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formal models of design 1/28 Radford, A D and Gero J S (1988). Design by Optimization in Architecture, Building, and Construction, Van Nostrand Reinhold, New York

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computers use symbolic models 2/28 must use a formal model

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design as decision-making 3/28 ● goals exist – purposeful ● decisions on how to achieve ● outcome is design solution ● design solution has performance

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design as optimization / satisficing 4/28 ● optimization ● ‘best’ solution possible ● satisficing ● satisfies constraints ● ‘needle in haystack’

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5/28 problem formulation synthesis analysis evaluation the design process

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behaviour and structure ● behaviour ● performance criteria ● optimise or satisfy or both ● structure ● decisions on structure - states 6/28

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state space – performance space state spaceperformance space 7/28

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state space – performance space 8/28 performance space cost state space shapes

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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 9/28

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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 10/28

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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 11/28

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simulation - analysis what does it mean? OED definition 12/28 “technique of imitating the behaviour of some situation or process by means of suitably analogous situation or apparatus for the purpose of study or personnel training.”

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simulation - analysis ● physical models ● building models ● flight simulator ● computer models ● building models ● other models 13/28

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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 14/28

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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 …. 15/28

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simulation – analysis use ● 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 16/28

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17/28 simulation analysis structure behaviour

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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 18/28

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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 19/28 colour red orange purple shape circle square size 50 55 60 position centre up down

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generation - synthesis ● shape grammars ● Stiny, Knight ● lego blocks ● generate feasible solutions within grammar ● start – apply rules – result ● large space 20/28

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generation - synthesis 21/28 R1 R2 R4 R3 R5 R2R4R1R5 R1

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22/28 generation synthesis structure ● no ranking ● no evaluation

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optimization ● getting the ‘best’ ● best according to criteria ● min or max ● quantitive & qualitative criteria ● generation & simulation ● plus evaluation ● rank results 23/28

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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 24/28

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optimization ● single criterion ● classical optimization ● multiple criteria ● Pareto optimization ● not best but best compromise ● tradeoffs between criteria 25/28

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optimization ● techniques ● calculus ● linear programming ● nonlinear programming ● dynamic programming ● evolutionary computation 26/28

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optimization 27/28 difficult part is to formulate meaningful objectives in a discipline characterized by multiple and ill-defined objectives

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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 28/28

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