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Design of reinforced concrete road vaults by heuristic optimization. (English) Zbl 1373.74081
Summary: This paper aims at the automatic design and cost minimization of reinforced concrete vaults used in road construction. This paper presents three heuristic optimization methods: the multi-start global best descent local search (MGB), the meta-simulated annealing (SA) and the meta-threshold acceptance (TA). Penalty functions are used for unfeasible solutions. The structure is defined by 49 discrete design variables and the objective function is the cost of the structure. All methods are applied to a vault of 12.40 m of horizontal free span, 3.00 m of vertical height of the lateral walls and 1.00 m of earth cover. This paper presents two original moves of neighborhood search and an algorithm for the calibration of SA-TA algorithms. The MGB algorithm appears to be more efficient than the SA and the TA algorithms in terms of mean results. However, the SA outperforms MGB and TA in terms of best results. The optimization method indicates savings of about 10% with respect to a traditional design.
MSC:
74P10 Optimization of other properties in solid mechanics
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[1] Perea, C.; Alcala, J.; Yepes, V.; Gonzalez-Vidosa, F.; Hospitaler, A.: Design of reinforced concrete Bridge frames by heuristic optimization, Adv eng software 39, No. 8, 676-688 (2008)
[2] Goldberg, D. E.: Genetic algorithms in search, optimization and machine learning, (1989) · Zbl 0721.68056
[3] Kirkpatrick, S.; Gelatt, C. D.; Vecchi, M. P.: Optimization by simulated annealing, Science 220, No. 4598, 671-680 (1983) · Zbl 1225.90162 · doi:10.1126/science.220.4598.671
[4] Dorigo, M.; Maniezzo, V.; Colorni, A.: The ant system: optimization by a colony of cooperating agents, IEEE trans syst man cybernet B 26, No. 1, 29-41 (1996)
[5] Kennedy J, Eberhart R. Particle swarm optimization. In: IEEE international conference on neural networks, vol. IV. Piscataway: NJ; 1995. p. 1942 – 8.
[6] Cohn, M. Z.; Dinovitzer, A. S.: Application of structural optimization, ASCE J struct eng 120, No. 2, 617-649 (1994)
[7] Coello, C. A.; Christiansen, A. D.; Santos, F.: A simple genetic algorithm for the design of reinforced concrete beams, Eng comput 13, 185-196 (1997)
[8] Leite, J. P. B.; Topping, B. H. V.: Improved genetic operators for structural optimization, Adv eng soft 29, No. 7 – 9, 529-562 (1998)
[9] Kousmousis, V. K.; Arsenis, J.: Genetic algorithms in optimal detailed design of reinforced concrete members, Comput-aid civil infrastruct eng 13, 43-52 (1998)
[10] Rafiq, M. Y.; Southcombe, C.: Genetic algorithms in optimal design and detailing of reinforced concrete columns supported by a declarative approach for capacity checking, Comput struct 69, No. 4, 443-457 (1998) · Zbl 0968.74567 · doi:10.1016/S0045-7949(98)00108-4
[11] Rajeev, S.; Krisnamoorthy, C. S.: Genetic algorithm-based methodology for design optimization of reinforced concrete frames, Comput-aided civil infrastruct eng 13, 63-74 (1998)
[12] Hrstka, O.; Kucerova, A.; Leps, M.; Zeman, J.: A competitive comparison of different types of evolutionary algorithms, Comput struct 81, 1979-1990 (2003)
[13] Leps, M.; Sejnoha, M.: New approach to optimization of reinforced concrete beams, Comput struct 81, No. 18 – 19, 1957-1966 (2003)
[14] Lee, C.; Ahn, J.: Flexural design reinforced concrete frames by genetic algorithm, ASCE J struct eng 129, No. 6, 762-774 (2003)
[15] Camp, C. V.; Pezeshk, S.; Hansson, H.: Flexural design reinforced concrete frames using a genetic algorithm, ASCE J struct eng 129, No. 1, 105-115 (2003)
[16] Govindaraj, V.; Ramasamy, J. V.: Optimum detailed design of reinforced concrete continuous beams using genetic algorithms, Comput struct 84, No. 1 – 2, 34-48 (2005)
[17] Srinivas, V.; Ramanjaneyulu, K.: An integrated approach for optimum design of Bridge decks using genetic algorithms and artificial neural networks, Adv eng softw 38, No. 7, 475-487 (2007)
[18] Sahab, M. G.; Ashour, A. F.; Toporov, V. V.: Cost optimization of reinforced concrete flat slab buildings, Eng struct 27, No. 3, 313-322 (2008)
[19] Barakat, S. A.; Altoubat, S.: Application of evolutionary global optimization techniques in the design of RC water tanks, Eng struct 31, No. 2, 332-334 (2009)
[20] Yepes, V.; Alcala, J.; Perea, C.; Gonzalez-Vidosa, F.: A parametric study of Earth-retaining walls by simulated annealing, Eng struct 30, No. 3, 821-830 (2008)
[21] Paya, I.; Yepes, V.; Gonzalez-Vidosa, F.; Hospitaler, A.: Multiobjective optimization of concrete frames by simulated annealing, Comput-aid civil infrastruct eng 23, No. 8, 596-610 (2008)
[22] Paya-Zaforteza, I.; Yepes, V.; Hospitaler, A.; Gonzalez-Vidosa, F.: CO2-efficient design of reinforced concrete building frames, Eng struct 31, 1501-1508 (2009)
[23] Martinez, F. J.; Gonzalez-Vidosa, F.; Hospitaler, A.; Yepes, V.: Heuristic optimization of RC Bridge piers with rectangular hollow sections, Comput struct 88, 375-386 (2010)
[24] Fomento, M.: IAP-98: code about the actions to be considered for the design of road bridges, (1998)
[25] Fomento, M.: EHE-08: code of structural concrete, (2008)
[26] Carbonell A, Yepes V, Gonzalez-Vidosa F. Heuristic optimization of reinforced concrete vault underpasses. In: Papadrakakis M, Topping BHV, editors. Proceedings or the sixth international conference on engineering computational technology, Civil-Comp Press, Stirlingshire, UK, paper #85; 2008. doi:10.4203/ccp.8985.
[27] CEN. Eurocode 2. Design of concrete structures. Part 1-1: general rules and rules for buildings. Brussells: CEN; 1991.
[28] Carbonell A. Heuristic optimization of reinforced concrete vaults for subways (in Spanish). Doctoral thesis, Construction Engineering Dept., Universidad Politécnica de Valencia; 2009.
[29] Bitner, J. R.; Ehrlich, G.; Reingold, E. M.: Efficient generation of the binary reflected gray code and its applications, Commun ACM 19, No. 9, 517-521 (1976) · Zbl 0333.94006 · doi:10.1145/360336.360343
[30] Eiben, A. E.; Hintering, R.; Michalewicz, Z.: Parameter control in evolutionary algorithms, IEEE trans evolut comput 3, No. 2, 124-141 (1999)
[31] Grefenstette, J. J.: Optimisation of control parameters for genetic algorithms, IEEE trans syst man cyber 16, No. 1, 122-128 (1986)
[32] Dueck, G.; Scheuer, T.: Threshold accepting: a general purpose optimization algorithm superior to simulated annealing, J comput phys 90, 161-175 (1990) · Zbl 0707.65039 · doi:10.1016/0021-9991(90)90201-B
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