Design Through Computation: Explorations in Space Layout Planning Towards Mass Customization of...

The International Journal of the

Constructed Environment

ConsTruCTEdEnvIronmEnT.Com

VOLUME 3 ISSUE 4

__________________________________________________________________________

AUTHOR(S)

Article Title

Article Subtitle

THE INTERNATIONAL JOURNAL OF THE CONSTRUCTED ENVIRONMENT www.constructedenvironment.com

First published in 2013 in Champaign, Illinois, USA by Common Ground Publishing LLC www.commongroundpublishing.com

ISSN: 2154-8587

© 2013 (individual papers), the author(s) © 2013 (selection and editorial matter) Common Ground

All rights reserved. Apart from fair dealing for the purposes of study, research, criticism or review as permitted under the applicable copyright legislation, no part of this work may be reproduced by any process without written permission from the publisher. For permissions and other inquiries, please contact [email protected].

The International Journal of the Constructed Environment is peer-reviewed, supported by rigorous processes of criterion- referenced article ranking and qualitative commentary, ensuring that only intellectual work of the greatest substance and highest significance is published.

Design Through Computation: Explorations in Space Layout Planning Towards Mass Customization of Housing

Basem Eid Mohamed, McGill University, Canada Aaron Sprecher, McGill University, Canada

Abstract: This paper explores the application of computational techniques in problem solving. Specifically, it examines the use of a software add-on in the realm of space layout planning, to enable customization in the housing industry. The paper first proposes a comprehensive system to enable mass customization of housing, based on analysis of diverse research precedents, in addition to current industry applications. Then, a generative design model is proposed for the problem of space layout design. The model derives its logic from the concept of physical simulation and then implemented through a computer application that enables easy exploration and visualization of solutions. This paper represents a phase from an ongoing research endeavor to enable efficient customization in the housing industry through employing computational methods in design and production.

Keywords: Mass Customization, Space-layout Planning, Computational design, Physical Modeling

Introduction

ass customization, as defined by Pine (1993), is the production of individually customized goods and services. It relates to the ability to provide customized products or services through flexible processes, in high volumes and at reasonably low costs. The

concept emerged in the late 1980s and was viewed as a natural outcome of processes that have become increasingly flexible and optimized regarding quality and costs. The process of customization can be considered as a multi-faceted process that involves consideration of various aspects, such as managerial and technical. It is a production strategy that aims at providing customers with individualized products at near mass production efficiency. Recently, since many customers are increasingly demanding adaptation to their own requirements, most companies have shifted to customization in order to remain competitive. Implementation of mass customization as a business strategy enables companies to gain an advantage over competitors by providing additional features and benefits (Blecker and Friedrich 2006, 78).

Pertaining to the building industry, mass customization seems to be a suitable production paradigm as buildings are mostly unique, highly customized products. Today’s production processes, through the use of digital technologies both in design and fabrication in the form of Computer Aided Design/ Computer Aided Manufacturing (CAD/CAM), have proven to be a paradigm shift in production ideology (Kolarevic 2003, 23). Ever a vital sector in the building industry, housing has witnessed a renewed surge of interest in the last two decades, especially following these new approaches to modes of design and production. While this interest has taken many forms and constituencies, digital design and manufacturing strategies have inspired the most diverse research and pragmatic solutions to contemporary industry challenges. However, there is marked gap between proposed research approaches and current production practices, specifically in the prefabricated housing industry, which otherwise represents an ideal model to adopt mass customization.

Although unrecognized within standard housing production, current research acknowledges the need for advanced computer applications for enabling mass customization in the housing industry. In that sense, this paper explores the application of computational tools to enable a high level of customization in the housing industry. It proposes the implementation of a computer- based generative tool that could support customization at early design phase; space layout design. While

M

The International Journal of the Constructed Environment Volume 3, 2013, www.constructedenvironment.com, ISSN 2154-8587 © Common Ground, Basem Eid Mohamed, Aaron Sprecher, All Rights Reserved Permissions: [email protected]

THE INTERNATIONAL JOURNAL OF THE CONSTRUCTED ENVIRONMENT

such as generative tools has been utilized successfully in various cases, this paper focuses on integrating the idea of physical simulation within a comprehensive design system for customization, in the form of a software add-on.

To achieve the mentioned goal; the paper is structured according to the following sequence. The first section provides an introduction to the topic of mass customization in the housing industry. The second explores the applications of algorithms within the design process, and various approaches in spaces layout planning, through employing computational techniques. The third presents the methodology towards implementing a comprehensive process for mass customization, with focus on the generative too-based design system. Finally, conclusions, and future work.

Mass Customization of Housing

Mass customization of housing could be explored in two different domains; industry applications, and research directions. On the one hand, the common application of customization within the housing industry is to provide homebuyers different alternatives regarding layout, finishes and systems. For example, the homebuyer will be offered to choose between housing layout A, B, or C, then kitchen/bath layout A, B, or C, an optional extra garage or even extra story. Such an approach has been defined in research as “multiple choice housing”, which takes the form of printed catalogues and, recently, has been developed into interactive electronic catalogues. Electronic catalogues; typically, take the form of browser-based interfaces; such as applications by prefabricated housing companies in North America; LivingHomes, Bluhomes, Marmolradziner , and Connect: homes, offering homebuyers the ability to navigate, and then modify the design of a housing unit over the internet (Figure 1). However, such a trend is considered to be exhaustive, as the architect is required to formerly design all possible alternatives. As a result, in some cases the amount of alternatives has to be kept limited to three to four options in order to avoid additional overhead cost. Moreover, offering many choices might be confusing to some homebuyers. Nevertheless, there are still chances that customers’ desired design variation will not be offered, since alternatives are developed according to architect’s view, not the customer’s demand.

Figure 1: A snap-shot of Bluhomes’ Interactive, 3-D Configurator Plug-in Source: http://www.bluhomes.com/homeconfigurator/

On the other hand, there have been valuable contributions to the process of mass customization

of housing in two main domains. First, researchers have attempted to develop computer- based design systems to be used by architects or homebuyers to customize dwellings on various levels, ranging from direct participation in unit layout, to finishing material selection. Precedent research efforts have proposed various customization models that encompass design systems based on various generative models, such as shape grammars (Duarte 2001). Second, efforts have been made 36

BASEM EID MOHAMED AND AARON SPRECHER: DESIGN THROUGH COMPUTATION

to explore the potential of digital fabrication and how it could be deployed to enhance production processes, leading to the production of mass customized housing. While continuous innovation in computational design and fabrication technologies has made the idea of mass customization possible, these applications have not been fully integrated within the prefabricated housing industry, specifically in the North American context.

Within this research, the exploration is directed towards to highest level of customization, thus engage homebuyers at the earliest of the design stages denoted with the house layout development. In that sense, a generative tool is suggested to be employed, having the capability to generate housing layouts that responds to homebuyers’ requirements.

Figure 2: Levels of Customization in Housing Organized Sequentially from High to Low, as a Series of Decisions that Homebuyers Make.

Source: Author

Computation in Design and Architecture

The interest in design research emerged in the late 1960s with the aim of developing design methods based on science, technology and rationalism. Such interest targeted the systemization of the design process and methods, in addition to exploring modes of employing computers to the design and planning fields, along with the rise of computers as arithmetic and logical device, and Artificial Intelligence (AI). Along with the design methods movement, and with the rise of computers as arithmetic and logical devices, researchers defined a new role for computers that would support designers in the design process. Such a concept was developed with the purpose of partially, or fully automating the design process, thus assisting in both analysis and synthesis phase of design.

Various researchers proposed different methodologies to integrate computational techniques within the architectural design process. In the 1970s, approaches demonstrated an attempt to formalize interactive man- machine systems that would be intelligent enough to assist designers in the decision-making process of design. Applications at that time employed accessible programming techniques, focusing on fulfilling diverse design requirements (Mitchell 1977, 52). Recently, computer-based design systems have become more involved in the design process, offering architects, and designers the opportunity to investigate design problems from various angles, and on different levels. These systems operate on the basis of employing algorithms that are designed by programmers in order to take advantage of the arithmetic and logical capacities of computers, thus produce certain results.

An algorithm is a procedure of addressing a problem in a finite number of steps using logical if-then-else operations. It is either an expression of systematic problem-solving procedures, or a stochastic search towards potential solutions for a particularly known problem. In order to do so,

37


the process is codified through linguistic expressions into a series of finite, consistent, and rational steps (Terzidis 2006, 30). Algorithms use numerical methods to address problems, where in computational terms; numbers can be built to tackle an infinite level of division thus exhibiting theoretical stability. The main linguistic components used in algorithms are constraints, variables, procedures, and libraries and the basic operations are arithmetical, logical, combinational, and rational arranged under specific grammatical and syntactical rules. These components are structured to deal with the numerical nature of computers while composing logical models (Terzidis 2006, 34).

Generative design systems are composed of algorithms. They are the most malleable, as they can deal with any type of units, ones with fixed or flexible definitions. In computation, algorithms can only manipulate discrete units, one with fixed description, identity, or boundaries. These units are possibly numbers, alphabets, or geometric elements, which are then used to compose functions, or equations. The collection of functions; subtraction, addition, and others, lead to developing techniques for achieving certain tasks. These techniques are defined as methods, which can be grouped, taking the task of generating specific types of units. Accordingly, an algorithm can be regarded as a collection of methods. Algorithms employ randomness, probability, or complexity the outcome of which undefined, and unimaginable. They are about exploration, codification, and extension to the human mind, instead of interpretation.

When designing an algorithm, designer may decide to employ fixed or changeable units, which has all to be stored in placeholders. A placeholder for a fixed unit is called a constant, while for a changeable unit is called variable. A placeholder for constant or variable is a parameter. In order to control the operation of an algorithm, constraints are introduced to diverge values. If applied inaccurately, constraints may eradicate potential solutions or interrupt the generation process (Woodburry 2010, 78).

To explain more about the nature if these units: - Variables and parameters: Variables are used to store values that are continuously

changeable within the design process, while parameters are measures that are used to represent some characteristics of a modeled object and can be linked to other factors. Parameters are considered to be more static, and, in some cases, can be regarded as constants that change only in case the object needs to be modified.

- Constraints: Can be defined as restrictions on the degree of freedom within the process of finding a solution for a specific problem. Each constraint has to be considered carefully as it represents a potential to control the design process.

Similar to algorithms, design can be perceived as set procedures leading to a solution for a specific problem. Rather than emulating manual design methods, algorithms can be developed as methodologies complementing the human mind. They employ randomness, probability, for the sake of exploration, codification, and extension of human mind. Within the realm of architecture, there are some problems whose complexity level, vagueness, or array of potential solutions makes it difficult to handle though traditional techniques. In such case, algorithmic strategies can offer assistance by ensuring synergetic relationship between human mind and the machine. However, the role of algorithms in design can vary from exploring, organizing, to solving problems within increasing visual or organizational complexities (Kalay 2004, 102).

Commonly, the structure of an algorithm depends mainly on the problem. Yet, the most crucial factor is that the problem has to be describable. Within the operation of the algorithm, the computer reflects the human mind’s way of thinking, so the design could be explored as a mental process by monitoring human behavior throughout the performance of the machine. In order to do so, the complete set of instructions will be carried out by the computer without any human intervention , until the first set of results are obtained, and that the decision-making mechanism are built within the machine itself (Terzidis 2006, 38).

38


There are various synthesis algorithms that demonstrated potential significance in synthesis phase of the design process. The application of any of these algorithms depends mainly on the problem, its complexity level, and creativity extent of desired solution.

The Case of Space planning

Space layout planning is one of the most challenging phases of customization. Grouping together a set of spaces according to various relationships and diverse criteria, such as the ever-important issues of aesthetics, this problem presents complex objectives for both human and computer-based systems. Lobos and Donath (2010) stated that the problem of space layout planning is often ill-defined, necessitating a search through a set of both geometrical and topological design constraints that must be successively satisfied. Accordingly, the optimum solution to such a problem is difficult to find.

Creating a spatial layout or a plan for a building reflects the assignment of relationships to a group of functions then geometry these functions. The plan has to fulfill diverse criteria including adjacency between different functions, minimum circulation distance, area and volume efficiency, geometric composition, environmental performance, and economy. However, it has to be clear that the creation of a floor plan layout in architecture does not follow a precise methodology. Additionally, sometimes it is difficult to fulfill all design criteria. In the case of housing, specifically modular homes, an important criterion is that the housing layout has to reflect a modular design, being geometrically composed of rectangular shapes with a dimensional scalar that varies according to numerous factors.

Automated layout planning is concerned with applying CAD techniques towards devising solutions for architectural design problems; specifically, space planning. Numerous methods have been implemented using approaches from various disciplines, primarily engineering and computer science, since development of CAD techniques in architecture. Recently, with the rise of R&D units in architectural firms, such tools and applications have found its way into practice. Additionally, the significant advancements in CAD tools have offered great opportunities to expand the application of these tools in the design process, from conceptual design, design development, and production documents.

There are two primary approaches to space layout planning: aggregating spaces and subdividing spaces. Aggregating spaces builds a layout through which various pre-defined spaces are connected into one layout based on adjacency requirements. Conversely, subdividing spaces starts with a predefined layout which is then subdivided in accordance to predefined spatial typologies. These subdivisions are assigned activities according to programmatic requirements. Both approaches can be implemented through generative algorithms, and thus the design system will have to embody specific design logic.

Various algorithm-based approaches have been proposed to tackle the problem of automated layout planning, including shape grammar (Stiny, 1972) and (Duarte, 2001), evolutionary algorithms (Gero,97), and constraint based systems (Hsu, 2000), which have proven success towards the customization of housing. Yet, both systems embody a series of implications that dictates the exploration of a different methodology within this paper. On the one hand, shape grammar requires the presence of a clear design scheme, which was not the case of the problem investigated in this research. On the other hand, the implementation of multi-objective evolutionary algorithm is considered time consuming, and entails significant programming knowledge. In that sense, the authors propose the application of physical simulation algorithms, for a set of reasons that are explained in the following sections.

Problem and Methodology

Whereas the common application of customization in the housing industry is based on offering homebuyers choices through a configuration system, the intended application within this research

39


seeks to move beyond such an idea to a more advanced one. The proposed approach tends to employ a generative tool-based system to devise housing solutions, in response to the analysis of a homebuyer’s profile. Such an application is represented inclusively within a comprehensive model to mass customize prefabricated housing.

The structure of the customization system is composed of a series of interlinked phases as shown in Figure 3. While the focus in this research is on the generative model, it is important to understand that the selection of such a model emerges from analyzing the case of mass customization in the housing industry, in addition to approaches to space layout planning. The issue of automated layout planning, specifically for customization, raises several crucial aspects that influence the selection of an appropriate generative algorithm, most importantly; degree of system automation, the system operator, and representation technique. Within this paper, the focus is directed towards the first two aspects. Accordingly, design logic has been derived from an analysis of prior approaches to space layout planning, in addition to the prefabricated housing industry trends, with focus on exploring its appropriateness with the concept of customization.

Figure 3: A diagrammatic Representation of the Proposed Customization System.

Source(s): Author

Regarded as the core element of the customization system, the proposed algorithm is aimed at generating design solutions for a dwelling unit through systematically combining a set of predetermined spaces representing a set of basic activities that, when combined, will form a two-bedroom single-family house. The topological properties of these spaces were derived from a critical analysis that represents basic domestic activities. The listed spaces are categorized according to name (EN, LR, KT, DR, BA, LA, BR, MBR), and are dimensionally coordinated (i.e. their size is based on a common basic unit of 2’x2’). The design has to mostly satisfy adjacency requirements, and reflect topological and dimensional constraints. Spaces are fixed in size, and are derived from analyzing a set of the single family dwellings, provided by one of the leading

40


prefabricated housing companies operating in Quebec housing market. They represent a category from a wider range of an array of domestic spaces, developed to respond to a wider sector of the housing market. Table 1 defines the set of spaces. The following list of elements that constitute the problem required to be solved:

Table 1.1: Definition of Spaces Space 1 Entrance EN ( 5,4)

Space 2 Living room LV (8,6)

Space 3 Kitchen KT (4,6)

Space 4 Dining room DN ( 5,6)

Space 5 Bathroom BA ( 4,5) – possible L+1 increment

Space 6 Laundry LA (2,3)

Space 7 Bedroom BR (5,6) – possible L+1 increment

Space 8 Master Bedroom MBR (8,6)

Source(s): Author

Physical Simulation/ Modeling Technique

The application of cutting-edge software add-ons has shifted the way architects and designers tackle design problems, offering them greater power over the design process, from conceptual design and development to production documents. The methodology proposed by this research is based on a robust approach to problem solving; dynamic physical simulation, a concept developed by Arvin and House in 1999. The physical modeling technique derives its structure from the application of force and tension to space layout components. Architects define programmatic objectives in the usual manner, and represent proximity through a ‘spring connection’. This transforms the movement of space within a design via the compression or expansion of forces. Spaces and walls are modeled as physical objects and masses, while objectives specified in the architectural plans are translated into forces and applied to the masses in a dynamic physical simulation.

The implementation of such a process follows a series of phases that, while they might differ from one software platform to another, are connected by a unified concept. In general, topological objectives apply forces to the center of spaces, in the form of a spring dynamics, which are the primary component of physical modeling. These forces symbolize primary and secondary adjacency requirements, operating in 3-D environments. Once the forces are applied, geometric objectives assign volumes to specific points. The resultant volumes represent various domestic spaces, as derived from the basic program requirements.

In order to avoid the overlapping of volumes, constraining the length of the spring force to a specific range controls collision. This also maintains a precise distance between spaces, and ensures adjacency. Once the geometrical simulations have reached equilibrium, the designer can add a set of anchor points to suggest specific relationships to site conditions, thus making minute adjustments to the outcome by manipulating the solution components, in order to respond to additional design criteria other than adjacency. Figure 5 represents the outcome of applying the physical simulation approach to previously defined spaces.

41


Phase 1: defining a set of points, and then assigning geometrical objectives to points.

Phase 2: applying spring force between spaces, simulating proximity requirements.

The dynamic simulation starts running, thus transforming spaces along spring force connections, with regard to proximity objective.

Phase 3: Creating a set of anchor point that represents additional organizational criteria related to site, or performance of the housing unit to refine the location of different elements.

Refining the relationship between elements to reach a layout that would fulfill design requirements with regard to spatial organization, and other criteria added by the designer.

Figure 5: The process of the Physical- Simulation Based Generative Tool. Source: Author

Further elements, such as site conditions, could be added to refine the design generation

process, and to account for orientation, views, and accessibility. Each of these elements can be 42


translated into an anchor point, which would utilize a spring connection to generate forces from specific spaces, in accordance with defined requirements, such as the orientation of all bedrooms towards the north-east, as preferred by the homebuyer. Such an approach to design systems has proven successful, and offers great flexibility based on the employed software platform.

Discussion and Conclusion

Recent developments in computational design techniques have offered architects and designers tools that can support the process of decision making. These systems have been applied within various phases in the design process, and showed potential in areas including mass customization of housing. Coates (2010) argued that people might think that there are numerous differences between the recent applications on computation techniques in architecture and the ones developed in the late 1960s. In fact, the outcome of critically analyzing these approaches demonstrated that ideas have been conceptually very analogous, while the main differences are in computation techniques; coding and visualization, and remarkable advancements in software platform that offered designers a new perspective in solving problems.

Along with the development of these tools in the form or plug-ins and add-ons to existing software packages, remarkable applications have emerged, examining form finding techniques, issues with cladding patterns, environmental simulations, and Building Information Modeling (BIM).

This paper describes recent developments in research aimed at devising a computational framework for enabling high levels of customization in the housing industry. It explores the role of the machine as an enabler for mass customization, through employing a physical simulation model for space layout planning. In that sense, the proposed approach tackled the system operator and degree of automation as two crucial aspects of implementation. Accordingly, the computational tool would be operated by the company designer has been proposed. However, it has to be understandable that the paper focuses on the integration of such a tool within the customization system, rather than the novelty of the computational approach. In other words, the paper does not intend to propose a novel approach to automated layout planning, yet the suitability, and application of physical simulation to enable customization.

The process utilizes the described computational tools to bring a homebuyer’s profile together with logic of construction and design, and to ultimately generate a housing design. The resulting output of the generative tool is not a formal solution, yet it is represents a diagram in the form of an organizational layout of a housing unit. Such model raises two crucial issues when implementing a computer- based design system to enable customization in the housing industry, rather than offering a definitive solution. These two issues are related to system operator, and degree of automation. The physical simulation approach requires to be operated by an experienced designer who would be able to translate design requirements into set of procedures and relationships. On the other hand, automating the design process at such level would facilitate the process of generating design that respond to specific homebuyers’ needs and requirements.

Reccomendations for Future Work

Future development of such a system would explore the possibilities of expanding the proposed design by linking it to a spreadsheet that comprises a wider list of domestic spaces, classified according to area, typology, and size criteria. The classification would aim at responding to a wider sector of the housing market, based on a detailed analysis of household needs. Spaces would be created in a BIM environment, to allow the development of production data and instructions. Additionally, special focus will be dedicated to investigate the possibility of combining physical simulation, with an evolutionary algorithm, to benefit from high creativity of evolutionary systems, and expand the system’s ability to optimize resulting solutions.

43


REFERENCES

Aguilar, Rodolfo J. 1973. Systems Analysis and Design in Engineering, Architecture, Construction, and Planning. N.J., Prentice-Hall: Englewood Cliffs, 34-75.

Arvin, S., and D. House. 1999. "Physically Based Modeling Technique in Space Layout Planning." Paper presented at the International Conference on Computer Aided Architectural Design Futures, Atlanta, Georgia, June7-8, 1999.

Benros, D., and J.P. Duarte. 2009. "An Integrated System for Providing Mass Customized Housing." Automation in Construction 18, no. 3: 310-20.

Blecker, Thorsten, and Gerhard Friedrich. 2006. Mass Customization: Challenges and Solutions. New York: Springer,78.

Coates, Paul. 2010. Programming Architecture. New York: Routledge,162. Cross, Nigel. 1984. The Automated Architect. London: Pion, 28. Duarte, José Pinto. 2001. "Customizing Mass Housing : A Discursive Grammar for Siza's

Malagueira Houses." PhD doss., Massachusetts Institute of Technology. Gero, John S., and Vladimir A. Kazakov. 1998. "Evolving design genes in space layout planning

problems." Artificial Intelligence in Engineering no. 12 (3):163-176. doi: http://dx.doi.org/10.1016/S0954-1810(97)00022-8.

HSU, Y.C. 2000. "Constraint Based Space Planning: A Case Study". ACADIA Quarterly, 19(3):2-3.

Jon McCormack, Alan Dorin, Troy Innocent. 2004. " Generative design: a paradigm for design research". FutureGround. Melbourne: Design Research Society.

Kalay, Yehuda. 2003. Architecture' New Media: Principles, Theories, and Methods of Computer- Aided Design. Cambridge, Mass.: MIT Press,102.

Kolarevic, Branko. 2003. Architecture in the Digital Age: Design and Manufacturing. New York: NY Spon Press, 23.

Koza, Joh.1992. Genetic Programming: On the programming of computers by natural selection. Cambridge: MIT press, 87.

Lobos, Danny, and Dirk Donath. 2010. "The Problem of Space Layout in Architecture: A Survey and Reflections." Arqhiteturarevista 6, no.2: 136- 61.

Mitchell, William J. 1977. Computer-Aided Architectural Design. New York: Petrocelli/Charter, 52.

Mitchell, William J. 1990. The Logic of Architecture : Design, Computation, and Cognition. Cambridge, Mass: MIT Press, 94.

Pine, B Joseph. 1993. Mass Customization: The New Frontier in Business Competition. Boston, Mass.: Harvard Business School Press, 38.

Terzidis, Kostas. 2006. Algorithmic Architecture. Oxford,Burlington,MA: Architectural Press, 34. Woodbury, Robert. 2010. Elements of Parametric Design. New York: Rootledge, 86.

ABOUT THE AUTHORS

Basem Eid Mohamed: Basem has completed his Doctoral degree at McGill University School of Architecture. His research focuses on the application of cutting-edge computational design tools and techniques to enable mass customization in the housing industry, specifically in the realm of prefab. He received a Mitacs research grant in 2010-11 to explore the applicability of such an approach within the structure of a leading prefabricated housing company in Quebec province, Canada. Besides a couple of publications, he has taught at AAST in Alexandria-Egypt, as well as at McGill School of Architecture. Additionally, Basem has collaborated with a number of Montreal architectural firms on some residential and commercial projects.

44


Aaron Sprecher: Aaron Sprecher has been an assistant professor at the School of Architecture in the Faculty of Engineering, McGill University since 2008. In parallel, he is co-founder and partner of Open Source Architecture, an international collaborative research group that brings together leading international researchers in the fields of design, engineering, media research, history and theory. He completed his graduate studies at Bezalel Academy of Art and Design (Israel) and the University of Californi--Los Angeles. His research and design work focuses on the synergy between information technologies, computational languages and automated digital systems, examining the way in which technology informs and generates innovative approaches to design processes. Beside numerous publications and exhibitions, he has lectured in many institutions including the Massachusetts Institute of Technology, The Cooper Union, and Harvard University. He is a recipient of research grants, most recently, awarded with the prestigious Canada Foundation for Innovation award and Social Sciences and Humanities Research Council (SSHRC) grants.

45

The International Journal of the Constructed Environment explores, in a broad-ranging and interdisciplinary way, human configurations of the environment and the interactions between the constructed, social and natural environments. The journal brings together the work of practitioners, researchers, and teachers in the architectonic and landscape arts. The resulting articles weave between the empirical and the theoretical, research and its application, the ideal and the pragmatic. They document and reflection upon spaces which are in their orientations private, public, or commercial.

As well as papers of a traditional scholarly type, this journal invites presentations of practice—including experimental forms of documentation and exegeses that can with equal validity be interrogated through a process of academic peer review. This, for instance, might take the form of a series of images and plans, with explanatory notes that articulate with other, significantly similar or different and explicitly referenced places, sites or material objects.

The International Journal of the Constructed Environment is a peer-reviewed scholarly journal.

ISSN 2154-8587

Design Through Computation: Explorations in Space Layout Planning Towards Mass Customization of...

Documents

Transcript of Design Through Computation: Explorations in Space Layout Planning Towards Mass Customization of...