Computer-Aided Design: Classification of Design problems in Architecturejoics.org/gallery/ics-1526.pdf · 2019-10-21 · 1 Computer-Aided Design: Classification of Design problems

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Computer-Aided Design: Classification of Design problems in Architecture

M. Vatandoost1, S. A. Yazdanfar2, A. Ekhlassi*3

Abstract The primary object of this review is to explore the application of Computational methods

in Architectural Design and Optimization. In any optimization model user (Architect) must have an excellent comprehension of the design problem and properties of the model, which affect the solution process. The limitation and domain of each method must be considered. Therefore a problem statement has to be "translated" into a "mathematical model" and requires a deep comprehension and accurate perception of how the problem could be defined. For this purpose we have studied several peer-reviewed papers in most reputed academic journals in the field, from 1960' S to 2018 by focusing more on the recent researches and classify the Architectural design problems which were used by previous researchers in Computer-Aided Architectural Design, in nine significant groups: Energy, Form, Space, Plan, Facade, Structure, adjacency, Orientation, mechanical and acoustic. Each problem and method is described, and illustrated examples are provided. Those design problems are: Design Exploration, Conceptual Exploration, Generation of the form, Shape Optimization problem, Area Assignment problem, Least Solar energy absorption, and the most shadow area problem, Optimize Building-integrated Photovoltaic shape, Space Allocation Problem (SAP), Area Partitioning, site layout planning, design floor plans, Energy Gain/lose optimization, Windows to wall ratio, maximizing daylight, Building Facade optimization, Minimizing Travel Time, structural design, acoustic optimization, and et al

Keywords: Computer-Aided Design (CAD); Automation in Architectural Design; Architectural

Design Algorithms; Architectural Design optimization; evolutionary algorithms; Genetic Algorithm

1 MA Alumni, IUST, School of Architecture & Environmental Design, [email protected] 2Associate Professor, Iran University of Science and Technology (IUST), School of Architecture & Environmental Design, [email protected]* 3 Associate Professor, IUST, School of Architecture & Environmental Design, [email protected]

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Volume 9 Issue 10 - 2019

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1. Introduction 1.1 Background

Today Architectural Design problems seem more complicated than an Architect lonely could tackle all those problems properly. CAD has the potential to apply for computer assistance in Architectural design.

Since the early 1960' S architecture were eager to apply automation in design and layout planning and many research has been done in order to apply Automation in Architectural design, but poor results and difficulty of translate "architectural language" to "computer language" has been a major hindering issue.

Figure 1 -Computer-Aided Design (CAD) workflow,

source: Authors

It was the parametric modeling and recent advances in using intelligent optimization algorithms without knowing computer coding that generated a new wave of research, and rigorous researchers continued contribution to this field in recent years. 1.2 The objective of this review

In order to build a framework and structure of a systematical literature review for CAAD, we have classified "Design Optimization Problems" in this paper. 1.3 Review method and Scope

In this reviewing paper, by searching the databases of scientific papers first by the keyword "Computer-Aided Architectural Design (CAAD) Optimization" 26,243 paper were

identified (October 2018), then by refining results by "Automation in Architectural Design" and "Computer-Aided Design" 1,409 paper was chosen. By considering available time-scale, the number of potential papers was reduced to a manageable number by limiting results in Research article and focusing more on Peer-reviewed journals; Results domain was circumscribed within (limited to) 253 papers. Finally, all the abstracts and conclusions of this paper were skimmed, and papers were classified and grouped. , then all those 253 paper were intensely studied and according to methodology, and their impacts in the field grouped and classified. 1.4 Previous reviews

To mention previous reviewing work in CAD; in a study by Liggett [1] in the year 2000, introduces some "technics" in which optimize a "single objective function." Also, Sönmez [2] published a study which reviews the use of examples for "automating architectural design task." Shi, Tian, Chen[3] compares design optimization based on "energy efficiency." In work by Zitzler[4] Comparison of "Multi-objective" "Evolutionary" Algorithms is depicted.

However, the introduction and classification of Architectural design problems and also optimization Algorithms as we have provided in this paper has not been done previously. 2. "Optimization Problems" in

Architectural Design The "optimization problems" is to find

the "set of parameters" which minimizes or maximize an "objective function" with the assistance of an evolutionary or metaheuristic algorithms. In this section, first, we have briefly introduced most of the design problems which have been defined and optimized previously by researchers in papers we have reviewed. Then we have classified those optimization



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problems in Table-1 at the end of this section. 2.1 Design Exploration

One of the significant applications of "Computer-Aided Design (CAD)" is to widening the design solution scope and generating more diverse solutions, which is tedious or even impossible for architect without the aid of the computer. In figure 4, the generated "diverse solution" for a sample plan is depicted.

Figure 2- widening Design Scope, generated the diverse solution, source:[5]

2.2 Conceptual exploration (CACD) "freehand sketch," in the early stages of

the design process Is contemplated as the "primary conceptual tool" [6]; also Jonson states some other technics such as "verbalization" and "Computer modeling" as the concept finding.

Recently researchers have intentioned in using CAD (Computer-Aided Design) in Conceptual phase, so the CACD (Computer-Aided Conceptual Design) method has emerged, but the result has been inappropriate and not satisfactory and still are under development.

CACD aims to help architect "explore" all possible concept variants in order to diversify the scope of the solution and make sure all the possible solutions are generated; therefore, no possible solution is avoided unintentionally. This method may help architects find more diverse and also better solutions.

For example, in the research published by Turrin [7] with particular software which is called "ParaGen"; dome shape

and long-span roof were optimized. "ParaGen" is enable based on structural performance "explore" the "morphology" of a dome.

Figure 3 -Example of shape (structure) optimization by ParaGen of Michigan, source: [7]

Also, researchers from Nanyang Technological University in Singapore [8] proposed a "heuristic algorithm, CDH (conceptual design heuristic)" algorithm which is enabled to perform on the model of conceptual design.

According to Matthews, [9] CACD systems should "guide" designers in directions of better and fittest solutions by widening the scope of solutions. 2.3 Generation of the form (Form Design)/ Shape Optimization

Optimizing a free form based on defined criteria's.

In research by Zhang[10], the objective was Shape optimization of "free-form" on "Solar radiation gain" and space efficiency. They contemplated their site in China which is an intense cold zone, so it was required that the building form is optimized. By employing the "multi-objective" genetic algorithm, they optimized it for gaining "maximize solar radiation." Their model was parametric in Grasshopper of Rhino, also to gain space efficiency they used "shape coefficient."

Figure 4- Example of the solution generated by CAD for Shape optimization in the severe cold zone of China,

source:[10]

2.4 Area assignment



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In this Optimization problem, some department unit areas are assigned to an equal or larger area of the building. In other words, Area assignment could be defined as to assign unit areas of each department (a set of spaces) to a building floor area.

In the works of Verma [5] and also Thakur [11], they apply "Dijkstra's Algorithm" to determine the best position of the plan layout and room doors according to the shortest "walking distance."

Figure 5- Area Assignment problem, the black holes are indicating the doors position, source:[5]

2.5 Unequal-area facility layout problem

If departments have unequal-area, it is not possible to assign departments to specific "centroid" locations, and the selected "exact configuration" determine the location of centroids. The optimized generated solution is determined by the maximum allowable aspect ratio for each department shape.

Scholz[12] employed the "Tabu search algorithm" for the unequal facility layout problem.

Besides, in the work of Tate [13], the unequal-area facility layout problem was addressed, and by "adaptive penalty function" was optimized.

Figure 6 -unequal-area assignment problem, source:[13]

Paes [14] Place rectangular facilities on a floor which is unrestricted and the goal was while avoiding areas to be overlapped the sum of distances between facilities be minimized for this purpose the “material-handling” flows were applied. The employed optimization algorithm was a Hybrid Genetic Algorithm (GA); it is alleged that the introduced method reduces computing time in comparison to GA. 2.6 Area Partitioning

Area Partitioning problem is dividing a Building floor area to multiple smaller areas and assigning design program to those areas.

"Voronoi Diagram" was used in the work of De La Barrera[15] [as cited by Rodrigues[16]] with a GA for dividing the building floor area. 2.7 Space allocation Problem (SAP)

In the Space Allocation Problem (SAP), the size and position of several rooms (also open spaces) are assigned in a 2-dimensional (single level) or 3-dimensional space (multi-level) space according to constraints and design program.

In "Spatial architectural layout" problem internal building spaces which consist of position, shapes, and dimension of them is optimized based on defined criteria's. In other words, "space layout problem" could be defined as a finding of the best



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"adjacencies" between "functional spaces." Here space is optimized. 2.7.1 Single-level space allocation

"Determining" the fittest position on a single level (two-dimensional) for a few rooms can be defined as the Space Allocation Problem (SAP). This allocation is related to constraints, objectives, and preferences. According to Rodrigues [17] in SAP, Geometric, and topological requirements must be considered. The primary purpose could be "minimizing distances" or could be defined as "minimizing costs or time" in the "flow of material." 2.7.2 Multi-level space allocation

In this optimization problem, the way that vertical connections are considered would determine the solutions generated. In some research, the place of Vertical connection is considered as fix position, but if the position of those connections could be defined as variable, the number of generated solutions could be multiplied.

Figure 7 -Example of Space allocation problem; CAD

generates plans, Source: [17]

According to Rodrigues[18] in the multi-level allocation problem vertical connections (stairs and elevators) are considered as "fixed objects" more often, so in the search process are not involved.

Even in many types of research, these vertical connections are even ignored.

However, In research by Rodrigues [18], a "hybrid evolutionary" technique is presented which consider the vertical connections as "parametric objects" that "interact" with other spaces while searching for optimization.

In another research which was published by Ramtin[19] for multi-level optimization, the "number and position" of elevators is being determined by consideration of "conflicting objectives" which include the minimizing "material handling cost" and maximizing "closeness rating."

Also In the work of Lee [20], an "improved genetic algorithm" for generating solutions in multi-level facility layouts that consist of "inner structure walls" and "passages" was employed. By applying "Dijkstra's algorithm" of the "graph theory," they used the "adjacency graph" to "minimize the distance" between facilities.

Figure 8 -Example of the Space Allocation Problem. (having the "inner structure and passage"), source:[20]



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Figure 9 -Example of multi-level Space allocation, source: [20]

2.8 Site layout planning (building adjacency)

In The Site layout design problem, designer intends to place a building by considering site constraints and limitations such as access and adjacency. Also In any Construction site, it is essential to plan the efficient and operative layout.

Osman[21] Optimize the location of temporary facilities on a 24.000 square meter site of a sports club in Cairo of Egypt by applying Genetic Algorithm which optimizes the solution by Minimizing "transportation costs" on site.

Figure 10 -Example of site Layout Problem, source:[21]

Figure 11 -Example of site layout design, Existing layout

(above) solution generated by CAD (below), source (Osman et al., 2003)

Another Example of the "site layout Design optimization" is when we want to add a new building in a site which some other buildings exist and finding the best position of the new building according to those that stand, Could be a very complicated task. The objective function for this purpose could vary from maximizing solar gain or minimizing transformation and access between the new building and existing ones. For example, in a hospital site when we want to plan for extension and renovation, this could be a very complicated task. Moreover, CAD can assist the Architect in order to seek more option on the solution and also find the optimum solution.

Orientation and radiation gain results are as a way to site layout planning; figure 14 Indicates the orientation study based on energy analysis in ladybug plug-in for Grasshopper of Rhinoceros in the research by Roudsari [22].



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Figure 12 -Orientation study and the radiation results for each orientation, source: [22]

Another approach for site optimization is based on the "overshadowing" of neighboring. Which is finding the suitable position of building in the site according to the shadow of existing buildings in the site and also considering the shadow of the new building on those existing ones. This problem based on existing buildings and constraints is enabled to determine the position and orientation or even form of the new building in the site.

Figure 13 -Example of site optimization based on overshadowing of neighboring, source: [23]

Figure 14- Placement of buildings in the site, agent-

based system, source:[24]

2.9 Design floor plans In this problem, the "physical

arrangement" of space on a 2-D plane is automated.

In Research work of Ouarghi [25] optimization of an office building was studied, Area was optimized based on Energy and construction cost with Genetic Algorithm (GA) along with the "Bayesian Neural Network." They have considered parameters such as "the windows to wall ration, type of glazing, Climate, wall and roof insulation" and et al.

In a case study, Rodrigues[26] generated floor plans automatically based on "thermal performance." They modeled single and two-story houses. For each case, they made 12 alternative plans (which were automatically generated), so the results were assessed, then ordered due to their "thermal performance." The result revealed that the difference between the good and poor thermal performance is significant, it was 17% and 35% in single and double floor respectively.



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Figure 15 -Example of Building Thermal performance

analysis, source: [26]

Figure 16- Example of a house floor plan design based on "penalties information" and "topological space graph,"

source:

2.10 Hierarchical layout plan (Graph-based plan layout):

In Hierarchical layout (HL) plan design which is applied to plan layer, constraints should be meet (can be expressed as "graph requirements") in order the design solution be provided [27].

Figure 17 -Example of Graph-based Plan layout optimization, a)Hl-graph, b)the floor layout

corresponding to HL-graph, source: [27]

2.11 The grid system for layout representation (2D)

Galle[28] Applied a "grid system" for layout "representation." In his work a "Grid System" is "adopted" in order to represent layout, then he proposed a method to count all "arrangements" of rooms within the grid.

Figure 18 –Grid System and connections, source: [28]

Figure 19 -Example of a variety of Solutions Generated by Computer, source: [28]

In the work of Michalek [29] The "position" and "aspect ratio" of "2-D rectangular rooms" is optimized by considering geometry and topology.



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Figure 20 -Example of Plan floor optimization, Sorce: [29]

Figure 21 -Example of the Hybrid SA/SQP algorithm, source: [29]

In published research from the Hong Kong Polytechnic University by Wong[30] an optimization algorithm- "EvoArch"- is introduced based on "graph algorithm" and Genetic algorithm. Space layout problem can be optimized by "EvoArch." For this purpose first "adjacency matrices" is shaped based on "relation" and connection between spaces, then by constraint and criteria, those space are positioned.

Figure 22- Graph theory and Adjacency matrix,

Source:[30]

Figure 23- EVoArch- optimized topology generated- source:[30]

2.12 Energy Gain/loose optimization Optimization of building energy

consumption or energy gain/loose of building in the early design phase is important since the form of the building and also its orientation is extremely related to the "energy performance" of the building. Energy optimization may lead to energy conservation and greenhouse gas reduction.

To name some research in this field we should mention Jin & Jeong[31] work which optimize a "Free-form" building based on "external thermal load", their model was a parametric model in the Grasshopper of Rhinoceros, on that model the surface of form meshed so the heat gain and loss of whole building was analyzed. Finally, with GA, they could optimize free-form in "various climates."



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Figure 24 -Example of Free-Form optimization, a) initial

free form in Jin [31] work

The final generated form in Jin[31] work based on Energy gain/loose optimization and in climate zone B (as an example) is depicted in figure 27.

Figure 25 -Example of free-form optimization, b) final solution in climate zone B, source:[31]

Figure 26 - Example of roof shape optimization based on

solar optimization,(Ecotect plug-in Grasshopper), source:[32]

As the same way, Shi [32] optimized a building based on energy performance. First a parametric model obtained by grasshopper of Rhino, then Weather data were simulated using Ecotect, and building performance were evaluated with Radiance, so The fittest solution as depicted in figure 30, was generated in step 750.

Figure 27 -the parametric model of the rectangular building and its windows, source:[32]

Figure 28 -the solu on at step 750 has the minimum energy consumption, source:[32]

2.13 Minimize Energy consumption To study and evaluate energy gain/lose

in the early stages in design, Researcher at ETH Zürich university Schlueter[33] introduced "DPV" plug-in for Revit which enable to provide "Energy/exergy4" balance in the building model. That model can be used for optimization of the building based on Energy gain/loose.

4 "exergy" concept is employed for evaluation of the quality of energy sources.



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Figure 29- Energy gain/loose- DPV plug-in for Revit,

Energy/exergy Balance tab, source:[33]

2.14 Windows to wall Ratio In consideration of the optimizing the

"Ratio of the window to the wall" lead to the low-energy building, it is essential that this ratio is optimized, so In Many research, this ratio was studied.

In a study by Researchers of MIT[34] they have applied GA in an office building in order to optimize location and dimension of windows, so they measured the energy performance of the building by studying "lighting, heating and cooling" data based on size and position of windows. First, they assessed the thermal performances of the program by the results that were generated by hand, which were compliant.

By different runs of program variant solutions were generated, which indicated that "different configurations may correspond to similar standards of environmental performance."

Figure 30 -Example of the window to the wall ratio op miza on by GA, source (Caldas & Norford, 2002)

2.15 Maximize daylight One of the most considerable design

problems in the ground of Environmental friendly Architectural, "Energy efficiency" and green building is the optimization of Energy and maximizing the use of daylight (solar energy). Lots of effort has been made in this filed to reduce energy consumption in the building. [35], [36], [37],[38].

Currently, the researcher's ambition is to find fast and straightforward optimization methods not only in the evaluation or in the final phases, but also at the first (early) stages of design. Researchers try to apply intelligent algorithms to optimize building in the concept phase. So form, shape, orientation, the windows to wall ratio and also type of windows and structures, building cost et al. is considered to be optimized.

One of the today trends in the field of an energy assessment is parametric modeling which is provided mostly with Grasshopper of Rhinoceros[35]; [23]; along with using some Plug-in such as Ladybug[22], Honeybee, Diva, and et al. in order to study and optimize the building based on Energy analysis. 2.16 "Least Solar energy absorption and the most shadow area" problem

In research by Anton [39], they used parametric modeling and GA in order to optimize architectural form based on "Least Solar energy absorption" and simultaneously the "most shadow area."



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Figure 31 -Example of CAD in the "least Solar energy absorption and most shadow area" problem, source:

[39]

2.17 Daylighting optimization in smart Façade systems

Park [38] Employed an optimization method in order to optimize daylight in Smart façade systems (SFS) in which the daylight is controlled by "motorized Venetian blinds," these blinds are placed inside the glass and form smart panels that control shading. The point in the floor which daylight was measured is illustrated in figure 42.

Figure 32- Daylighting optimization in smart Façade systems, source:[38]

2.18 Optimize Building-integrated Photovoltaic shape

Toward achieving "net-Zero energy" building, applying "Building-integrated Photovoltaics" (BIPV) is an excellent tool in the climates where it is possible.

According to Youssef et al.[40] "External façades" of high-rise commercial buildings are needed for PV "integration" in addition to the roof, because the roof-mounted PV "cannot generate 40.4 kWh/(ft2.yr)" In hot climates, while according to the ASHRAE standard (ASHRAE 90.1-2007) high-rise commercial buildings require 13.4 kWh/(ft2.yr)– So the generated electricity from the roof can provide only three floors. Therefore optimization of the BIPV in high-rise commercial buildings is essential.

Figure 33 -Example of Building Integration Photovoltaic

optimization problem, source:[40]

Researchers from Colorado University [40] proposed a method for optimizing "building integrated photovoltaic" (BIPV) based on a genetic algorithm, they employed a "graphic user interface" (GUI) based on MATLAB, and also they applied "shape grammar" theory. The objective of their study was optimizing BIPV (Building Integrated Photovoltaic) shape with GA. For energy consumption analysis, they used "DOE2" and Autodesk "ECOTECT" for modeling and considering shadowing of neighborhoods.



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Figure 34 -Layout derivation of the size Grammar,

source: [2]

Figure 35 Application of Shape Graphic theory in optimization, source:[40]

2.19 Building Shape and envelope features optimization

Evaluation and assessment of Building Shape and Envelope features and study the effects of building an envelope on energy consumption.

Tuhus-Dubrow [41] In a residential building, Employed a GA based approach to optimize the "building envelope." So that In order to study the building envelope effect on energy performance (consumption) of the building, models

with different shapes L, T, Cross, U, H, Rectangular, trapezoid were generated to be compared, in those models dimensions of shapes defined as a variable so that the program could modify it. Also, in those models building envelope features such as the type of glazing, insulation and et al. were considered. The final results approve that in five distinct studied climates, the rectangular and trapezoidal building shapes had the best result in comparison to other shapes.

Figure 36 -Example of Building Shape and "Envelope

features" Optimization, a) alternatives considered for shape optimization source:[41]

Figure 37 -Example of Building Shape and "Envelope

features" Optimization, b)summary of shape optimization, source: (Tuhus-Dubrow & Krar , 2010)

2.20 Building façade optimization In a study by Weng[42] which optimize the building form (building facades and internal layouts) based on the energy efficiency; A case study of a complex multi-façade (8 façade) is tested in a chosen climate of China. Results are depicted in figure 9.



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Figure 38 -Building Facade optimization problem, the final results for the different climate is depicted, source:

[42]

Despite the regular optimization of Rectangular windows, Wright [43] introduced "fenestration optimization" in order to optimize the shape and size of the non-rectangular window for "façade optimization" based on Energy performance. The proposed method is balancing minimum "building energy use" and minimum "capital cost" by optimizing the "shape, number, and position" of windows by employing an "evolutionary algorithm" (EA).

Figure 39 -Facade Optimization, "Cellular Fenestration," source: [43]

2.21 Design adaptation "Design adaptation" is to adapting the

building to the outside environment, or even defining the building self "microenvironment."

In research from MIT University[44] in order to build architectural form, he compared various algorithms with the "adaptation" approach. In his work, the "adaptation" was considered as "environmental behavior" which search for shapes that maximizing daylighting and

also lessen thermal exchanges with the external environment.

Figure 40- design adaptation, comparison of solutions, source:[44]

Figure 41- ?, source:[44]

2.22 Minimize Travel Time (nursing unite design)



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According to Su [45] "Walking" for nurses is one of the most "time-consuming" activity, and "Unnecessary walking" may lead to "wasting of time" and intensify "fatigue and stress." Nurses can devote more time to caregiving If time saved by walking.

If travel time minimized, so waste could be minimized, and efficiency would increase, a good architectural layout plan could achieve this.

Su [45] In the hospital layout plan, Applied a "fast Genetic Algorithm." They convert a "complex problem" into simplified versions of the original problem by employing a "fast GA" and with the "simplified" problems, experiment on the methods and workflow.

Figure 42 -Example of "Nursing unite Design" problem

("minimizing walking distance"), the Constant Parameters in potential layout solution, Source: [45]

Figure 43 -Example of "Nursing unite Design" problem ("minimizing walking distance"), Unit Layout with the "shortest" travel(left) and "Longest" travel distance

(right), Source:[45]

2.23 Structural Design Structural design optimization,

according to Kociecki[46], could state as "improvement" of a projected design that leads to the minimum cost along with best properties.

In a case study, the structural design of steel space-frame roof structure of a train station in the Ottawa Light Rail Transit (OLRT) system was considered; the "minimum weight" was the primary goal in the design of "free-form." The model comprised of discrete "rectangular hollow structural sections" (HSS) that were prefabricated and available in the market. Kociecki [46] Genetic algorithms were applied as an optimization tool. In this example structure had a "diamond grid pattern," and the primary forces in the members was torsion, bending and axial forces. Figure 52 indicates the final design results.

Figure 44 -Example of CAD in structural Design by GA, source: [46]

Kaveh[47] employed "ant colony" optimization for the design of "skeletal structures."

Figure 45 -Truss structure optimized in the work of [48]



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Figure 46 -Example of Structural Design Optimization, A

26-Story building with bracing, source: [47]

Figure 47 -an example of structural design optimization, A 26-story building with bracing, the history of

optimization, source: [47]

Another example in this category is the work of Turrin[7] which employs the genetic algorithm in a parametric model in order to optimize the "long-span roof." 2.24 Pipeline distribution and structure stability

In this objective function, the position of rooms is determined based on pipeline optimization, so all bathrooms and halls on different floor required to become aligned.

In a case study by Guo [49], a three-level house with 17 room and a staircase were optimized based on "pipeline

distribution and structural stability." In their method "agent-based topology system" was introduced and applied.

Figure 48 –pipeline distribution and structural stability optimization problem, source:[49]

2.25 Acoustic optimization The best results of "Acoustic Design

problem" is provided when it is conducted in the early phase of architectural design since the shape and configuration of space affect the acoustic results.

Researchers at MIT University[50] Introduced a novelty optimization algorithm based on "Simulation Annealing algorithm" in order to optimize Acoustic in an "interactive acoustic design approach" that enables the architect to provide an "architectural configuration" with a high acoustic performance.

Figure 49- Acoustic optimization, source:[50]



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Classification of Objective Goals (Design Problems) in CAAD: In the Table-1 we have classified the Objective Goals (Design Problems) in CAD in the

Papers we have reviewed, Table 1. Classification of design problems in architectural design (source: Authors)

Architectural Design Problem (Objective Goal)

Group classification

Applied by researcher (Cited As an Example)

Design Exploration Form/plan/Space/Orientation Verma & Thakur, 2010 [5]

Conceptual Exploration (CACD) Form/plan/Space/Orientation Chong et al., 2009[8] Jonson, 2005[6]

Turrin et al., 2011[7] Generation of Form (Form Design)/ Shape Optimization

form Zhang et al., 2016[10]

Area Assignment Plan Thakur et al., 2010[11] Verma & Thakur, 2010[5]

Unequal-area facility layout problem Plan Scholz et al., 2009[12] Tate∗ & Smith, 1995[13]

Area Partitioning Plan De La Barrera & Ignacio, 2010[15]

Rodrigues et al., 2013b[16] Space allocation Problem (SAP)

Space/Form/Plan Rodrigues et al., 2013[18] Guo & Li, 2017[49]

Rodrigues et al., 2013c[17] Ram n et al., 2010[19]

Lee et al., 2005[20] Site layout planning Adjacency/Orientation Osman et al., 2003[21]

Roudsari et al., 2013[22] Design Floor Plans Plan Ouarghi & Krar , 2006[25]

Rodrigues et al., 2014[26] Hierarchical layout plan (Graph-based plan layout)

Plan Ślusarczyk, 2018[27]

The grid system for layout representation plan Galle, 1981[28] Michalek et al., 2002[29] Wong & Chan, 2009[30]

Energy Gain/loose optimization Form/orientation Jin & Jeong, 2014[31] Shi & Yang, 2013[32]

Minimizing Energy Consumption Form/Energy Schlueter & Thesseling, 2009[33]

Windows to wall ratio Façade/Form L. G. Caldas & Norford, 2002[34]

Maximize Daylight Orientation/Form Qingsong & Fukuda, 2016[35]

Rakha & Nassar, 2011[36] Park et al., 2003[38]

Least Solar energy absorption + the most shadow area

Adjacency/ Form/orientation Anton & Tănase, 2016[39]

Daylight optimization in smart façade systems

Façade Park et al., 2003[38]

Building Integrated Photovoltaic shape Form/Façade Youssef et al., 2018[40] Building Shape and envelope features Form Tuhus-Dubrow & Krarti,

2010[41] Building façade optimization Façade Weng et al., 2015[42]

Wright et al., 2014[43] Design Adaptation Form L. G. M. L. d. O. G. C.

Caldas, 2001[44] Minimize Travel Time Adjacency/Plan Su & Yan, 2015[45]

Structural Design optimization Structure Kociecki & Adeli, 2013[46]; Kaveh & Shojaee, 2007[47]

Turrin et al., 2011[7] Pipeline distribution and structural stability

mechanical Guo & Li, 2017[49]

Acoustic optimization Acoustic Monks et al., 2000[50]



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Conclusion In order to build a framework for

CAAD, we have classified "Design Optimization Problems" in this paper. According to Table 1, the Architectural design problems that heretofore have been considered in CAAD could be classified into nine major groups; those are Form, Space, Plan, Facade, Structure, adjacency, Orientation, mechanical, acoustic.

Creating an elaborate yet simple model is prerequisite in optimization. The model should be the correct and proper portion, and modeler (designer) must have an excellent comprehension of the problem and properties of the model, which affect the solution process. The limitation and domain of each method must be considered. In any optimization model, a problem statement has to be "translated" into a "mathematical model" and requires a deep comprehension and accurate perception of how the problem could be defined. Properties of variables should be

considered significant in order to recognize and model the behavior of variable based on mathematical properties such as continuous, discrete, or mixed-discrete. Based on objective function and

design optimization problem and model properties, a suitable method should be considered as an optimizer.

In applying design optimization algorithms, limitation and capabilities of algorithms must be considered, For example, According to choudhary[51] "gradient-based" methods not only are "fast" but also are meticulous and accurate for handling "continuous problems," even though a "stochastic methods" is suitable for "large combinatorial" problems. One of the main impediments in

applying intelligent Algorithms and CAD in design in real-world Projects is that

these methods currently are so "time-consuming" and requires a considerable amount of computational power, furthermore this methods need specialized knowledge and most of the time the design problem is so complicated. It seems that if the design methods and CAD became more user-friendly rather than computer coding, more architects would instead use CAD as an Aid in design. We have to declare that the

problems listed in Table 1 are not "only problems" in the Architectural Design, but these are those which researchers were interested so far and have tried to propose methods to optimize those. It seems that in the coming years, more optimization problems would be considered and introduce to the CAAD. In order to build a framework for

CAAD, we have classified "Design Optimization Problems" in this (the first part of) paper and in part II of paper (*) we have provided the classification of intelligent algorithms in CAAD.

Acknowledgments

The Authors appreciate the aid provided by the school of Architecture and Builds Environments of Iran University of Science and Technology (IUST).



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