BIMBOT-(Artificial intelligence applied to BIM design)

César Frías; Jose María Peña; Érika Sánchez; Lorena Almeida

doi:10.4995/ege.2020.13942

Autores/as

César Frías Architecture Meets Engineering S.L https://orcid.org/0000-0002-1203-3227
Jose María Peña LURTIS RULES S.L,
Érika Sánchez Architecture Meets Engineering S.L https://orcid.org/0000-0002-3558-1330
Lorena Almeida LURTIS RULES S.L.

DOI:

https://doi.org/10.4995/ege.2020.13942

Palabras clave:

AI, Machine Learning, Soft Computing, desarrollo de software, arquitectura, DataBase

Resumen

BIMBOT es un asistente de diseño inteligente para la industria AEC. Sus herramientas se ejecutan sobre un software de modelado BIM y producen varias soluciones de diseño con modelos BIM optimizados. Funciona con el uso de métodos avanzados de Inteligencia Artificial (optimización soft computing y Machine Learning) y es compatible con bases de datos NoSQL. Contempla varias etapas: La definición por el usuario de restricciones / prioridades establecidas ejecuta un proceso de diseño generativo impulsado por varios métodos de IA. Éste crea diferentes soluciones en modelos BIM almacenados y refinados a partir de un catálogo de objetos inteligentes. Con ello, los usuarios pueden interactuar importando modelos BIM con diseños propuestos, crearlos o editarlos in situ y recibir asistencia de una serie de métricas configurables que dan calidad al diseño de acuerdo con las preferencias iniciales. Así, obtenemos un Modelo BIM completo como resultado del proceso iterativo. Finalmente, el entrenamiento continuo de los algoritmos mejorará la eficiencia en futuros diseños. BIMBOT está concebido para extender las habilidades de los diseñadores a través del desarrollo de software BIM, permitiéndoles ser más productivos en tareas complejas del proceso de diseño. BIMBOT está financiado por el programa europeo Eureka / Eurostars (E! 12863).

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

Jose María Peña, LURTIS RULES S.L,

CTO

Érika Sánchez, Architecture Meets Engineering S.L

Especialista I+D BIM

Lorena Almeida, LURTIS RULES S.L.

Architect

Citas

I. As, S. Pal y P. Basu, 2018 «Artificial intelligence in architecture: Generating conceptual design via deep learning,» International Journal of Architectural Computing, vol. 16, nº 4, pp. 306-327. https://doi.org/10.1177/1478077118800982

M. Ashouri, F. Haghighat, B. C. Fung, A. Lazrak y H. Yoshino, 2018 «Development of building energy saving advisory: A data mining approach,» Energy and Buildings, vol. 172, pp. 139-151. https://doi.org/10.1016/j.enbuild.2018.04.052

S. Attia, E. Gratia, A. D. Herde y J. L. M. Hensen, 2012 «Imulation-based decision support tool for early stages of zero-energy building design,» Energy and Buildings, vol. 49, pp. 2-15. https://doi.org/10.1016/j.enbuild.2012.01.028

M. Bhatt, J. Suchan, C. Schultz, V. Kondyli y S. Goyal, 2016 «Artificial Intelligence for Predictive and Evidence Based Architecture Design,» de Thirtieth AAAI Conference on Artificial Intelligence.

A. Buluç, H. Meyerhenke, I. Safro, P. Sanders y C. Schulz, 2016 «Recent Advances in Graph Partitioning,» Algorithm Engineering, vol. 9220, pp. 117-158. https://doi.org/10.1007/978-3-319-49487-6_4

G. G. Chowdhury, 2003 «Natural language processing,» Annual review of information science and technology, vol. 37, nº 1, pp. 51-89. https://doi.org/10.1002/aris.1440370103

M. Colledanchise y P. Ögren, 2009 «How Behavior Trees modularize robustness and safety in hybrid systems,» de RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA, 2014. https://doi.org/10.1109/IROS.2014.6942752

D. J. Cook, J. C. Augusto y V. R. Jakkula, «Ambient intelligence: Technologies, applications, and opportunities,» Pervasive and Mobile Computing, vol. 5, nº 4, pp. 277-298. https://doi.org/10.1016/j.pmcj.2009.04.001

D. B. Crawley, J. W. Hand, M. Kummert y B. T. Griffith, 2008 «Contrasting the capabilities of building energy performance simulation programs,» Building and Environment, vol. 43, nº 4, pp. 661-673. https://doi.org/10.1016/j.buildenv.2006.10.027

I. G. Dino, 2012 «Creative design exploration by parametric generative systems in architecture,» METU Journal of Faculty of Architecture, vol. 29, nº 1, pp. 207-224.

R. Evins, 2013«A review of computational optimisation methods applied to sustainable building design,» Renewable and Sustainable Energy Reviews, vol. 22, pp. 230-245. https://doi.org/10.1016/j.rser.2013.02.004

R. Gagnon, L. Gosselin, S. Park, S. Stratbücker y S. Decker, 2019 «Comparison between two genetic algorithms minimizing carbon footprint of energy and materials in a residential building,» Journal of Building Performance Simulation, vol. 12, pp. 224-242. https://doi.org/10.1080/19401493.2018.1501095

D. J. Gerber y S.-H. E. Lin, 2013 «Designing in complexity: Simulation, integration, and multidisciplinary design optimization for architecture,» Simulation, vol. 90, nº 8, pp. 936-959. https://doi.org/10.1177/0037549713482027

P. Hansen, N. Mladenović y J. A. Moreno Pérez, 2010 «Variable neighbourhood search: methods and applications,» Annals of Operations Research, vol. 175, p. 367-407. https://doi.org/10.1007/s10479-009-0657-6

M. Hendrikx, S. Meijer, J. Van Der Velden y A. Iosup, 2013 «Procedural content generation for games: A survey,» . ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 9, nº 1, pp. 1-22. https://doi.org/10.1145/2422956.2422957

X. Jun, J. Annan, W. Zhiwu y Q. Jingping, 2013 «A Nonlinear Optimization Technique of Tunnel Construction Based on DE and LSSVM,» Mathematical Problems in Engineering, vol. 2013, p. 11. https://doi.org/10.1155/2013/980154

D. Karavolos, A. Liapis y G. N. Yannakakis, 2019 «A Multi-Faceted Surrogate Model for Search-based Procedural Content Generation,» IEEE Transactions on Games, pp. 1-1. https://doi.org/10.1109/TG.2019.2931044

A. LaTorre, S. Muelas y J. M. Peña, 2015 «A comprehensive comparison of large scale global optimizers,» Information Sciences, vol. 316, nº C, pp. 517-549. https://doi.org/10.1016/j.ins.2014.09.031

P. Lu, S. Chen y Y. Zheng, 2012 «Artificial Intelligence in Civil Engineering,» Mathematical Problems in Engineering, vol. 2012, p. 22. https://doi.org/10.1155/2012/145974

V. Machairas, A. Tsangrassoulis y K. Axarli, 2014 «Algorithms for optimization of building design: A review,» Renewable and Sustainable Energy Reviews, vol. 31, pp. 101-112. https://doi.org/10.1016/j.rser.2013.11.036

J. McCormack, A. Dorin y T. Innocent, 2004 «Generative design: a paradigm for design research,» de Proceedings of Futureground, Melbourne, Australia.

T. McGinley, J. Collins, Q. Schwarz y M. Muehlbauer, 2016 «Suburban mutations: towards the multidimensional appropriation of science in architecture,» de Architectural Science Association Conference.

T. Méndez-Echenagucia, A. Capozzoli, Y. Cascone y M. Sassone, 2015 «The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis,» Applied Energy, vol. 154, pp. 577-591. https://doi.org/10.1016/j.apenergy.2015.04.090

S. Muelas, J. Peña, K. Muzhetskaya, A. LaTorre y P. D. Miguel, 2008 «Optimizing the Design of Composite Panels using an Improved Genetic Algorithm,» de Proceedings of the International Conference on Engineering Optimization (EngOpt'08), Rio de Janeiro.

D. Nagy, D. Lau, J. Locke, J. Stoddart, L. Villaggi, R. Wang, D. Zhao y D. Benjamin, 2017 «Project Discover: An application of generative design for architectural space planning,» de Proceedings of the Symposium on Simulation for Architecture and Urban Design, Toronto, ON, Canada.

N. Noilublao y S. Bureerat, 2013 «Simultaneous topology, shape, and sizing optimisation of plane trusses with adaptive ground finite elements using MOEAs,» Mathematical Problems in Engineering, vol. 2013, p. 12. https://doi.org/10.1155/2013/838102

J. M. Peña, A. LaTorre y A. Jérusalem, 2019 «SoftFEM: The Soft Finite Element Method,» International Journal for Numerical Methods in Engineering, vol. 118, nº 10, pp. 606-630. https://doi.org/10.1002/nme.6029

J. M. Peña, J. Viedma, S. Muelas, A. LaTorre y L. Peña, 2014 «Designer-driven 3D buildings generated using Variable Neighborhood Search,» de IEEE Conference on Computational Intelligence and Games, Dortmund, Germany. https://doi.org/10.1109/CIG.2014.6932902

D. Pham y P. Pham, 1999 «Artificial intelligence in engineering,» International Journal of Machine Tools and Manufacture, vol. 39, nº 6, pp. 937-949. https://doi.org/10.1016/S0890-6955(98)00076-5

M. Qingsong y H. Fukuda, 2016 «Parametric office building for daylight and energy analysis in the early design stages.,» Procedia-Social and Behavioral Sciences, vol. 216, pp. 818-828. https://doi.org/10.1016/j.sbspro.2015.12.079

C. Ramos, J. C. Augusto y D. Shapiro, 2008 «Ambient Intelligence-the Next Step for Artificial Intelligence,» IEEE Intelligent Systems , vol. 23, nº 2, pp. 15-18. https://doi.org/10.1109/MIS.2008.19

R. Roy, T. Furuhashi y P. K. Chawdhry, Advances in soft computing: Engineering design and manufacturing, Springer Science & Business Media.

S. Russell y P. Norvig, 2016 Artificial intelligence: a modern approach, Malaysia: Pearson Education Limited.

M. P. Saka y Z. W. Geem, 2013 «Mathematical and Metaheuristic Applications in Design Optimization of Steel Frame Structures: An Extensive Review,» Mathematical Problems in Engineering, vol. 2013, p. 33. https://doi.org/10.1155/2013/271031

M. P. Saka, O. Hasançebi y Z. W. Geem, 2016 «Metaheuristics in structural optimization and discussions on harmony search algorithm,» Swarm and Evolutionary Computation, vol. 28, pp. 88-97. https://doi.org/10.1016/j.swevo.2016.01.005

H. Salehia y R. Burgueño, 2018 «Emerging artificial intelligence methods in structural engineering,» Engineering Structures, vol. 171, pp. 170-189. https://doi.org/10.1016/j.engstruct.2018.05.084

K. M. Saridakis y A. J. Dentsoras, 2008 «Soft computing in engineering design - A review,» Advanced Engineering Informatics, vol. 22, nº 2, pp. 201-221. https://doi.org/10.1016/j.aei.2007.10.001

P. H. Shaikh, N. B. M. Nor, P. Nallagownden, I. Elamvazuthi y T. Ibrahim, 2014«A review on optimized control systems for building energy and comfort management of smart sustainable buildings,» Renewable and Sustainable Energy Reviews, vol. 34, pp. 409-429. https://doi.org/10.1016/j.rser.2014.03.027

D. Snoonian, 2003 «Smart buildings,» IEEE Spectrum, vol. 40, nº 8, pp. 18-23. https://doi.org/10.1109/MSPEC.2003.1222043

E. Touloupaki y T. Theodosiou, 2017 «Optimization of Building form to Minimize Energy Consumption through Parametric Modelling,» Procedia Environmental Sciences, vol. 38, pp. 509-514. https://doi.org/10.1016/j.proenv.2017.03.114

M. Turrin, P. v. Buelow y R. Stouffs, 2011 «Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms,» Advanced Engineering Informatics, vol. 25, nº 4, pp. 656-675. https://doi.org/10.1016/j.aei.2011.07.009

J. Togelius, G. Yannakakis, K. O. Stanley y C. Browne, 2011 «Search-Based Procedural Content Generation: A Taxonomy and Survey,» IEEE Transactions on Computational Intelligence and AI in Games, vol. 3, nº 3, pp. 172-186. https://doi.org/10.1109/TCIAIG.2011.2148116

T. Wortmann, C. Waibel, G. Nannicini, R. Evins, T. Schroepfer y J. Carmeliet, 2017 «Are genetic algorithms really the best choice for building energy optimization?,» de Proceedings of the Symposium on Simulation for Architecture and Urban Design, Toronto, ON, Canada.