Inductive machine learning of optimal modular structures: Estimating solutions using support vector machines

Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM

Volumn 21, Issue 4, 2007, Pages 351-366

  Hanna, Sean ^a,b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Machine learning; Optimization; Structures; Support vector machines

Indexed keywords

ALGORITHMS; COMPOSITE STRUCTURES; ITERATIVE METHODS; MODULAR CONSTRUCTION; STRUCTURAL OPTIMIZATION;

GRADIENT DESCENT OPTIMIZATION; PHYSICS-BASED MODEL;

SUPPORT VECTOR MACHINES;

EID: 34548795310     PISSN: 08900604     EISSN: 14691760     Source Type: Journal    
DOI: 10.1017/S0890060407000327     Document Type: Conference Paper

References (32)

1. Adeli, H., & Cheng, N. (1993). Integrated genetic algorithm for optimisation of space structures. Journal of Aerospace Engineering 6(4), 315-328.
2. Arciszewski, T., & Ziarko, W. (1.990). Inductive learning incivil engineering: rough sets approach. Microcomputers in Civil Engineering 5(1), 19-28.
3. Astley, R.J., Harrington, J.J., & Stol, K.A. (1997). Mechanical modelling of wood microstructure, an engineering approach. IPENZ Transactions 24(1), 21-29.
4. Bendsøe, M.P., & Kikuchi, N. (1988). Generating optimal topologies in structural design using a homogenization method. Computer Methods in Applied Mechanics and Engineering 71(2), 197-224.
5. Bendsøe, M.P., & Sigmund, O. (1999). Material interpolation schemes in topology optimisation. Archives of Applied Mechanics 69, 635-654.
6. Bendsøe, M.P., & Sigmund, O. (2003). Topology Optimisation: Theory, Methods and Applications. Berlin: Springer.
7. Chen, Y.M. (2002). Nodal based evolutionary structural optimisation methods. PhD Thesis. University of Southhampton.
8. Duda, R.O., Hart, P.E., & Stork, D.G. (2001). Pattern Classification. New York: Wiley.
9. Duffy, A.H.B. (1997). The "what" and "how" of learning in design. IEEE Expert: Intelligent Systems and Their Applications 12(3), 71.-76.
10. Hanna, S. & Haroun Mahdavi, S. (2004). Modularity and flexibility at the small scale: evolving continuous material variation with stereolithography. In Fabrication: Examining the Digital Practice of Architecture (Beesley, P., Cheng, W., & Williamson, R., Eds.), pp. 76-87. Toronto: University of Waterloo School of Architecture Press.
11. Hanna, S., & Haroun Mahdavi, S. (2006). Inductive machine learning of microstructures: estimating a finite element optimisation using support vector machines. In Design Computing and Cognition '06 (Gero, J.S., Ed.), pp. 563-582. Dordrecht: Springer.
12. Haroun Mahdavi, S., & Hanna, S. (2003). An evolutionary approach to microstructure optimisation, of stereolithographic models. Proc. CEC2003, Congr. Evolutionary Computation, pp. 723-730, Canberra, Australia.
13. Haroun Mahdavi, S., & Hanna, S. (2004). Optimising continuous microstructures: a comparison of gradient-based and stochastic methods. Proc. SCIS & ISIS 2004, Joint 2nd Int. Conf. Sofi Computing and Intelligent Systems and 5th Int. Symp. Advanced Intelligent Systems, p. WE-7-5, Yokohama, Japan.
14. Kicinger, R., Arciszewski, T., & De Jong, K. (2005). Parameterized versus generative representations in structural design: an empirical comparison. Proc. GECCO '05, pp. 2007-2014.
15. Meiler, J., & Baker, D. (2003). Coupled prediction of protein secondary and tertiary structure. Proceedings of the National Academy of Sciences of the United States of America 700(21), 12105-12110.
16. Murawski, K., Arciszewski, T., & De Jong, K, (2000). Evolutionary computation in structural, design. Engineering with Computers 16(3-4), 275-286.
17. Murdoch, T., & Ball, N. (1996). Machine learning in configuration design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10(2), 101-113.
18. Neocleous, C.C., & Schizas, C.N. (1995). Artificial, neural networks in marine propeller design. Proc. ICNN'95 Int. Conf Neural Networks, Vol. 2, pp. 1.098-1102 New York: IEEE Computer Society Press.
19. Pezeshk, S., & Camp, C.V. (2002). State of the art on the use of genetic algorithms in design of steel structures. In. Recent Advances in Optimal Structural Design. New York: ASCE.
20. Ping, Y. ( 1996). Development of genetic algorithm based approach for structural optimisation. PhD Thesis. Singapore: Nanyang Technological University.
21. Quinlan, J.R., (1.993). C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.
22. Reich, Y. (1997). Machine learning techniques for civil engineering problems. Computer-Aided Civil and Infrastructure Engineering 1.2(4), 295-310.
23. Reich, Y., & Barai, S.V. (1999). Evaluating machine learning models for engineering problems. Artificial Intelligence in Engineering 1.3, 257-272.
24. Schoenhauer, M. (1996). Shape representations and evolution schemes. Proc. 5th Annual Conf. Evolutionary Programming, pp. 121-129.
25. Schwabacher, M., Ellman, T., & Hirsh, H, (1998). Learning to set up numerical, optimisations of engineering designs. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12(2), 173-192.
26. Shea, K., & Cagan, J. (1998). Topology design of truss structures by shape annealing. In Proc. DETC98: 1998ASMEDesign Engineering Technical Conf, pp. 1-11 New York: ASME.
27. Sischka, J., Hensel, M., Menges, A., & Weinstock, M. (2004). Manufacturing complexity. Architectural Design 74(3), 72-79.
28. Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., & Vandewalle, J. (2002). Least Squares Support Vector Machines. Singapore: World Scientific.
29. Szczepanik, W., Arciszewski, T., & Wnek, J. (1996). Empirical performance comparison of selective and constructive induction. Engineering Applications of Artificial Intelligence 9(6), 627-637.
30. Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York: Springer-Verlag.
31. Von Buelow, P. (2002). Using evolutionary algorithms to aid designers of archictural. structures. In Creative Evolutionary Systems (Bentley, P.J., & Come, D.W., Eds.). San Francisco, CA: Morgan Kaufmann.
32. Wang, L.H., Liu, J., Li, Y.F., & Zhou, H.B. (2004). Predicting protein secondary structure by a support vector machine based on a new coding scheme. Genome Informatics 15(2), 181-190.