formal models of design 1/28 radford, a d and gero j s (1988). design by optimization in...
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formal modelsof design
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Radford, A D and Gero J S (1988). Design by Optimization in Architecture, Building, and Construction,
Van Nostrand Reinhold, New York
computers usesymbolic models
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must use a formal model
design asdecision-making
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● goals exist – purposeful
● decisions on how to achieve
● outcome is design solution
● design solution has performance
design asoptimization / satisficing
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● optimization● ‘best’ solution possible
● satisficing● satisfies constraints
● ‘needle in haystack’
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problem formulation
synthesis
analysis
evaluation
the design process
behaviour andstructure
● behaviour● performance criteria● optimise or satisfy or both
● structure● decisions on structure - states
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state space – performance space
state space performance space
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state space – performance space
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performance spacecost
state spaceshapes
symbolic modelling● symbolic models use symbols
● mathematical models common
● symbols ● variables &/or constants● equations – y = mx + c
● computers – symbolic models● models based on algorithms● step by step procedure / rules
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purpose● to predict behaviour
● input values for input variables● outcome – values for output variables● describe relationships between variables
● to design ● arrive at values for design variables● endogenous variables● exogenous variables● dependent variables
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design process models ● simulation
● for predicting● outcome – values for output variables● describe relationships between variables
● generation ● arrive at values for design variables● endogenous/ exogenous/ dependent variables
● optimization● the ‘best’ – optimal solution● subsumes simulation and generation
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simulation - analysis
what does it mean?OED definition
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“technique of imitating the behaviourof some situation or process by means ofsuitably analogous situation or apparatus
for the purpose of study or personnel training.”
simulation - analysis
● physical models● building models● flight simulator
● computer models● building models● other models
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simulation - analysis
● what do we do?● fix all the variables interested● set values● run model● examine results● change values – new results
● trial-and-error● no indication of how good or bad
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simulation - analysis
● well established process● first must have a solution● relationships must be correct● iterative process● postulate-evaluate-modify● generate-and-test● no clues as to worth of solution● may indicate trend● change 1 variable or 2 or ….
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simulation – analysisuse
● for checking performance
● to improve solution● must understand relationships between performance & design variables● will need to hypothesize about how to improve● what values to change● may need to add new variables – e.g. brakes
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simulation analysis
structure behaviour
generation - synthesis
● what designers do● come up with solutions● select design (decision) variables● select values for variables
● generative models● model generates design solutions according to prescribed rules
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generation - synthesis ● morphological method
● Zwicky, Luckman● put down all states (values) of variables● generates all possibilities● need rules or constraints to eliminate infeasible solutions
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colour
redorangepurple
shape
circlesquare
size
505560
position
centreupdown
generation - synthesis
● shape grammars● Stiny, Knight● lego blocks
● generate feasible solutions within grammar● start – apply rules – result● large space
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generation - synthesis
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R1
R2
R4
R3
R5
R2 R4 R1 R5
R1 R1 R1
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generation synthesis
structure structure
● no ranking● no evaluation
optimization ● getting the ‘best’
● best according to criteria● min or max● quantitive & qualitative criteria
● generation & simulation● plus evaluation
● rank results
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optimization
● search mechanism● search whole field of feasible solutions● identify best according to criteria
● exhaustive enumeration (brute force)● partial enumeration (directed search)● can identify near optimal solutions
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optimization
● single criterion● classical optimization
● multiple criteria● Pareto optimization● not best but best compromise ● tradeoffs between criteria
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optimization
● techniques● calculus● linear programming● nonlinear programming● dynamic programming● evolutionary computation
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optimization
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difficult part is to formulatemeaningful objectives
in a discipline characterized bymultiple and ill-defined objectives
comparison of approaches ●simulation
● great deal of information – about one solution● no comparison to other solutions
● generation● produces number of feasible solutions● nothing about merit of solutions● solutions ‘grammatically’ correct
● optimization● produces ordered set of solutions according to specified criteria● subsumes generation and simulation
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