An interactive model for the layout of buildings

Sharpe, Ron; Marksjö, Bertil; Mitchell, Joel; Crawford, J.R

doi:10.1016/0307-904x(85)90009-5

Cited by 19 publications

(8 citation statements)

References 4 publications

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“…Re-8 lated work in this area has examined the positioning of individual facilities within a city (e.g. shopping malls, Yu et al, 2007), laying out space within buildings (Sharpe et al, 1985), the planning of development densities around transit stations (Lin and Gau, 2006), and using multi-objective optimization to generate a range of Pareto optimal layouts for discussion with planners (Jiang-Ping and Qun, 2009).…”

Section: Modelling Urban Form and Functionmentioning

confidence: 99%

Calculating minimum energy urban layouts with mathematical programming and Monte Carlo analysis techniques

Keirstead

Shah

2011

Computers, Environment and Urban Systems

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Cities consume significant amounts of energy and improving their efficiency is an integral part of tackling global climate change. As existing urban layouts can be difficult to change, new developments offer significant opportunities for demonstrating low energy urban forms. However the limits of these improvements are not well known and so this paper presents an optimization tool for designing minimum energy urban layouts, considering both the transport and building sectors. Our work builds upon the excess commuting and sketch modelling literatures, with a greater focus on energy consumption as a specific objective and improved model performance.After validating the model with the case of a UK eco-town, Monte Carlo analysis is used to assess how the minimum energy benchmark varies in response to planning constraints on high-density housing and car ownership.The methodology presented here therefore offers a new tool for calculating absolute minimum urban energy benchmarks, which can be used early in the planning process to complement more behaviourally-realistic land-use transportation models.

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Section: Modelling Urban Form and Functionmentioning

confidence: 99%

Calculating minimum energy urban layouts with mathematical programming and Monte Carlo analysis techniques

Keirstead

Shah

2011

Computers, Environment and Urban Systems

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“…Using a different definition of topology, such as in Refs. [3][4][5], it is possible to eliminate the rough position topology variables, and ensure that the mapping from topology to local optimal geometry is both injective and surjective (meaning that each valid topology will create a different locally optimal geometries, and that all possible local optimal geometry could be created by a specific topology).…”

Section: Architectural Design 481mentioning

confidence: 99%

“…One approach to spatial allocation is to define the available space as a set of grid squares and use an algorithm to allocate each square to a particular room or activity [4][5][6][7] (see Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

“…Spatial configuration is relevant to all physical design problems, so it is an important area of inquiry. Research on automation of spatial configuration includes component packing [11][12][13], route path planning [18], process and facilities layout, VLSI design ½16; 17, and architectural layout [3][4][5][6][7][8][9][10]. Architectural layout is particularly interesting because in addition to common engineering objectives such as cost and performance, architectural design is especially concerned with aesthetic and usability qualities of a layout, which are generally more difficult to describe formally.…”

Section: Introductionmentioning

confidence: 99%

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Architectural layout design optimization

Michalek

Choudhary

Papalambros³

2002

Engineering Optimization

179

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This article presents an optimization model of the quantifiable aspects of architectural floorplan layout design, and a companion article presents a method for integrating mathematical optimization and subjective decision making during conceptual design. The model presented here offers a new approach to floorplan layout optimization that takes advantage of the efficiency of gradient-based algorithms, where appropriate, and uses evolutionary algorithms to make discrete decisions and do global search. Automated optimization results are comparable to other methods in this research area, and the new formulation makes it possible to integrate the power of human decision-making into the process.

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“…Mitchell [2], for example, discusses the merits of automated design in architecture and also offers a method for generating least cost floor plan layouts [3]. In the past few decades various computational tools have been developed for automated design of floor plan layouts [4][5][8][9][10][11][12][13][14][15][16][17][18]. In general, these tools attempted to find the best possible floor plan layout to a given objective and set of constraints by searching through the space of all possible solutions.…”

Section: Introductionmentioning

confidence: 99%

Collective Intelligence: An Agent-Based Approach to Programmatic Organization in Architecture

Ophir

2009

International Journal of Architectural Computing

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Architectural programming is the research and decision-making process that identifies the scope of work to be designed. Programming is difficult because it involves identifying, collecting, analyzing and updating information from different sources such as engineers, clients, users, consultants, and others. In this paper I propose a computational model for programming and describe its implementation, a tool called PENA that allows a programming expert to represent different processes and people involved in a project using intelligent agents. By delegating responsibility to agents, a programming expert can better organize and manage project data as well as find creative solutions to conflicting issues through agent negotiation. As a proof-of-concept, I show how an agent, called the Arch-Learner, manages adjacencies of rooms in a simple program for a house by clustering them into public and private rooms. I conclude with a discussion of future work and development of PENA.

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An interactive model for the layout of buildings

Cited by 19 publications

References 4 publications

Calculating minimum energy urban layouts with mathematical programming and Monte Carlo analysis techniques

Calculating minimum energy urban layouts with mathematical programming and Monte Carlo analysis techniques

Architectural layout design optimization

Collective Intelligence: An Agent-Based Approach to Programmatic Organization in Architecture

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