈(𝑡)=0
(6)
Wi h he ollowing de ined ma ixes:
𝑀=(𝑚10 0
0 𝑚20
0 0 𝑚3)
𝐶=(𝑐1+𝑐2−𝑐20
−𝑐2𝑐2+𝑐3−𝑐3
0 −𝑐3𝑐3)
𝐾=(𝑘1+𝑘2−𝑘20
−𝑘2𝑘2+𝑘3−𝑘3
0 −𝑘3𝑘3)
And he displacemen unc ion wi h i s ime de i a i es:
𝑈(𝑡)=(𝑢1(𝑡)
𝑢2(𝑡)
𝑢3(𝑡))
𝑈(𝑡)=(𝑢1(𝑡)
𝑢2(𝑡)
𝑢3(𝑡))
𝑈(𝑡)=(𝑢1(𝑡)
𝑢2(𝑡)
𝑢3(𝑡))
Now, he p oblem esides in sol ing he equa ion (6) which has a o m o an homogeneous
sys em and, o do so, i is impo an o look back on s uc u al dynamics.
4.2.1 S uc u al Dynamics
S uc u al dynamics is a comp ehensi e s udy o s uc u es beha io no only unde
ex e nal exci a ions bu also in he absence o ex e nal o ces whe e dynamic
cha ac e is ics o he s uc u e in luence i beha io , including ee ib a ions.
As he s udy is based on a single deg ee o eedom, namely he Y di ec ion (see Figu e
5), he Single Deg ee o F eedom (SDOF) model will be used o sol e equa ion (6). I
assumes ha he s uc u e can be ep esen ed as a mass 𝑚 a ached o a sp ing 𝑘 and a
damping sys em 𝑐.
The equa ion o mo ion o an SDOF sys em is as ollows:
𝑚𝑥(𝑡)+𝑐𝑥(𝑡)+𝑘𝑥(𝑡)=0
(7)
I we conside he undamped case, igno ing damping, he equa ion (6) is educed o:
𝑚𝑥(𝑡)+𝑘𝑥(𝑡)=0
(8)
Assuming ha he sys em unde goes ha monic mo ion in ee ib a ion because no
ex e nal o ces a e ac ing, equa ion (8) can be w i en as:
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A e ha , Ope a ional Modal Analysis (OMA) is pe o med using he F equency Domain
Decomposi ion (FDD) me hod o analyze he s uc u al beha io . MATLAB's eely
a ailable FDD code is employed [32] o de i e c i ical dynamic cha ac e is ics om he
accele a ion da a eco ded du ing he moni o ing o ci il enginee ing s uc u es exposed
o en i onmen al noise. This analysis compu es he mode shapes, he eigen equencies
and he modal damping a ios.
Then, o co ela e he accu acy o he wo di e en ypes o senso s, a compa a i e s udy
will be ca ied ou be ween i s eigen equencies and i s mode shapes. To do so, i equi es
new ma hema ical ools such as he Modal Assu ance C i e ion (MAC), which is a
s a is ical indica o used o quan i y he co ela ion be ween wo mode shapes, also called
eigen ec o s. On he o he hand, he Absolu e Rela i e E o (ARE) will help quan i ying
he accu acy o he measu ed eigen equencies o bo h senso s.
4.6.1 Absolu e Rela i e E o
In o de o compa e he accu acy o he measu ed eigen equencies o he wo senso s,
he absolu e ela i e e o o mula will be used.
𝐴𝑅𝐸%=|𝑓𝑖,𝐿𝐴𝑅𝐴−𝑓𝑖,𝑐𝑜𝑚𝑚𝑒𝑟𝑐𝑖𝑎𝑙
𝑓𝑖,𝑐𝑜𝑚𝑚𝑒𝑟𝑐𝑖𝑎𝑙 |×100
(17)
Whe e,
- 𝑓𝑖,𝐿𝐴𝑅𝐴 ep esen s he 𝑖𝑡ℎ eigen equency o he LARA senso
- 𝑓𝑖𝑐𝑜𝑚𝑚𝑒𝑟𝑐𝑖𝑎𝑙 ep esen s he 𝑖𝑡ℎ eigen equency o he comme cial senso
4.6.2 Modal Assu ance C i e ion
To compa e he mode shapes o he wo senso s unde s udy, i is essen ial o compu e
he MAC. To calcula e he MAC o wo 4x4 ma ixes, hese ones should be compa ed in
a way ha gene alizes he ec o o mula, namely:
𝑀𝐴𝐶(𝐶,𝐿)=|𝑣𝑒𝑐(𝐶)𝑇𝑣𝑒𝑐(𝐿)|²
(𝑣𝑒𝑐(𝐶)𝑇𝑣𝑒𝑐(𝐶)).(𝑣𝑒𝑐(𝐿)𝑇𝑣𝑒𝑐(𝐿))
(18)
Whe e, - 𝐶 e e s o he comme cial senso mode shape ma ix
- 𝐿 e e s o he comme cial senso mode shape ma ix
- 𝑣𝑒𝑐(Φ) s acks he columns o Φ in a 16x1 ec o
- 𝑣𝑒𝑐(Φ)𝑇 is he anspose ma ix o Φ wi h a size 1x16
To calcula e i in an easie way, a MATLAB code will be used, namely
“compu e_MAC” (see he code in Appendix).
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Chap e 5: Expe imen al Resul s
In his chap e , all he expe imen al esul s will be p esen ed and o de ed ollowing he
line ime o he exci a ions he senso s we e subjec ed o.
5.1 Da a P esen a ion
A i s , i is impo an o p esen a gene al o e iew o he esponse o he LARA senso .
To do so, i will be selec ed he o al ime o he expe imen , ha is o say om 9:42:00
(UTC+7) o 10:01:00 (UTC+7).
Un o una ely, he da a o he comme cial senso was only collec ed du ing he exci a ions
pe iods. The e o e, he e is no comme cial da a du ing he ee mo emen pe iod o he
s uc u e unde s udy.
5.1.1 Ea hquake 1
To s a compa ing he wo senso s, i will be i s analyzed he seismic simula ion
“Ea hquake 1” om 9:42:02 (UTC+7) o 9:42:15 (UTC+7).
Figu e 12: Gene al o e iew o he LARA’s esponse
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By using he Picking me hod o he AFDD MATLAB code [32], i is possible o selec
he ou i s modes o ob ain he ou i s eigen equencies o bo h senso s.
Figu e 13: LARA’s esponse agains Ea hquake 1 exci a ion
Figu e 14: Comme cial peaks o equencies selec ed o “Ea hquake1”
Figu e 15: LARA’s peaks o equencies selec ed o “Ea hquake1”
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The isual esponse p esen s g ea simila i ies bu i has o be e i ied by calcula ing he
ARE and he MAC.
Ea hquake 1
Mode numbe
Comme cial (Hz)
LARA (Hz)
Rela i e E o (%)
1
3,12
3,03
2,84%
2
9,35
9,42
0,76%
3
13,64
13,80
1,17%
4
24,55
24,56
0,07%
The able con i ms he i s sigh by showing ha he ou i s eigen equencies a e e y
simila . Indeed, hey di e om a maximum o 2,84%.
Fu he mo e, he MAC is compu ed and con i ms e en mo e he simila i ies be ween he
wo esponses o bo h senso s.
Ea hquake1
Mode Shape Comme cial
Mode Shape LARA
MAC
-1
-0,808
-0,498
-0,048
-1
-0,822
-0,493
-0,072
0,9971
-0,769
0,354
1
0,012
-0,800
0,380
1
0,010
-0,504
1
-0,779
-0,008
-0,485
1
-0,744
0,015
-0,012
-0,031
0,124
-1
-0,110
-0,073
0,070
-1
5.1.2 Ea hquake 2
The same p ocedu e is used o ob ain he esul s o he “Ea hquake2”, om 9:46:10
(UTC+7) o 9:46:23 (UTC+7).
Table 5: 4 i s eigen equencies o he senso s du ing “Ea hquake1”
Table 6: 4 i s mode shapes o he senso s du ing “Ea hquake1”
Figu e 16: LARA’s esponse agains “Ea hquake2” exci a ion
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Again, he i s sigh seems o be e y simila o bo h g aphs. To e i y i , he ARE and
he MAC a e calcula ed as ollows.
Ea hquake 2
Mode numbe
Comme cial (Hz)
LARA (Hz)
Rela i e E o (%)
1
3,12
3,36
7,95%
2
9,35
9,42
0,76%
3
13,64
13,80
1,17%
4
15,97
15,93
0,30%
The able con i ms he i s sigh by showing ha he ou i s eigen equencies a e e y
simila . Howe e , he i s eigen equency di e s by 7,95%. We will y o explain ha
di e ence la e .
Table 7: 4 i s eigen equencies o he senso s du ing “Ea hquake2”
Figu e 17: Comme cial peaks o equencies selec ed o “Ea hquake2” exci a ion
Figu e 18: LARA’s peaks o equencies selec ed o “Ea hquake2” exci a ion
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Fu he mo e, he MAC is compu ed and con i ms e en mo e he simila i ies be ween he
wo esponses o bo h senso s.
Ea hquake2
Mode Shape Comme cial
Mode Shape LARA
MAC
-1
-0,802
-0,482
-0,023
-1
-0,824
-0,469
-0,003
0,9961
-0,768
0,348
1
0,031
-0,794
0,368
1
0,048
-0,501
1
-0,771
-0,024
-0,478
1
-0,791
0,014
-0,085
0,252
-0,528
1
-0,120
0,381
-0,614
1
5.1.3 Sine 1
As well as he wo i s cases, he same p ocedu e is used o ob ain he esul s o he
“Sine1”, om 9:48:34 (UTC+7) o 9:48:46 (UTC+7).
Table 8: 4 i s mode shapes o he senso s du ing “Ea hquake2”
Figu e 19: LARA’s esponse agains “Sine1” exci a ion
Figu e 20: Comme cial peaks o equencies selec ed o “Sine1” exci a ion
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To comple e he compa ison, he ARE and he MAC a e calcula ed as ollows.
Sine1
Mode numbe
Comme cial (Hz)
LARA (Hz)
Rela i e E o (%)
1
1,17
1,01
13,64%
2
3,12
3,36
7,95%
3
8,96
9,08
1,38%
4
14,03
13,80
1,64%
Sine 1
Mode Shape Comme cial
Mode Shape LARA
MAC
-1
-0,940
-0,955
-0,888
-1
-0,981
-0,948
-0,896
0,9972
-1
-0,801
0
-0,020
-1
-0,812
0
-0,011
-0,773
0
1
0,195
-0,799
0
1
0,185
-0,477
1
-0,909
0
-0,483
1
-0,782
0
5.1.4 Sine 2
As well as he h ee i s cases, he same p ocedu e is used o ob ain he esul s o he
“Sine2”, om 9:51:16 (UTC+7) o 9:51:30 (UTC+7).
Table 9: 4 i s eigen equencies o he senso s du ing “Sine1”
Table 10: 4 i s mode shapes o he senso s du ing “Sine1”
Table 8: 4 i s mode shapes o he senso s du ing “Ea hquake2”
Figu e 21: LARA’s peaks o equencies selec ed o “Sine1” exci a ion
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Figu e 22: LARA’s esponse agains “Sine2” exci a ion
Figu e 23: Comme cial peaks o equencies selec ed o “Sine2” exci a ion
Figu e 24: LARA’s peaks o equencies selec ed o “Sine2” exci a ion
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Below will be p esen ed he ables o ARE and MAC alues.
Sine2
Mode numbe
Comme cial (Hz)
LARA (Hz)
Rela i e E o (%)
1
1,17
1,01
13,64%
2
3,12
3,36
7,95%
3
9,35
9,42
0,76%
4
14,03
13,80
1,64%
Sine 2
Mode Shape Comme cial
Mode Shape LARA
MAC
-1
-0,939
-0,949
-0,878
-1
-0,977
-0,945
-0,908
0,995
-1
-0,804
0
-0,021
-1
-0,811
0
-0,004
-0,768
0
1
0,053
-0,790
0
1
0,054
-0,477
1
-0,891
0
-0,480
1
-0,796
0
5.1.5 Sine 3
As well as he ou i s cases, he same p ocedu e is used o ob ain he esul s o he
“Sine3”, om 9:54:03 (UTC+7) o 9:55:50 (UTC+7).
Table 11: 4 i s eigen equencies o he senso s du ing “Sine2”
Table 12: 4 i s mode shapes o he senso s du ing “Sine2”
Figu e 25: LARA’s esponse agains “Sine3” exci a ion
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Below will be p esen ed he ables o ARE and MAC alues.
Sine3
Mode numbe
Comme cial (Hz)
LARA (Hz)
Rela i e E o (%)
1
0,98
1,01
3,52%
2
2,98
2,99
0,41%
3
3,22
3,24
0,64%
4
4,00
4,00
0,06%
Table 13: 4 i s eigen equencies o he senso s du ing “Sine3”
Figu e 26: Comme cial peaks o equencies selec ed o “Sine3” exci a ion
Figu e 27: LARA’s peaks o equencies selec ed o “Sine3” exci a ion
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Re e ences
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Appendices
Appendix A: Technical shee LARA senso
I em
Desc ip ion
Type o Model
MEMS accele ome e /Inclinome e
Dimension
40 x 40 x 5mm
Measu emen Range as
accele ome e
± 2.0 g*
Noise densi y as
accele ome e
51µg/√Hz
Resolu ion as
accele ome e
31µg
F equency Response as
accele ome e
0 – 61Hz
Sampling Ra e as
accele ome e
333 Hz
Sensi i i y as
accele ome e
625 mV/g
Measu emen ange
(deg ees) as
inclinome e
0-4o
P ecision (deg ees) as
inclinome e
Up o 0.002 deg ees in S a ic mode
0.02 deg ees in Dynamic mode
Sampling a e as
inclinome e
Up o 333 Hz
Ope a ing empe a u e
-40oC o +85oC
Communica ion
E he ne o USB
Powe Sou ce
E he ne o USB
Powe Consump ion
200mA @ 5V
Wa e p oo
IP67 complian
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Appendix B: Technical shee PCB 3713B112G senso
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Appendix C: Ma lab code “Timeselec ing”
Appendix D: Ma lab code “Compu e_MAC”
