Intelligent analysis for historical macroseismic damage scenarios Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Cinzia Zotta - Archaeological and monumental heritage

Intelligent Analysis of Environmental Data (S4 ENVISA Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009) 18-20 June 2009, University of Palermo, Workshop 2009) 18-20 June 2009, University of Palermo, ItalyItaly Intelligent analysis for Intelligent analysis for

historical macroseismic historical macroseismic damage scenariosdamage scenarios

Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Cinzia Zotta,

Laboratory of Urban and Territorial Systems, University of Basilicata, Italy

Lucia Tilio, Maria Danese, Beniamino Murgante

Archaeological and monumental heritage institute, National Research Council, Italy

Intelligent Analysis of Environmental Data (S4 ENVISA Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009) 18-20 June 2009, Palermo, ItalyWorkshop 2009) 18-20 June 2009, Palermo, Italy

Intelligent analysis for historical macroseismic damage Intelligent analysis for historical macroseismic damage scenarios scenarios Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Cinzia Zotta, Maria Danese, Cinzia Zotta, Lucia Tilio, Beniamino Murgante Lucia Tilio, Beniamino Murgante

IntroductionIntroduction

Analysis concerning earthquake events, are normally strictly related to damage survey.

It is evident that documentary sources concerning urban historical damage can provide useful information for seismic microzonation.

This research concerns historical earthquake (1930) damage related to towns of a seismic area of southern Italy (Vulture district, Basilicata).

4,000 dossiers compiled by the Special Office of Civil Engineers have been analyzed.



IntroductionIntroduction

Why Rough Set Analysis for the analysis of earthquake events?

o The aim is to verify the dependence of the damage level attribution to each building from some socio-economical local dynamics

o All available variables have been take into account and searching some patterns, able to create a cross-data control.



Information System Information System A) (U, IS

Rough setRough set

attribute) of (domain set value V Aa a

Let U be a nonempty finite set of objects called the universe

Let A be a nonempty finite set of attributes

nxxxxxxx ,...,......... , , , ,,U 654321

3 2 1 ,, A AAA

4 ,3,2, 1V

2, 1V

3,2, 1V

3

2

1



Information System Information System

Rough setRough set

U a1 a2 a3

x1 2 1 3

X2 3 2 1

X3 2 1 3

X4 2 2 3

X5 1 1 4

X6 1 1 2

X7 3 2 1

X8 1 1 4

X9 2 1 3

x10 3 2 1

function ninformatio V U:f aa



Rough setRough set

U a1 a2 a3 d1

x1 2 1 3 1

X2 3 2 1 4

X3 2 1 3 5

X4 2 2 3 2

X5 1 1 4 2

X6 1 1 2 4

X7 3 2 1 1

X8 1 1 4 2

X9 2 1 3 3

x10 3 2 1 2

Decision SystemDecision System

A decision system is an information system in which the values of a special decision attribute classify the cases

Attributes lConditiona

d-A attributes other a

Ad )A (U, DS d



Indiscernibility RelationIndiscernibility Relation (B) Ind AB

Bb )b()b( (B) Ind are e jiji xxxx

Rough set Rough set

o The equivalence class of Ind (B) The equivalence class of Ind (B) is called ELEMENTARY SETis called ELEMENTARY SET in Bin B

o For any element xi of U, the EQUIVALENCE CLASSEQUIVALENCE CLASS of R containing xi in relation Ind (B) will be denoted by [Xi] ind B

U/A a1 a2 a3

(X1 , X3 , X9 ) 2 1 3

(X2 , X7 , X10 ) 3 2 1

(X4) 2 2 3

(X5 , X8 ) 1 1 4

(X6) 1 1 2

(X7) 3 2 1



)( XxUxLX Bindii

LXUXBX

)( XxUxUX Bindii

Rough setRough set

Equivalence classes

Lower Approximation

Upper Approximation

Boundary Region

)(/)()( UXcardLXcardXB If BX = then the set X is

Crisp If BX ≠ then the set X is

Rough

Accuracy



Rough set Rough set

Rough membershipRough membership

In order to have an idea about how much an object x belongs to X we define rough membership.

)(

)()()(

)( and [0,1] : )(Bindi

BindiBindX

BindX

x

XxxUx

The rough membership function quantifies the degree of relative overlap between the set X and the equivalence class to which x belongs.



Rough set Rough set

ReductsReducts

A reduct eliminate redundant attributesA reduct is a minimal set of attributes (from the

whole attributes set) that preserves the partitioning of the of U and therefore the original classes.



Rough set Rough set

ReductsReducts

Color Size Shape Accept

x1 G Small Square Yes

x2 B Medium Triangular No

x3 R Small Rectangular

No

x4 G Medium Rectangular

Yes

x5 G Small Square Yes

x6 Y Large Round No

x7 Y Medium Triangular Yes

x8 B Medium Triangular No



Rough setRough set U = {x1, x2, x3, x4, x5, x6, x7, x8}

A = {color, size, shape} color(green, blue, red, yellow) size(small, large, medium)

shape(square, round, triangular, rectangular)

U/color = {(x1, x4, x5), (x2, x8), (x3), (x6, x7)}

U/size = {(x1, x3, x5), (x6), (x2, x4, x7 , x8)}

U/shape = {(x1, x5), (x6), (x2, x7 , x8), (x3 , x4 )}



Rough setRough set

U/IND(A) = {(x1, x5), (x2, x8), (x3), (x4), (x6), (x7)}

U/ IND(A –{color}) = {(x1, x5), (x2, x7 , x8), (x3), (x4) (x6)} U/IND(A)

U/ IND(A –{size}) = {(x1, x5), (x2, x8), (x3), (x4), (x6), (x7)}= U/IND(A)

U/ IND(A –{shape}) = {(x1, x5), (x2, x8), (x3), (x4), (x6), (x7)}= U/IND(A)

RED(A) = {(color, size), (color, shape)}

CORE(A) = {color}



Case StudyCase Study

Rapolla

Earthquake 1930

Buildings damage survey 738

Attributes 37

Which relationship between damage and reconstruction







Busta Fasc Ditta

Owner

Partita

Synthetic cadastral data

Mappale

IndirizzoAddress

Confini dell'immobileNeighbours

ImpresaContractor

Mappale

Parcel

sub

sub

Sott

U

PT

G

IP

IF

IIP

IIFYES NO

Plans

U GF 1F 2F

Floors

YES NO

SectionsDETAILED CADASTRAL DATA

YES NO

Expiry

YES NO

Works carried out bynational government

Revocation of housing subsidies

MAIN TECHNICAL REPORT

pp DATA

Date

Inizio lavori

Fine lavori

PP imp Proposto: PP DMLP data

PP DMLP N

Date

N.

Cost Decree

Supplementary technical report

PS data

PS importo

Date

Cost:

Supplementary subsidy

PSS data

PSS importo

Date:

Cost :

TEST (acceptance of work) CC data

CC imp1

Work time

From

To

Work costs

Ministry comunication

Total cost

Date

CM approvato

CM data1

CM sussidio

CM data2

Subsidy

Date

Prize for quick execution works

Date

USGCM date

Year income

Concession date

Data richiesta ditta

Data proposta Genio

Reddito annuo

Data concessione Ministero

DAMAGE

Direct

YES NO

NOTESNote

PP NN

PA percent%

Valore immobileProperty value

Imponibile fabbr

Cadastral rent

Imponibile totale fabbr

Particelle confinantiNeighbouring parcels urban rural

Sospensione dal

Sospensione al

From

To

Stoppage

YES NOPublic buildingb

GENERAL DATA AND TECHNICAL REPORT

N. fascicle N. tech. report

Form used in order to record and to analyse the documentary data



Case StudyCase Studya lot of information about

reconstruction

budget amount, effective expense, presence of some

interventions, building value, annual income

and so on…




Building IDReference – Map Reference – Envelope Reference – Folder Reference – Street Building demolitionPublic BuildingReligious Building Withdrawn subventionAssessment of damage DateCosts of works Effectively Funded

Start Work DateEnd Work DateReal estate values of Building Owner Annual IncomeAdoption of tie-beamRoof rebuilding Cracks rebuilding Test date Estimated costs of works Costs of works accounted

Data concerning information about the damage, the post-seismic repairing procedures with buildings techniques description of the housing units and technical-economic-administrative data.




Walls demolition Floors demolitionVault demolitionNew wallNew Floors Toothing projectsShearing stress of masonry (technical procedure for walls rebuilding) Cuci-Scuci (technical procedure for walls rebuilding) Damage description Declared Destroyed (if the building was damaged and declared not reconstructable)Damage class EMSPresence of caves under the building










}CONDITIONAL PART

ASSIGNMENT}
















There is a certain number of rules (25/88) that present a clear discrepancy into damage level attribution:

The analysis permits the identification of such discrepancy and a possible interpretation: differences in damage distribution are not spatially clusterized, but they concerns areas having different social and building features (rich and poor owners, big and small housing, building well preserved and lacking of maintenance ect.)




Changes in damage classification seem not to be due to voluntary human influences (e.g. acquaintance with technicians to get increase of damage attribution by favoritism) rather differences may be imputable to other factors, among which:

o Rough initial inspection of buildings (e.g. only some rooms were surveyed, damage assessment was carried out from outside of buildings).

o Different vocational training of engineers entrusted to survey affected housing units.




o Feature of damage description: during initial post-seismic phases, report of damage included improvements and/or extension works unrelated to the seismic event.

o Incompleteness of descriptive data: administrative/technical parametric information on which the rules are based on, sometimes supply more constraints of some very concise description of effects given by the engineer surveys.



Future developmentsFuture developments

New study area

It is known that during an earthquake the damage to buildings with comparable features can differ enormously between points.In a wider area it could be interesting to analyze also effects of geological surface.



Future developmentsFuture developments

Compare Rough Set results with other intelligent methods using Visual Analytics:

o Multiform Bivariate Matrix

o Self-Organising Map (SOM)

o Parallel Coordinates Plot (PCP)

Technology

Intelligent analysis for historical macroseismic damage scenarios Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Cinzia Zotta - Archaeological and monumental heritage