Automated space layout planning for environmental sustainability

There is a growing global interest in low/zero carbon buildings in response to the increased CO2 in the atmosphere, nearly half of which comes from building energy consumption. Buildings are built for a considerably longer lifespan and enhancing energy efficiency in buildings can play a significant role in reducing CO2 emissions. Energy efficiency features need to be incorporated at the earliest, as alterations to the design at latter stages may prove to be difficult and sometimes expensive. Building design is concerned with satisfying various objectives (e.g. cost, efficiency of a space layout, energy consumption), which are sometimes in conflict with each other. Performance of various indicators, therefore, needs to be assessed as a whole rather than in isolation. Space layout planning is considered as the starting point of building design. Most performance indicators; i.e. cost, energy efficiency, etc. are closely linked with the layout. Researchers have attempted at automating space layout planning since the 1960s with a view to effectively search the solution space. Diverse approaches are adopted in space layout planning that ranges from the analysis of spatial proximity to the application of ‘space syntax’ theory. Developments in whole building energy simulation and integration of simulation in the design process imply that the search for optimum space layout could be better guided by incorporating detailed-based simulation as response generators as opposed to the ones with a simplified representation of the problem domain. This paper describes a framework for sustainable space layout planning that uses evolutionary computation methods to search the solution space. Whole building simulation programs are used as response generators to guide the search for energy efficient layouts. The integrated approach enables the consideration of energy consumption, in addition to the geometry and topology, for decision making during space layout planning.

[1]  Russell P. Leslie,et al.  Capturing the daylight dividend in buildings: why and how? , 2003 .

[2]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[3]  Panos M. Pardalos,et al.  Simulated Annealing and Genetic Algorithms for the Facility Layout Problem: A Survey , 1997, Comput. Optim. Appl..

[4]  Svend Svendsen,et al.  Life cycle cost optimization of buildings with regard to energy use, thermal indoor environment and daylight , 2002 .

[5]  Edward Henry Mathews,et al.  Improved thermal building management with the aid of integrated dynamic HVAC simulation , 2003 .

[6]  Leslie K. Norford,et al.  A design optimization tool based on a genetic algorithm , 2002 .

[7]  John S. Gero,et al.  A Genetic Engineering Approach to Genetic Algorithms , 2001, Evolutionary Computation.

[8]  P. R. Head Construction materials and technology: a look at the future , 2001 .

[9]  J. Palutikof,et al.  Climate change 2007 : impacts, adaptation and vulnerability , 2001 .

[10]  John Grason An approach to computerized space planning using graph theory , 1971, DAC '71.

[11]  Christian Stoy,et al.  Benchmarking electricity consumption , 2006 .

[12]  Elwood S. Buffa,et al.  A Heuristic Algorithm and Simulation Approach to Relative Location of Facilities , 1963 .

[13]  Charles M. Eastman,et al.  Automated Space Planning , 1973, Artif. Intell..

[14]  William J. Mitchell,et al.  Optimal space planning in practice , 1981 .

[15]  Robert Woodbury,et al.  Software Environment to Support Early Phases in Building Design (SEED): Overview , 1995 .

[16]  William R. Miller Computer-aided space planning , 1970, DAC '70.

[17]  Amar Bennadji,et al.  The engineering dimension of nD modelling , 2005, J. Inf. Technol. Constr..

[18]  U. Flemming Wall Representations of Rectangular Dissections and Their Use in Automated Space Allocation , 1978 .

[19]  John S. Gero,et al.  Space layout planning using an evolutionary approach , 1998, Artif. Intell. Eng..

[20]  Luisa Caldas Generation of energy-efficient architecture solutions applying GENE_ARCH: An evolution-based generative design system , 2008, Adv. Eng. Informatics.

[21]  Jonathan A. Wright,et al.  Optimization of building thermal design and control by multi-criterion genetic algorithm , 2002 .

[22]  John S. Gero,et al.  Tradeoff diagrams for the integrated design of the physical environment in buildings , 1980 .

[23]  John S. Gero,et al.  A Genetic Programming Approach to the Space Layout Planning Problem , 1997 .

[24]  V. I. Hanby,et al.  Optimization of distribution piping network in district cooling system using genetic algorithm with local search , 2007 .

[25]  P. R. Head Construction materials and technology: a look at the future , 2001 .

[26]  John S. Gero,et al.  An Evolutionary Approach to Generating Constraint-Based Space Layout Topologies , 1997 .

[27]  Bernard Yannou,et al.  Dynamic space ordering at a topological level in space planning , 2001, Artif. Intell. Eng..

[28]  Amar Bennadji,et al.  The engineering dimension of nD modelling: performance assessment at conceptual design stage. , 2005 .

[29]  John Christopher Miles,et al.  The conceptual design of commercial buildings using a genetic algorithm , 2001 .

[30]  Denis Kelliher,et al.  ArDOT: a tool to optimise environmental design of buildings , 2003 .

[31]  Robin S Liggett,et al.  Automated facilities layout: past, present and future , 2000 .

[32]  Z. Grodzki,et al.  Optimisation of industrialised building systems , 1975 .

[33]  G. Stiny Introduction to Shape and Shape Grammars , 1980 .

[34]  Nisansa de Silva,et al.  INTEGRATING SUSTAINABLE DEVELOPMENT AND CLIMATE CHANGE IN THE IPCC FOURTH ASSESSMENT REPORT , 2009 .