Appunti del corso di dottorato: Ottimizzazione Strutturale / Structural Optimization - parte II -...

Ottimizzazione Strutturale

franco.bontempi@uniroma1.it

Franco Bontempi

Ordinario di Tecnca delle Costruzioni

Facolta’ di Ingegneria Civile e Industriale

Sapienza Universita’ di Roma

Introduzione alla

OTTIMIZZAZIONE STRUTTURALE (parte II)

Object of the course

• Introduction of basic and advanced ideas

and aspects of structural design without to

much stress on the analytical apparatus

but with some insigth on the computational

techniques.

General Scheduling

• 1st Day:

Basic definitions of structure, requirements,

values, optimization, …;

• 2nd Day:

Advanced specific case of structural optimization

(service / ultimate / extreme scenarios);

• 3rd Day:

Advanced concepts (structural systems,

advanced criteria, tools of design)

5TECHNIQUES

FEW OBSERVATIONS

The function: y(x1,x2)

The sensibility of the function

STRATEGY #1: SENSITIVITY

governance of priorities

The boundings of the function

STRATEGY #2: BOUNDING

behavior governance

Design of Experiments (DOE)

• In general usage, design of experiments (DOE) or

experimental design is the design of any information-

gathering exercises where variation is present, whether under

the full control of the experimenter or not. However, in

statistics, these terms are usually used for controlled

experiments.

• Formal planned experimentation is often used in evaluating

physical objects, chemical formulations, structures,

components, and materials. Other types of study, and their

design, are discussed in the articles on computer

experiments, opinion polls and statistical surveys (which are

types of observational study), natural experiments and quasi-

experiments (for example, quasi-experimental design).

Sampling Points (1)

Sampling Points (2)

Simulation & Approximation

of the response (≈ surrogate)

The nature of optimum (1)

The nature of optimum (2)

A sub-optimal solution

to a problem is one

that is less than perfect.

Slack situation: loose and not pulled tight.

Example (1)

Example (2)

Bounded Rationality

Bounded rationality is the idea that in decision-making, rationality

of individuals is limited by the information they have, the

cognitive limitations of their minds, and the finite amount of time

they have to make a decision. It was proposed by Herbert A.

Simon as an alternative basis for the mathematical modeling of

decision making, as used in economics, political science and

related disciplines; it complements rationality as optimization,

which views decision-making as a fully rational process of finding

an optimal choice given the information available. Another way to

look at bounded rationality is that, because decision-makers lack

the ability and resources to arrive at the optimal solution, they

instead apply their rationality only after having greatly simplified

the choices available. Thus the decision-maker is a satisfier, one

seeking a satisfactory solution rather than the optimal one. Ottimizzazione Strutturale

Model Extensions

• Ariel Rubinstein proposed to model bounded rationality by

explicitly specifying decision-making procedures..

• Gerd Gigerenzer opines that decision theorists have not really

adhered to Simon's original ideas and proposes and shows

that simple heuristics often lead to better decisions than

theoretically optimal procedures.

• Huw Dixon later argues that it may not be necessary to

analyze in detail the process of reasoning underlying bounded

rationality. If we believe that agents will choose an action that

gets them "close" to the optimum, then we can use the notion

of epsilon-optimization, that means you choose your actions

so that the payoff is within epsilon of the optimum. The notion

of strict rationality is then a special case (ε=0).

OPTIMIZATION METHODS

Heuristics

Nelder – Mead

Genetic Algorithm

εὑρίσκω

• Heuristic (/hjʉˈrɪstɨk/; Greek:

"Εὑρίσκω", "find" or "discover") refers

to experience-based techniques for

problem solving, learning, and

discovery that give a solution which is

not guaranteed to be optimal. Where

the exhaustive search is impractical,

heuristic methods are used to speed

up the process of finding a satisfactory

solution via mental shortcuts to ease

the cognitive load of making a

decision. Examples of this method

include using a rule of thumb, an

educated guess, an intuitive judgment,

stereotyping, or common sense.

• In more precise terms, heuristics are

strategies using readily accessible,

though loosely applicable, information

to control problem solving in human

beings and machines.

• L'euristica (dalla lingua greca εὑρίσκω,

letteralmente "scopro" o "trovo") è una

parte dell'epistemologia e del metodo

scientifico.

• Si definisce procedimento euristico, un

metodo di approccio alla soluzione dei

problemi che non segue un chiaro

percorso, ma che si affida all'intuito e

allo stato temporaneo delle

circostanze, al fine di generare nuova

conoscenza. È opposto al

procedimento algoritmico. In

particolare, l'euristica di una teoria

dovrebbe indicare le strade e le

possibilità da approfondire nel

tentativo di rendere una teoria

progressiva.

Simulated Annealing (Metropolis)

• Simulated annealing (SA) is a generic probabilistic heuristic for the

global optimization problem of locating a good approximation to the

global optimum of a given function in a large search space.

• The name and inspiration come from annealing in metallurgy, a

technique involving heating and controlled cooling of a material to

increase the size of its crystals and reduce their defects.

• This notion of slow cooling is implemented in the Simulated

Annealing algorithm as a slow decrease in the probability of

accepting worse solutions as it explores the solution space.

Accepting worse solutions is a fundamental property of heuristics

because it allows for a more extensive search for the optimum.

• The method is an adaptation of the Metropolis-Hastings algorithm, a

Monte Carlo method to generate sample states of a thermodynamic

system, invented by M.N. Rosenbluth and published in a paper by

N. Metropolis et al. in 1953.

Basic version (1)

Basic version (2)

Points for SA

• Diameter of the search graph

• Transition probabilities

• Acceptance probabilities

• Efficient candidate generation

• Barrier avoidance

• Cooling schedule

Nelder-Mead Method (Amoeba)

• The Nelder–Mead method or downhill simplex

method or amoeba method is a commonly used

nonlinear optimization technique, which is a

well-defined numerical method for problems for

which derivatives may not be known.

• The Nelder–Mead technique is a heuristic

search method that was proposed by John

Nelder & Roger Mead (1965) for minimizing an

objective function in a many-dimensional space.

Remarks

Genetic Algorithm (GA)

• The original motivation for the GA approach was a biological

analogy. In the selective breeding of plants or animals, for example,

offspring are sought that have certain desirable characteristics,

characteristics that are determined at the genetic level by the way

the parents’ chromosomes combine. In the case of GAs, a

population of strings is used, i.e. chromosomes.

• The recombination of strings is carried out using analogies of

genetic crossover and mutation, and the search is guided by the

results of evaluating the objective function f for each string in the

population.

• Based on this evaluation, strings that have higher fitness (i.e.,

represent better solutions) can be identified, and these are given

more opportunity to breed.

Coding

• One of the distinctive features of the GA approach is to

allow the separation of the representation of the problem

from the actual variables in which it was originally

formulated. In line with biological usage of the terms, it

has become customary to distinguish the ‘genotype’—

the encoded representation of the variables, from the

‘phenotype’—the set of variables themselves.

Genotype space = {0,1}L

(mappa)

Phenotype space

(territorio)

Encoding

(representation)

Decoding

(inverse representation)

01101001

01001001

10010010

10010001

Translation

Costruzione della

popolazione iniziale

Valutazione della

funzione di fitness

di tutti gli individui

Selezione

Riproduzione

Crossover

Mutazione

M/2 cicli

Operatori

genetici

popolazione

Fine? No

Probabilità di crossover: 80%

Probabilità di mutazione: 1%

Mating, Mutation, Selection

OPTIMIZATION OF HCS

Precast hollow core slabs (HCS)

Tensile crack phenomena in HCS

(splitting, bursting, spalling).

• splitting cracks: caused by stresses resulting from

the development of prestressing in the anchorage

zone, that may generate traction stresses in the

concrete.

• bursting cracks: a local effect, generated by the

strand slippage into the slab end while the former

widens slightly on being cut.

• spalling cracks: occurring above the axis of the

strands in the HCS end zone, caused also by the

development of prestressing in the concrete at the

slab ends where only the lower part holding the

strands begins to be prestressed.Ottimizzazione Strutturale

Tensile crack phenomena in HCS

(splitting, bursting, spalling).

Cross-section of the reference HCS

and numerical model

Tensile deformations in the vertical

directions for the spalling effect

The binary coding of the geometry

characteristics of the section

• The fitness function F includes terms to represent the weight of the slab.

• First, functions gi(x), represents the geometry constraints, implicitly satisfied during the definition of the variable space.

• Functions hi(x) represent the structural safety constraints. In this study, two checks are carried out:1. the first one on the bending stress, carried out after the

initial structural analyses on the meso-scale model. 2. the second one, on the spalling stress, carried out on the

micro-scale model.

• If both checks are positive, the individual is fitting the constrain conditions, otherwise, it is discarded and a different element is introduced in the population.

Original values values obtained

after the optimization process

OPTIMIZATION OF S&T

1° Step

2° Step

OPTIMIZATION AS A TOOL

FOR EXPLORATION

aOttimizzazione Strutturale

Limit States

Service Limit States Ultimate Limit States

Prestressed Continuous Beam

Elements of nonlinear formulation

Equilibrium Equations

Nominal behavior

Level of uncertainty

Uncertainty

α - level

Random / Optimized Sampling

Cujaba River Bridge

Ultimate Limit States (ULS)

bOttimizzazione Strutturale

Uses of genetic algorithm

• To perform the stochastic exploration of the load space;

• To handle the uncertainties related to the definition of the loads;

• To investigate the global behavior of the structure by means of the definition of the envelope diagram of the performances;

• To define the worst load combination;

• To scrutinize the exact value of a specific performance.

Dissipation devices

Soil behaviorMaterial

Soil-Structure interface Contact

Support system

Cable system

Geometrical

Soil-Structure

Response

Vehicle-StructureWind-Structure

Nonlinearities

Interactions

Action

Uncertainties

Dependability attributes threats,

means and their interactions.

Performance in relation to the

return periods of the actions.

Geometry of the long-span

suspension bridge considered.

The design variables and

the performance levels

A genetic algorithm approach for

performance assessment• The performance of a long-span suspension bridge is

investigated by means of a GA approach. • Focus is given to three aspects of the structural behavior of

the bridge:1) maximum vertical displacement;2) maximum longitudinal and transversal slope;3) maximum tension in main cable and in the tower legs.

• The load scenarios that lead to the most severe performance metrics are explored in the space of the load variables by an optimization process based on GA’s.

• The implementation of a GA based optimization is essential since the traditional optimization techniques are rather ineffective, due to the high number of dimensions of the load variables space and the presence of numerous local optimum points.

Loading systems considered in the

genetic algorithm analysis.

Remarks on loading system

• Traffic and train loads are directed vertically but the possibility to have a longitudinal component due to the acceleration (A) or the deceleration (D) is also taken into account.

• In addition, a torque is present if the traffic loads are not positioned on the axis of the respective box girder section.

• The wind action, assumed always present and flowing transversally to the longitudinal axis of the bridge, produces lift, drag and torque.

• In order to represent analytically the entire loading system, 16 variables are needed.

• Since each of the girders is formed by 123 finite elements, the position of the loads will be defined by integer variables, ranging, in general, from 0 to 123.

Variables considered for the

definition of the loading system.

Description of loading system

Binary coding

• The position variables are implemented in binary

code with a dimension of eight bits (the minimum

dimension able to represent the position of the

loads on the bridge deck):

• In this row vector, x1 defines the position of the

train on the bridge deck, in binary code: for

example, if the train load starts from the fifteenth

element on the deck, the variable x1 is:

Population

• The GA starts by considering an initial population of N

row vector x created assigning random values to the

unknown variables; each row of the matrix X represents

the chromosome of one solution:

Target functions

• In order to evaluate the performance of the bridge, the

following six target functions are considered:

1. the vertical displacement (negative) for the bridge deck;

2. the horizontal displacement (positive) for the bridge

3. the longitudinal slope for the bridge deck;

4. the transversal slope for the bridge deck;

5. the axial tension for the main cables;

6. the stress state induced by the axial action and the two

bending moments for the bridge tower legs.

• Each performance is measured by the peak value over

all nodes of the bridge deck. Ottimizzazione Strutturale

Evolution of population

• For each target function, the genetic algorithm creates new populations of N row vector x in order to find the worse configurations of the considered loads.

• The genetic algorithm works by evaluating the target function in correspondence with each assumed vector x.

• If the population contains a N number of x vectors, the best N/2 vectors are saved in a new population while the other vectors are erased.

• To complete the new population, additional N/2 vectors are formed from the saved vectors using the genetic operator of mutation and crossover.

Mutation

• The mutation on the generic vector i of the population n changes a single bit of a randomly selected chromosome; for example provides the change from 1 to 0:

• As a result a new vector k is obtained for the population n+1.

Crossover

• The crossover on the generic vectors i and j of

population n is provided in this example:

• where a group of cells of chromosomes i and j is

selected and the respective states changed.

• As a result there are two new vectors k and l for the

population n+1. Ottimizzazione Strutturale

Remarks

• When N/2 new vectors are created, the genetic algorithm restarts with the evaluation of the target function for each vector xn+1.

• It should be observed that a genetic algorithm is a stochastic evolutionary procedure because the operators of mutation and crossover are not deterministic but there is a probability of occurrence for each operator.

• Usually the probability of occurrence of the mutation operator is low (0 – 5%) while the probability of occurrence of the crossover operator is high (70 – 90%).

• What makes this procedure attractive is the fact that usually there is a large interdependence between the quality of results and of the choice of these parameters.

• The FE model consists of 1614 elements (beams, no compression cable elements and gaps) and 1140 nodes.

• For each of the six previously chosen performance metrics (target functions), GA analysis is performed with an initial randomly chosen population of 100 chromosomes. For each chromosome the structural analysis, accounting for geometrical and material nonlinearities, is developed using ADINA, starting each time from the reference configurations under permanent loads and adding the traffic and wind loads.

• The custom software reads the output evaluation and performs the genetic recombination of the chromosomes to get a new generation of chromosomes: 100 cycles of regeneration are considered for a total of 10000 different load scenarios, each of them leading to a nonlinear structural analysis.

• The probability of occurrence of the crossover operator is of 80% while the probability of occurrence of the mutation operator is of 2%.

Remarks

• It is clear that the convergence of the variables that

define the train position (A) is better than the one that

defines the position of the light traffic load (B).

• From a design point of view, it means that the influence

of the railway load in defining the vertical displacement is

much higher than the traffic load.

• In addition, it can be observed that the railway loads

converge towards two different edges (North and South).

This is due to the fact that the geometry of the bridge is

almost symmetrical.

Vertical displacement envelope

Transversal slope envelope

Longitudinal slope envelope

Comparative importance

of the loads.

7THEORETICAL ASPECTS OF DESIGN

• Knowledge

• People

• Design process as a decision process

• Limits

• Scale effects

• Ergonomy

KNOWLEDGE

CONOSCENZA

RICHIESTA

DA UN PROGETTO

EVOLUTIVO

CONOSCENZA

RICHESTA

DA UN PROGETTO

INNOVATIVO

BASE DI

CONOSCENZA

ATTUALE

La crescita di conoscenze

Evolutive vs Innovative Design

156Ottimizzazione Strutturale

Knowledge Growth Process

KNOWLEDGE

REQUIRED

BY AN EVOLUTIVE

DESIGN

NEW KNOWLEDGE

REQUIRED BY

AN INNOVATIVE

DESIGN

ACTUAL

KNOWLEDGE BASIS

Evolutive Jump

Dalian, June 2008 167

Horizons

Failure due to errors

Failure due to unexpected facts

NASA System Complexity

Causes of system failure

Unknown phenomena

Known phenomena

Research

Design code

past present future

HPLC vs LPHC EVENTS

HPLCHIGH PROBABILITY

LOW CONSEQUENCES

LPHCLOW PROBABILITY

HIGH CONSEQUENCES

COMPLEXITY:Nonlinear Behavior andStructural Organization

PROBLEMFRAMEWORK

Deterministic

Stochastic

QUALITATIVE /DETERMINISTIC

ANALYSIS

QUANTITATIVEPROBABILISTIC

ANALYSIS

PRAGMATICSCENARIOS

ANALYSIS

8 e 9 luglio 2010

EXPLOSIONS - ESPLOSIONIhttp://www.francobontempi.org/handling.php

13 e 14 novembre 2008

FIRE - INCENDIOhttp://www.francobontempi.org/handling_08.php

PEOPLE

VALUES

Nonaka-Takeuchi Concept

Nonaka & Takeuchi:

conoscenza esplicite e implicite

The spiral of Knowledge

DESIGN PROCESS AS

DECISION PROCESS

LIMITS

Reaching limits

S Curve

SPAN 1100 1298 1385 1410 1624 1991 3300

Lorenz

Mental Heritage

Hybrid Solution

SCALE EFFECTS

Quebec Bridge

Quebec Bridge Failure

Chord Members

2nd Quebec Bridge

GLOBAL LEVEL

3300 mLocal level

Example: Size Effect

ERGONOMY

Interactions

i vita

8OPERATIVE ASPECTS OF DESIGN

• The structure of design

• The organization of the design process

• Complexity

• System decomposition

• Analysis models vs. design models

THE STRUCTURE

OF DESIGN

Meccanica Strutturale

Ingegneria Strutturale

Processo di analisi

e processo di sintesi (1)

CALCOLO

RISULTATI

MODIFICA

CALCOLO

RISULTATI

TESTSI’ NO

Pre-processing

Post-processing

Processo di analisi

e processo di sintesi (2)

MODIFICA

CALCOLO

RISULTATI

TESTSI’ NO

Sviluppo top-down

copertura omogenea

approfondimento

Sviluppo bottom-up

copertura omogenea

sviluppo di

un filone

Sviluppo mixed

Direct and Inverse problems in

Structural Engineering

• A direct problem is an analysis problem: it consists in the evaluation of the response of a structure immersed in its design environment, i.e. under assigned external actions and other boundary conditions as constrains, in agreement with the fulfilment of all the design constrains, by using a suitable model.

• Inverse problems are, on the other hand, those for which the structural response constitutes available known data. the inverse problems can be so classified:

1. Synthesis problems: given the actions and the constraints, the structure is designed to obtain a specific structural response;

2. Control problems: given a description for the structure and the mandatory structural response, an appropriate action to generate that response is searched;

3. Identification problems: given both the actions and the structural response, the model is looked for.

Data related to different

structural problems

Top-Down and Bottom-Up

approach for problem solving

aESEMPIO DI PROGETTO DI STRUTTURE

PRECOMPRESSE

Strutture precompresse

Problema diretto (analisi)

• Dato il tiro del cavo di

precompressione

• date le eccentricità

del cavo

trovare

• il diagramma del

momento dovuto alla

precompressione.

Problema inverso

(progetto)

• Dato il diagramma

del momento (dovuto

ai carichi esterni)

trovare

• il tiro del cavo di

precompressione

• e le eccentricità del

cavo nelle varie

sezioni.

ANALISI:dato il tiro del cavoe le sue eccentricità

si ricava il diagramma del momento

PROGETTO:dato il diagramma del momento (esterno)

trovare il tiro del cavoe le sue eccentricità

in modo da annullarlo momento

Reti neurali (1)

Reti neurali (2)

DIAGRAMMA DEL MOMENTO

TIRO DEL CAVOECCENTRICITA’

Discretizzazione

bESEMPIO DI IDENTIFICAZIONE STRUTTURALE

Tiro cavi all'ancoraggio

115000

120000

125000

130000

135000

140000

600 1100 1600 2100 2600 3100

Tempo (s)

Sponda siciliana, lato nord Sponda calabrese, lato nord

Sponda siciliana, lato sud Sponda calabrese, lato sud

AXIAL FORCE IN THE MAIN CABLES (1)

Vento = f(s,t)

FB 268

Tiro cavi all'ancoraggio

115000

120000

125000

130000

135000

140000

600 1100 1600 2100 2600 3100

Tempo (s)

Sponda siciliana, lato nord Sponda calabrese, lato nord

Sponda siciliana, lato sud Sponda calabrese, lato sud

AXIAL FORCE IN THE MAIN CABLES (2)

Vento = f(s,t)

FB 269

Hierarchical damage identification strategy

0 20 40 60 80

Damage detection

Identification of the

portion of the deck

element

STEP 1:

DAMAGE DETECTION

IDENTIFICATION OF

THE AREA

STEP 2:

IDENTIFICATION OF

THE ELEMENT

QUANTIFICATION OF

THE DAMAGE

Step 1a: approximation of the response

time-history using neural networks

Initial architecture

of the network

4 inputs + 4 hidden units

Measure of network

performance

wEERMS

Response time

history in sensor

ktf 2tf tf1tf 1tf

Structural system

Ambient excitation

600 1100 1600 2100 2600time

Training Test A Test B Test C

Step 1a: example – vertical displacement

0 25 50 75 100time

0 100 200 300 400time

Training TestA TestB TestC

0 25 50 75 100time

0 100 200 300 400 500time

Training TestA TestB TestC TestD

Training Undamaged

0 20 40 60 80

Damaged section

Error in function approximation in the undamaged sections

Training Undamaged

0 20 40 60 80

Training Undamaged

0 20 40 60 80

DETAIL

Training Undamaged

0 20 40 60 80

Training Undamaged

0 20 40 60 80

Training Undamaged

0 20 40 60 80

Step 1b: damage detection - identification of the area

Damage is intended as reduction

of stiffness in hangers, cables,

transverse beams.

- hangers: reduction from 5% to 80%

- cable: reduction from 1% to 10%

- transverse: reduction from 5% to 30%

Response time history

in sensor #n

1dtf 3tf 1tf2tf tf kntf 1ntf2ntf ntf

ntnt fye e 0

undamaged

Anomalies in section #n

M i optimal model

Network model M i

i = i +1

Network model M i+1

yes no ?1 ii MDpMDp

Damage in section #n

Structural

system

Ambient excitation

e > 0 in all

sections? Restart training from f(t+dt)

phase 2training test

Anomaly in section

Damage in section

Continu

e in time

Response time history

in sensor #m

Damage in the transverse (wind excitation)

30% 10% 5%

Damage in the transverse (train excitation)

0,3 0,1 0,05

Damage in the cable (train excitation)

30% 1% 0,50%

Damage in the cable (wind excitation)

10% 1% 0,50%

Damage in the hanger (wind excitation)

80% 50% 10% 5%

Damage in the hanger (train excitation)

50% 20% 10% 5%

Damage detection

using wind actions and traffic loads

hangers cables transverse

Mean values of the increment of the error with respect to the undamaged situation

1° finestra temporale

Vertical displacement

-0.006

-0.004

-0.002

400 450 500 550 600 650 700 750 800 850 900

Step 2: identification of the damaged element &

quantification of the damage

Response for train excitation

Error in function

approximation Location and

level of the

damage

400 examples are created for the training set considering

different scenarios

In order to give a global and intuitive representation of the results, two quantities have

been defined as follows:

Location: it gives a measure of the error in the positioning of the damage

ytiloc

where t is the vector of the target values and y is the vector coming from the network

model. If this quantity is equal to one the damage is well localized.

Intensity: it gives a measure of the error in quantifying the level of damage

ti )int(

If it is equal to one, the level of damage is correctly estimated.

Step 2: measures of identification of the element &

quantification of the damage

Intensity (optimized model)

0 10 20 30

Intensity (model 20 units)

0 10 20 30

Location (optimized model)

0 10 20 30

Location (model 20 units)

0 10 20 30

Test: comparison of location and intensity

70% 90%

50% 68%

LOCATION 20 UNITS

INTENSITY 11 UNITS

LOCATION 11 UNITS

INTENSITY 20 UNITS

POSTERIOR FOR ,

TRAINING: OPTIMIZATION

w = wMAP?

? DMEVDMEV ii 1

INFERENCE OF NEW DATA

CHOOSE MODEL Mi-1

POSTERIOR FOR Mi

, = MP, MP

DATA PRE- PROCESSING

OUTPUT

NETWORK MODEL Mi

N HIDDEN = i

N INPUT = k

POSTERIOR FOR w

DATA POST PROCESSING

PROBABILISTIC MODEL

CHOOSE INITIAL ,

INITIALIZE WEIGHTS w

RE-ESTIMATION OF ,

i= i+1

is 1,…,k

‘very large’?

k= k-1

0 20 40 60 80

Damage detection

portion of the deck

element

THE ORGANIZATION

OF DESIGN PROCESS

Sicurezza in caso d’incendio

Strategie per

la gestione

dell'incendio

Prevenzione

Gestione

dell'evento

Gestione

dell'incendio

4Gestione delle

persone e

dei beni

Difesa sul posto

Spostamento

Disposibilità

delle vie

di fuga

Far avvenire

il deflusso

Controllo

della quantità

combustibile

Soppressione

dell'incendio

10Controllo

dell'incendio

attraverso il

progetto

Automatica

Manuale

Controllo dei

materiali

presenti

6Controllo

del movimento

dell'incendio

7Resistenza e

stabilità

strutturale

Contenimento

Ventilazione

Strategie per

la gestione

dell'incendio

Prevenzione

Gestione

dell'evento

Gestione

dell'incendio

4Gestione delle

persone e

dei beni

Difesa sul posto

Spostamento

Disposibilità

delle vie

di fuga

Far avvenire

il deflusso

Controllo

della quantità

combustibile

Soppressione

dell'incendio

10Controllo

dell'incendio

attraverso il

progetto

Automatica

Manuale

Controllo dei

materiali

presenti

6Controllo

del movimento

dell'incendio

7Resistenza e

stabilità

strutturale

Contenimento

Ventilazione

Stile della rappresentazione

grafica dei processi

AVANTI

FIGLIO FIGLIO

ENTRANTI

USCENTI“NO”

“SI’”

Forward vs. Reverse

Engineering

Qualita’ documenti (1)

Qualita’ documenti (2)

Simboli base

Altri simboli

Decisioni e connessioni

COMPLEXITY

Geometric Complexity

COMPLEXITY DEFINITION

linear interactions nonlinear

stight

mechanical behavior

FACTORS INFLUENCING

STRUCTURAL COMPLEXITY

NON LINEAR

BEHAVIOR

LINEAR

3300183 183777 627

960 3300 m 810

+77.00 m

+383.00 +383.00

+54.00+118.00

+52.00 +63.00

3300183 183777 627

960 3300 m 810

+77.00 m

+383.00 +383.00

+54.00+118.00

+52.00 +63.00

Dispositivi di Dissipazione

Comportamento del SuoloNon Linearità di Materiale

Interfaccia Suolo-Struttura Non Linearità di Contatto

Pendini

Cavi Principali

Non Linearità Geometrica

NON LINEARITA’

FACTORS INFLUENCING

AMBIGUITY UNCERTAINTY

NON LINEAR

BEHAVIOR

LINEAR

3300183 183777 627

960 3300 m 810

+77.00 m

+383.00 +383.00

+54.00+118.00

+52.00 +63.00

3300183 183777 627

960 3300 m 810

+77.00 m

+383.00 +383.00

+54.00+118.00

+52.00 +63.00

Incertezze legate al modello strutturale

Incertezze legate alla modellazione dei carichi

Incertezze legate alla geometria ed ai materiali

INCERTEZZE

FACTORS INFLUENCING

AMBIGUITY UNCERTAINTY

COUPLING INTERACTIONS CONNECTIONS

NON LINEAR

BEHAVIOR

LINEAR

Interazione Struttura - Traffico

Interazione Struttura - Vento

Interazione Struttura - Terreno

INTERAZIONI

LIVELLO GLOBALE

3300 mlivello locale

N.B. 1 - EFFETTO SCALA

HUMANWARESOFTWARE

HARDWARE

PROGETTO

COSTRUZIONE

(MATERIALI – COMPONENTI)

ON.B. 2

Fire, Heat Transfer, Structural and

Human Behavior Models.

SYSTEM DECOMPOSITION

330FB nov 05 330

ELEMENTI E COMPONENTI

STRUTTURALI

ORGANIZZAZIONE

Le relazioni stabili di funzione, funzionalità

e topologia che danno significato agli

elementi indipendentemente dalla loro specificità.

STRUTTURA

Elementi specifici che tramite le relazioni

strutturali formano una configurazione persistente nel

SISTEMA

Struttura durevole di elementi organizzati, che

viene osservata come unità che presenta

caratteristiche emergenti.

System Definitions

• ANSI/EIA-632-1999: "An aggregation of end products and enabling products to achieve a given purpose."

• IEEE Std 1220-1998: "A set or arrangement of elements and processes that are related and whose behavior satisfies customer/operational needs and provides for life cycle sustainment of the products."

• ISO/IEC 15288:2008: "A combination of interacting elements organized to achieve one or more stated purposes."

• NASA Systems Engineering Handbook: "(1) The combination of elements that function together to produce the capability to meet a need. The elements include all hardware, software, equipment, facilities, personnel, processes, and procedures needed for this purpose. (2) The end product (which performs operational functions) and enabling products (which provide life-cycle support services to the operational end products) that make up a system."

• INCOSE Systems Engineering Handbook: “Homogeneous entity that exhibits predefined behavior in the real world and is composed of heterogeneous parts that do not individually exhibit that behavior and an integrated configuration of components and/or subsystems."

• INCOSE: "A system is a construct or collection of different elements that together produce results not obtainable by the elements alone. The elements, or parts, can include people, hardware, software, facilities, policies, and documents; that is, all things required to produce systems-level results. The results include system level qualities, properties, characteristics, functions, behavior and performance. The value added by the system as a whole, beyond that contributed independently by the parts, is primarily created by the relationship among the parts; that is, how they are interconnected."

Structure vs. Structural System

• This requires evolving from a simplistic

idealization of the structure as

“device for channeling loads”

to the idea of the structural system,

intended as

“a set of interrelated components working

together toward a common purpose”.

variabili

ELEMENTO

COMPONENTE

STRUTTURA

SISTEMA

STRUTTURALE

Il MACROLIVELLO, che comprende il Ponte nella

sua globalità e i sistemi strutturali;

Il MESOLIVELLO, che include le diverse strutture

e sottostrutture che compongono il sistema

strutturale;

Il MICROLIVELLO, nel quale vengono descritti i

componenti delle sottostrutture e i rispettivi

elementi costituenti.

Per ciascun livello devono essere poi identificate e

definite le variabili di progetto.

Il complesso sistema strutturale deve essere scomposto,

ovvero sottostrutturato, in livelli crescenti di dettaglio:

Scomposizione Strutturale

SISTEMA

STRUTTURALE

PRINCIPALE

ZONE SPECIALI DI

IMPALCATO

SISTEMA DI

RITEGNO/SOSTEGNO

SISTEMA

STRUTTURALE

SECONDARIO

SISTEMA DI

SOSPENSIONE

IMPALCATO

CORRENTE

FONDAZIONI DELLE TORRI

ANCORAGGI

CAVI PRINCIPALI

PENDINI

CASSONI STRADALI

CASSONE FERROVIARIO

TRAVERSO

INTERNE

TERMINALI

SISTEMA STRUTTURALE

AUSILIARIO

STRADALE

FERROVIARIO

FUNZIONAMENTO

MANUTENZIONE

EMERGENZA

MACROLIVELLO

MESOLIVELLO

Individuazione delle

VARIABILI di progetto

per ciascun elemento

Individuazione degli

ELEMENTI

per ciascun componente

Individuazione dei

COMPONENTI

di ciascuna sottostruttura

SOTTOSTRUTTURAZIONE

del sistema globale

per lo studio di dettaglio

delle singole prestazioni

SISTEMA DI

RITEGNO/SOSTEGNO

FONDAZIONI DELLE TORRI

ANCORAGGI

Rappresentazione ipertestuale:

modellazione ad oggetti e rappresentazione

ad albero del problema strutturale

STRUCTURAL

SYSTEM

AUXILIARY

STRUCTURAL

SYSTEM

SECONDARY

STRUCTURAL

SYSTEM

SPECIAL

DECK ZONES

BRIDGE

HIGHWAY SYSTEM

RAILWAY SYSTEM

OPERATION

MAINTENANCE

EMERGENCY

FOUNDATION OF TOWERS

TOWERS

ANCHORAGESSUPPORTING

CONDITION

HIGHWAY BOX-GIRDER

CROSS BOX-GIRDER

RAILWAY BOX-GIRDER

BRIDGE

SUPERSTRUCTURE

MACRO-LEVELS

MESO-LEVELS

MAIN CABLES

HANGERS

SUSPENSION

SYSTEM

SADDLES

FRANCO BONTEMPI 343

Criterio meccanico: B-D regions

PERFORMANCE

• STRUTTURA – situazione puntuale:

definita nello spazio e nel tempo.

• SISTEMA STRUTTURALE – estensione:

– spaziale:

ambiente / contesto

sostenibilita’ / compatibilita’

– temporale:

ciclo di vita (life-cycle) - robustezza

Struttura / Sistema Strutturale

Structural System

357October 2012 www.francobontempi.org

• SISTEMA STRUTTURALE:

1. complessita’ del quadro prestazionale

(affidabilita’, sicurezza /security,

dependability)

2. CONCEZIONE STRUTTURALE

3. ANALISI STRUTTURALE:

motore del processo decisionale

strumenti generali e efficaci

Structural Analysis and Design

Macro-level Meso-level Micro-level

RESILIENCE

DEPENDABILITY

ATTRIBUTES

THREATS

RELIABILITY

FAILURE

FAULT TOLERANT

DESIGN

FAULT DETECTION

FAULT DIAGNOSIS

FAULT MANAGING

DEPENDABILITY

STRUCTURAL

SYSTEMS

AVAILABILITY

SAFETY

MAINTAINABILITY

permanent interruption of a system ability

to perform a required function

under specified operating conditions

the system is in an incorrect state:

it may or may not cause failure

it is a defect and represents a

potential cause of error, active or dormant

INTEGRITY

ways to increase

the dependability of a system

An understanding of the things

that can affect the dependability

of a system

A way to assess

the trustworthiness

of a system which allows

reliance to be justifiably placed

on the service it delivers

SECURITY

High level / active

performance

Low level / passive

performance

ATTRIBUTES

THREATS

MEANSMEANS

RELIABILITYRELIABILITY

FAILURE

FAULT TOLERANT

DESIGN

FAULT TOLERANT

DESIGN

FAULT DETECTIONFAULT DETECTION

FAULT DIAGNOSISFAULT DIAGNOSIS

FAULT MANAGINGFAULT MANAGING

DEPENDABILITY

STRUCTURAL

SYSTEMS

AVAILABILITY

SAFETY

MAINTAINABILITY

permanent interruption of a system ability

to perform a required function

under specified operating conditions

the system is in an incorrect state:

it may or may not cause failure

it is a defect and represents a

potential cause of error, active or dormant

INTEGRITY

ways to increase

An understanding of the things

that can affect the dependability

of a system

A way to assess

the trustworthiness

of a system which allows

reliance to be justifiably placed

on the service it delivers

SECURITY

High level / active

performance

Low level / passive

performance

HAZARD

HOLES DUE TO

ACTIVE ERRORS

HOLES DUE TO

HIDDEN ERRORS

Failure Path

Structural Design

WIND & TEMPERATURE

SOIL & EARTHQUAKE

JUDGEMENT

DECISIONS

NEGOTIATION &

REFRAMINGNo

STRUCTURAL

MODELING

Traditional design procedure

• The traditional design procedure that lead

to the “as built” construction are:

1) Formulation of the problem;

2) Synthesis of the solution;

3) Analysis of the proposed solution;

4) Evaluation of the solution performances;

5) Construction.

Traditional design procedure

• Difficulties associated with this kind of approach are evident. The “As Built” structure could be different from the “As Designed” one, due to different factors like fabrication mistakes or unexpected conditions during the construction phase, or also due to non appropriate design assumptions. These last could create doubts about the accuracy of the analysis results leading to a predicted behavior which does not correspond to the real one.

• One then can add:6) Monitoring of the real construction;7) Comparison between results from monitoring

and expected behaviour results;8) Increase in the accuracy of expectation of the

future structural behaviour.

Further steps added considering

a monitoring system

Performance-Based Design

9) Reformulation: development of advanced methods for

a more accurate description of the required behavior

and of the required performance;

10) Weak Evaluation: this methodology assumes that the

analysis is exact and that all the actions are known

exactly, from the probabilistic viewpoint;

11) Model improvement: the necessity connected with

the models improvement comes from experiences

based on monitoring, where expected and measured

behaviors on “as built” structures are compared;

12) Strong Evaluation: a third kind of evaluation becomes

possible when the improvement aims at assigning

more accurate values to the used parameters and to

define more accurate modelling hypothesis.

Functional Analysis/

Resources Allocation- Decomposition to lower-level function

- Allocate performance

- Define functional interfaces

- Define functional architecture

Requirement

Design loop

PROCESS

Historic Analyses

Evolutive / Innovative

Design

Risk Management

PROCESS

OUTPUT

Synthesis- Transform architecture

- Define alternative product concepts

- Define physical interfaces

- Define alternative product

and process solutions

Requirements Analysis- Analyze missions and enviroments

- Identify functional requirements

- Define performance and design

constraint requirement

System

Modeling

Analysis

ANALYSIS MODELS vs.

DESIGN MODELS

STRATEGY #0:

DECOMPOSITION

STRUCTURAL

QUALITY

- design life

- railway

runability

- highway

runability

- free channel

- robustness

- durability

- management

GLOBAL

GEOMETRY

TOPOLOGY

- suspension system

- towers

- towers foundation

- anchor system

- main deck

- deck landing

GLOBAL GEOMETRY

- main span

- sx span

- dx span

SECTIONAL GEOMETRY

- continuous girder sections

- transverse section

- main cables

- hangers

- towers

- secondary elements

MATERIALS

CHARACTERISTICS

- girders

- cables

SYNTHESIS OF

STRUCTURAL

SOLUTION

DOCUMENTATION

BOUNDARY

CONDITIONS

CONSTRAINTS:

rigid and elastic

constraints,

imposed

displacements

NATURAL

ACTIONS

- temperature

- wind

- earthquake

ANTROPIC

ACTIONS

a) permanent

loading

system

b) variable

- railway

- highway

c) accidental

BASIC STRUCTURAL

CONFIGURATION

PARAMETERS

- individuation

- definition

- uncertainty

- description

- bounding

GLOBAL

MODELING

MODELING WITH

DYNAMIC INTERACTION

ALTERNATIVE STRUCTURAL

CONFIGURATIONS

GLOBAL

OPTIMIZATION

- topology

- morphology

- parametric

OPTIMIZATION

- girders section

- transverse

section

- restraint zone

EXPERT AND

FIXED CHOICES

MEASURES

a) qualitative

b) materials volumes

c) serviceability

- modal characteristics

- deflections

- deformations

- reversibility

d) collapse scenarios

- collapse characteristics

- robustness

e) accidental scenarios

- configurations

- risks

DETAILED

MODELING

EXTENDED

MODELING

Numerical Modeling for the

Structural Analysis and Design of

MESSINA STRAIT BRIDGE:

subdivision and development of activities.

FB - june 6, 2005 / franco.bontempi@uniroma1.it 373

STRATEGY #1: SENSITIVITY

STRATEGY #2: BOUNDING

ControlloreProblema Risultato

Solutore #1

Solutore #2

Voting System

STRATEGY #3: REDUNDANCY

STRUCTURAL

MODELING

Global Frame Models Local Models

Substruct-

ured Models

STRUCTURAL

MODELING

Global Frame Models Local Models

Substruct-

ured Models

structural configurations

specificity of the modelingcommercial

Stro N

GERwww.stronger2012.com

Appunti del corso di dottorato: Ottimizzazione Strutturale / Structural Optimization - parte II -...

Education

Transcript of Appunti del corso di dottorato: Ottimizzazione Strutturale / Structural Optimization - parte II -...

Catálogo Bontempi News 02

BOMBOLE IN VETRORESINA STRUCTURAL - … - Bombole in Vetroresina STRUCTURAL.pdf · info@hytekintl.com BOMBOLE IN VETRORESINA STRUCTURAL / STRUCTURAL PRESSURE VESSELS PRESENTAZIONE

Search Engine Optimization (SEO)

Web Performance Optimization

Appunti del corso di dottorato: Ottimizzazione Strutturale / Structural Optimization - parte I - Bontempi

Modelli compartimentali e farmacocinetica - unipi.it · e farmacocinetica carmelo.demaria@ ... A PRIORI (STRUCTURAL) A POSTERIORI (STRUCTURAL + NUMERICAL) + ... • NON’IDENTIFICABILITA

Bontempi cucine Area

a cura di STRUCTURAL

Sedie - Bontempi

STRUCTURAL OPTIMIZATION OF MONO AND MULTIVALENT … · mannose receptor (CD206), DEC205 (CD205), DC-SIGN (CD209), blood DC antigen 2 (BDCA2), dectin-1, DC immunoreceptor (DCIR), DC-associated

MADE EXPO 2012 crosti bontempi

PSA - Caratteristiche del Fenomeno Incendio - Bontempi

Showtime Stage Microphone - Bontempi - Home

LINDO: Optimization Software

Particle Swarm Optimization - cuni.cz

2015 corso dottorato OTTIMIZZAZIONE STRUTTURALE franco bontempi

Dml web-content-optimization

Bontempi cucine Opera

Bontempi Cucine

Lowendalmasaï social charges optimization