Multidisciplinary process integration and design optimization of a classroom building

SUMMARY: Architecture, Engineering, and Construction (AEC) professionals typically generate and analyze very few design alternatives during the conceptual stage of a project. One primary cause is limitations in the processes and software tools used by the AEC industry. The aerospace industry has overcome similar limitations by using Process Integration and Design Optimization (PIDO) software to support Multidisciplinary Design Optimization (MDO), resulting in a significant reduction to design cycle time as well as improved product performance. This paper describes a test application of PIDO to an AEC case study: the MDO of a classroom building for structural and energy performance. We demonstrate how PIDO can enable orders of magnitude improvement in the number of design cycles typically achieved in practice, and assess PIDO’s potential to improve AEC MDO processes and products.

[1]  Kristina Shea,et al.  Towards integrated performance-driven generative design tools , 2004 .

[2]  D. Grierson,et al.  Optimal sizing, geometrical and topological design using a genetic algorithm , 1993 .

[3]  K. Deb,et al.  Design of truss-structures for minimum weight using genetic algorithms , 2001 .

[4]  Thomas E. Cheatham,et al.  Software Technology in the 1990's: Using a New Paradigm , 1983, Computer.

[5]  Kristina Shea,et al.  EVOLVING OPTIMIZED BRACED STEEL FRAMEWORKS FOR TALL BUILDINGS USING MODIFIED PATTERN SEARCH , 2005 .

[6]  Donald E. Grierson,et al.  Welfare Economics Applied to Design Engineering , 2006, EG-ICE.

[7]  Craig Christensen,et al.  BEopt(TM) Software for Building Energy Optimization: Features and Capabilities , 2006 .

[8]  John S. Gero,et al.  Modelling expertise of temporary design teams , 2004 .

[9]  Tomasz Arciszewski,et al.  Evolutionary Design of Steel Structures in Tall Buildings , 2005 .

[10]  David Shaw,et al.  Determining the structural layout of orthogonal framed buildings , 2008 .

[11]  T Haftka Raphael,et al.  Multidisciplinary aerospace design optimization: survey of recent developments , 1996 .

[12]  Ömer Akin,et al.  Variants in Design Cognition , 2001 .

[13]  W. Newstetter,et al.  Design Knowing and Learning: Cognition in Design Education , 2001 .

[14]  P. Torcellini,et al.  Automated Multivariate Optimization Tool for Energy Analysis , 2006 .

[15]  O. Hasançebi,et al.  Adaptive evolution strategies in structural optimization: Enhancing their computational performance with applications to large-scale structures , 2008 .

[16]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[17]  Z Turk.  Integration of existing programs using frames , 1991 .

[18]  Ghang Lee,et al.  Product data modeling using GTPPM - A case study , 2007 .

[19]  I. C. Parmee,et al.  Supporting implicit learning via the visualisation of COGA multi-objective data , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[20]  Kalyanmoy Deb,et al.  Evolutionary Algorithms for Multi-Criterion Optimization in Engineering Design , 1999 .

[21]  Henri Christiaans,et al.  Design Knowing and Learning: Cognition in Design Education , 2002 .

[22]  Ian C. Parmee,et al.  Evolutionary and adaptive computing in engineering design , 2001 .

[23]  John Haymaker,et al.  Coordinating Goals, Preferences, Options, and Analyses for the Stanford Living Laboratory Feasibility Study , 2006, EG-ICE.

[24]  Radu Zmeureanu,et al.  A Comparative Study Of Representation And Encodings For Building Shape Optimization With Genetic Algorithms , 2006 .

[25]  John C. Kunz,et al.  Perspectors: composable, reusable reasoning modules to construct an engineering view from other engineering views , 2004, Adv. Eng. Informatics.

[26]  I. C. Parmee.  Human — Centric Intelligent Systems for Design Exploration and Knowledge Discovery , 2005 .

[27]  G.M. Stump,et al.  Trade space exploration of satellite datasets using a design by shopping paradigm , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[28]  Claude Bédard,et al.  Interactive computer support for the conceptual design of building structures , 2006 .

[29]  Mark Burry,et al.  Parametric Design and Structural Optimisation for Early Design Exploration , 2007 .

[30]  Chee Kiong Soh,et al.  Genetic programming-based approach for structural optimization , 2000 .

[31]  Ayse T. Daloglu,et al.  An improved genetic algorithm with initial population strategy and self-adaptive member grouping , 2008 .

[32]  Andrew J. Booker,et al.  A comparison of optimization and search methods for multidisciplinary design , 1992 .

[33]  Luisa Caldas,et al.  GENE_ARCH: An Evolution-Based Generative Design System for Sustainable Architecture , 2006, EG-ICE.

[34]  Ian F. C. Smith,et al.  Intelligent computing in engineering and architecture , 2008, Adv. Eng. Informatics.

[35]  Robert Aish,et al.  Multi-level Interaction in Parametric Design , 2005, Smart Graphics.

[36]  K. F. Fong,et al.  HVAC system optimization for energy management by evolutionary programming , 2006 .

[37]  Jaroslaw Sobieszczanski-Sobieski,et al.  Multidisciplinary aerospace design optimization - Survey of recent developments , 1996 .

[38]  I. C. Parmee,et al.  OVERCOMING REPRESENTATION ISSUES WHEN INCLUDING AESTHETIC CRITERIA IN EVOLUTIONARY DESIGN , 2005 .

[39]  Robert P. Smith,et al.  A predictive model of sequential iteration in engineering design , 1997 .

[40]  Wolfgang Beitz,et al.  Engineering Design: A Systematic Approach , 1984 .

[41]  Ian C. Parmee,et al.  Towards an Interactive, Generative Design System: Integrating a 'Build and Evolve' Approach with Machine Learning for Complex Freeform Design , 2009, EvoWorkshops.

[42]  Martin Fischer,et al.  Formalizing Construction Knowledge for Concurrent Performance-Based Design , 2006, EG-ICE.

[43]  D. E. Grierson,et al.  Method for Conceptual Design Applied to Office Buildings , 2002 .

[44]  Chin-Jung Chen,et al.  Design optimisation for automotive applications , 2001 .

[45]  C. C. Cappiello,et al.  DOE-2 engineers manual (Version 2. 1A) , 1981 .

[46]  BalzerR.,et al.  Software Technology in the 1990's , 1983 .

[47]  I. C. Parmee,et al.  User-Centric Evolutionary Computing: Melding Human and Machine Capability to Satisfy Multiple Criteria , 2008, Multiobjective Problem Solving from Nature.

[48]  Jonathan A. Wright,et al.  COMPARISON OF A GENERALIZED PATTERN SEARCH AND A GENETIC ALGORITHM OPTIMIZATION METHOD , 2003 .

[49]  Mary Beth Rosson,et al.  Survey on user interface programming , 1992, CHI.

[50]  Beth A. Bechky,et al.  When Collections of Creatives Become Creative Collectives: A Field Study of Problem Solving at Work , 2006, Organ. Sci..

[51]  John L. Dettbarn,et al.  Cost Analysis of Inadequate Interoperability in the U.S. Capital Facilities Industry. , 2004 .

[52]  V. Braibant,et al.  Structural optimization: A new dual method using mixed variables , 1986 .

[53]  John Haymaker,et al.  A comparison of multidisciplinary design, analysis and optimization processes in the building construction and aerospace industries , 2007 .

[54]  Donald E. Grierson,et al.  Pareto multi-criteria decision making , 2008, Adv. Eng. Informatics.

[55]  Charles M. Eastman,et al.  Deployment of an AEC industry sector product model , 2005, Comput. Aided Des..

[56]  L. Berke,et al.  Structural optimization using optimality criteria , 1987 .

[57]  Jami J. Shah,et al.  Parametric and Feature-Based CAD/CAM: Concepts, Techniques, and Applications , 1995 .

[58]  Thomas A. Grandine,et al.  The search for the perfect body: Shape Control for multidisciplinary design optimization , 2006 .

[59]  Tetsuo Tomiyama,et al.  Advanced Engineering Informatics , 2007, Adv. Eng. Informatics.

[60]  D. E. Grierson,et al.  Profitability versus safety of high-rise office buildings , 2003 .

[61]  John Haymaker,et al.  COMMUNICATING, INTEGRATING AND IMPROVING MULTIDISCIPLINARY DESIGN NARRATIVES , 2006 .

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

[63]  Dominik Holzer,et al.  Developing a Framework for Linking Design Intelligence from Multiple Professions in the AEC Industry , 2007 .

[64]  Ian F. C. Smith,et al.  Improving Full-Scale Transmission Tower Design through Topology and Shape Optimization , 2006 .

[65]  Paolo Ermanni,et al.  Evolutionary truss topology optimization using a graph-based parameterization concept , 2006 .

[66]  Weimin Wang,et al.  An object-oriented framework for simulation-based green building design optimization with genetic algorithms , 2005, Adv. Eng. Informatics.

[67]  D. V. Steward.  Systems Analysis and Management: Structure, Strategy and Design , 1981 .

[68]  Kalyanmoy Deb,et al.  Multiobjective Problem Solving from Nature: From Concepts to Applications , 2008, Natural Computing Series.