zbMATH — the first resource for mathematics

Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
Optimal design of structures subjected to time history loading by swarm intelligence and an advanced metamodel. (English) Zbl 1229.74114
Summary: This paper proposes a new metamodeling framework that reduces the computational burden of the structural optimization against the time history loading. In order to achieve this, two strategies are adopted. In the first strategy, a novel metamodel consisting of adaptive neuro-fuzzy inference system (ANFIS), subtractive algorithm (SA), self organizing map (SOM) and a set of radial basis function (RBF) networks is proposed to accurately predict the time history responses of structures. The metamodel proposed is called fuzzy self-organizing radial basis function (FSORBF) networks. In this study, the most influential natural periods on the dynamic behavior of structures are treated as the inputs of the neural networks. In order to find the most influential natural periods from all the involved ones, ANFIS is employed. To train the FSORBF, the input-output samples are classified by a hybrid algorithm consisting of SA and SOM clusterings, and then a RBF network is trained for each cluster by using the data located. In the second strategy, particle swarm optimization (PSO) is employed to find the optimum design. Two building frame examples are presented to illustrate the effectiveness and practicality of the proposed methodology. A plane steel shear frame and a realistic steel space frame are designed for optimal weight using exact and approximate time history analyses. The numerical results demonstrate the efficiency and computational advantages of the proposed methodology.

74P10Optimization of other properties (solid mechanics)
ANFIS; Matlab
Full Text: DOI
[1] Lagaros, N. D.; Fragiadakis, M.; Papadrakakis, M.; Tsompanakis, Y.: Structural optimization: a tool for evaluating dynamic design procedures, Engineering structures 28, 1623-1633 (2006)
[2] Zou, X. K.; Chan, C. M.: An optimal resizing technique for dynamic drift design of concrete buildings subjected to response spectrum and time history loadings, Computers and structures 83, 1689-1704 (2005)
[3] Kocer, F. Y.; Arora, J. S.: Optimal design of H-frame transmission poles for earthquake loading, ASCE journal of structural engineering 125, 1299-1308 (1999)
[4] Kocer, F. Y.; Arora, J. S.: Optimal design of latticed towers subjected to earthquake loading, ASCE journal of structural engineering 128, 197-204 (2002)
[5] F.Y. Cheng, D. Li, J. Ger, Multiobjective optimization of dynamic structures, in: M. Elgaaly (Ed.), ASCE Structures 2000 Conference Proceedings, 2000.
[6] Salajegheh, E.; Heidari, A.; Saryazdi, S.: Optimum design of structures against earthquake by discrete wavelet transform, International journal for numerical methods in engineering 62, 2178-2192 (2005) · Zbl 1118.74340 · doi:10.1002/nme.1279
[7] Salajegheh, E.; Heidari, A.: Optimum design of structures against earthquake by wavelet neural network and filter banks, Earthquake engineering and structural dynamics 34, 67-82 (2005)
[8] Adeli, H.; Jiang, X.: Dynamic fuzzy wavelet neural network model for structural system identification, ASCE journal of structural engineering 132, 102-111 (2006)
[9] Wang, Q. F.: Numerical approximation of optimal control for distributed diffusion Hopfield neural networks, International journal for numerical methods in engineering 69, 443-468 (2007) · Zbl 1194.92006 · doi:10.1002/nme.1769
[10] Jang, J. S. R.: ANFIS: adaptive-network-based fuzzy inference systems, IEEE transactions on systems man and cybernetics 23, 665-685 (1993)
[11] Chiu, S. L.: Fuzzy model identification based on cluster estimation, Journal of intelligent and fuzzy systems 2, 267-278 (1994)
[12] Kohonen, T.: Self-organization and associative memory, (1987) · Zbl 0528.68062
[13] Wasserman, P. D.: Advanced methods in neural computing, (1993) · Zbl 0842.68062
[14] Gholizadeh, S.; Salajegheh, E.; Torkzadeh, P.: Structural optimization with frequency constraints by genetic algorithm using wavelet radial basis function neural network, Journal of sound and vibration 312, 316-331 (2008)
[15] The Language of Technical Computing. MATLAB, Math Works Inc., 2006.
[16] J. Kennedy, The particle swarm: social adaptation of knowledge, in: Proceedings of the International Conference on Evolutionary Computation. Piscataway, NJ: IEEE (1977), pp. 303 -- 308.
[17] American Institute of Steel Construction. Manual of Steel Construction-allowable Stress Design, 9th ed., Chicago, 1995.
[18] Arora, J. S.: Optimization of structures subjected to dynamic loads, Structural dynamic systems computational techniques and optimization (1999)
[19] Kang, B. S.; Park, G. J.; Arora, J. S.: A review of optimization of structures subjected to transient loads, Structural and multidisciplinary optimization 31, 81-95 (2006) · Zbl 1245.74057
[20] R.C. Eberhart, J. Kennedy, A new optimizer using particles swarm theory, in: Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995, pp. 39 -- 43.
[21] J. Kennedy, R.C. Eberhart, Particle swarm optimization, in: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, 1995, pp. 1942 -- 1945.
[22] V. Bergh, A. Engelbrecht, Using neighbourhood with the guaranteed convergence PSO, in: 2003 IEEE Swarm Intelligence Symposium, USA, 2003, pp. 235 -- 242.
[23] Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, in: Proceeding IEEE International Conference on Evolutionary Computation, 1997, pp. 303 -- 308.
[24] Perez, R. E.; Behdinan, K.: Particle swarm approach for structural design optimization, Computers and structures 85, 1579-1588 (2007)
[25] Kathiravan, R.; Ganguli, R.: Strength design of composite beam using gradient and particle swarm optimization, Composite structures 81, 471-479 (2007)
[26] Salajegheh, E.; Gholizadeh, S.; Khatibinia, M.: Optimal design of structures for earthquake loads by a hybrid RBF -- BPSO method, Earthquake engineering and engineering vibration 7, 13-24 (2008)
[27] Rafiq, M. Y.; Bugmann, G.; Easterbrook, D. J.: Neural network design for engineering applications, Computers and structures 79, 1541-1552 (2001)
[28] Zhang, A.; Zhang, L.: RBF neural networks for the prediction of building interference effects, Computers and structures 82, 2333-2339 (2004)
[29] Deng, J.: Structural reliability analysis for implicit performance function using radial basis function network, International journal of solids and structures 43, 3255-3291 (2006) · Zbl 1121.74489 · doi:10.1016/j.ijsolstr.2005.05.055
[30] Roy, N.; Ganguli, R.: Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring, Applied soft computing 6, 154-169 (2006)
[31] Topcu, I. B.; Saridemir, M.: Prediction of rubberized concrete properties using artificial neural network and fuzzy logic, Construction and building material 22, 532-540 (2008)
[32] Mamdani, E. H.; Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller, International journal of man -- machine studies 7, 1-13 (1975) · Zbl 0301.68076 · doi:10.1016/S0020-7373(75)80002-2
[33] Sugeno, M.: Industrial applications of fuzzy control, (1985) · Zbl 0586.93053
[34] Paiva, R. P.; Dourado, A.: Structure and parameter learning of neuro-fuzzy systems: a methodology and a comparative study, Journal of intelligent and fuzzy systems 11, 147-161 (2001)
[35] Jiang, X.; Mahadevan, S.; Adeli, H.: Bayesian wavelet packet denoising for structural system identification, Structural control and health monitoring 14, 333-356 (2006)
[36] Martinet, R. K.; Morlet, J.; Grossmann, A.: Analysis of sound patterns through wavelet transforms, International journal of pattern recognition and artificial intelligence 1, 273-302 (1987)