Abstract
This paper focuses on discrete sizing optimization of frame structures using commercial profile catalogs. The optimization problem is formulated as a mixed-integer linear programming (MILP) problem by including the equations of structural analysis as constraints. The internal forces of the members are taken as continuous state variables. Binary variables are used for choosing the member profiles from a catalog. Both the displacement and stress constraints are formulated such that for each member limit values can be imposed at predefined locations along the member. A valuable feature of the formulation, lacking in most contemporary approaches, is that global optimality of the solution is guaranteed by solving the MILP using branch-and-bound techniques. The method is applied to three design problems: a portal frame, a two-story frame with three load cases and a multiple-bay multiple-story frame. Performance profiles are determined to compare the MILP reformulation method with a genetic algorithm.
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References
Arora J (2002) Methods for discrete variable structural optimization. In: Burns SA (ed) Recent Advances in Optimal Structural Design, pages 1–40. ASCE
Arora JS, Huang M-W (1996) Discrete structural optimization with commercially available sections. Structural Engineering/Earthquake Engineering 13:93–110
Arora JS, Wang Q (2005) Review of formulations for structural and mechanical system optimization. Struct Multidiscip Optim 30(4):251–272
Camp C, Pezeshk S, Guozhong C (1998) Optimized design of two-dimensional structures using a genetic algorithm. J Struct Eng 124(5):551–559
Camp CV, Bichon BJ, Stovall SP (2005) Design of steel frames using ant colony optimization. J Struct Eng 131(7):369–379
Carbas S (2016) Design optimization of steel frames using an enhanced firefly algorithm, Engineering Optimization, pp. 1–19
Chai S, Sun HC (1996) A relative difference quotient algorithm for discrete optimization. Struct Optim 12(1):46–56
Dolan ED, Moré JJ (2002) Benchmarking optimization software with performance profiles. Math Program 91(2):201–213
Farkas J (2005) Structural optimization as a harmony of design, fabrication and economy. Struct Multidiscip Optim 30:66–75
Faustino AM, Júdice JJ, Ribeiro IM, Neves AS (2006) An integer programming model for truss topology optimization. Investigação Operacional 26:111–127
Gandomi AH, Yang XS, Alavi AH (2011) Mixed variable structural optimization using firefly algorithm. Comput Struct 89(23):2325–2336
Gero MBP, Garcia AB, Diaz JJC (2006) Design optimization of 3d steel structures: genetic algorithms vs. classical techniques. J Constr Steel Res 62(12):1303–1309
Ghattas O, Grossmann IE (1991) MINLP and MILP strategies for discrete sizing structural optimization problems. In: Ural O, Wang TL (eds) Proceedings of the 10th Conference on Electronic Computation, pages 197–204. ASCE
Grossmann IE, Voudouris VT, Ghattas O (1992) Mixed-integer linear programming reformulations for some nonlinear discrete design optimization problems. In: Floudas CA, Pardalos PM (eds) Recent Advances in Global Optimization, pages 478–512. Princeton University Press
Gurobi Optimization Inc (2015) Gurobi optimizer reference manual
Hirota M, Kanno Y (2015) Optimal design of periodic frame structures with negative thermal expansion via mixed integer programming. Optim Eng 16(4):767–809
Huang M-W, Arora JS (1997) Optimal design of steel structures using standard sections. Struct Optim 14:24–35
Jivotovski G (2000) A gradient based heuristic algorithm and its application to discrete optimization of bar structures. Struct Multidiscip Optim 19(3):237–248
Juang DS, Chang WT (2006) A revised discrete lagrangian-based search algorithm for the optimal design of skeletal structures using available sections. Struct Multidiscip Optim 31(3):201– 210
Kanno Y, Guo X (2010) A mixed integer programming for robust truss topology optimization with stress constraints. Int J Numer Methods Eng 83(13):1675–1699
Kassimali A (1999) Matrix Analysis of Structures. Brooks/Cole Publishing Company, London
Kaveh A, Talatahari S (2010) An improved ant colony optimization for the design of planar steel frames. Eng Struct 32(3):864–873
Kazemzadeh Azad S, Hasancebi O (2015) Computationally efficient discrete sizing of steel frames via guided stochastic search heuristic. Comput Struct 156:12–28
Kripakaran P, Brian H, Abhinav G (2010) A genetic algorithm for design of moment-resisting steel frames. Struct Multidiscip Optim 32(3):559–574
Kureta R, Kanno Y (2014) A mixed integer programming approach to designing periodic frame structures with negative poissons ratio. Optim Eng 15(3):773–800
Mela K (2014) Resolving issues with member buckling in truss topology optimization using a mixed variable approach. Struct Multidiscip Optim 50(6):1037–1049
Nemhauser G, Wolsey L (1999) Integer and Combinatorial Optimization. Wiley
Rajeev S, Krishnamoorthy CS (1992) Discrete optimization of structures using genetic algorithms. J Struct Eng 118(5):1233– 1250
Rasmussen MH, Stolpe M (2008) Global optimization of discrete truss topology design problems using a parallel cut-and-branch method. Comput Struct 86(13):1527–1538
Rojas-Labanda S, Stolpe M (2015) Benchmarking optimization solvers for structural topology optimization. Struct Multidiscip Optim 52(3):527–547
Saka MP (2009) Optimum design of steel sway frames to bs5950 using harmony search algorithm. J Constr Steel Res 65(1):36– 43
Schevenels M, McGinn S, Rolvink A, Coenders J (2014) An optimality criteria based method for discrete design optimization taking into account buildability constraints. Struct Multidiscip Optim 50(5):755–774
Stolpe M (2007) On the refomulation of topology optimization problems as linear or convex quadratic mixed 0-1 programs. Optim Eng 8:163–192
Stolpe M, Svanberg K (2003) Modelling topology optimization problems as linear mixed 0-1 programs. Int J Numer Methods Eng 57:723–739
Talatahari S, Gandomi A H, Yang X-S, Deb S (2015) Optimum design of frame structures using the eagle strategy with differential evolution. Eng Struct 91:16–25
Thanedar PB, Vanderplaats GN (1995) Survey of discrete variable optimization for structural design. J Struct Eng 121:301–306
Togan V (2012) Design of planar steel frames using teaching-learning based optimization. Eng Struct 225 (8):34
Van Mellaert R, Schevenels M (2015) Global size optimization of statically determinate trusses considering displacement, member, and joint constraints. J Struct Eng 142(2):04015120
Venter G, Sobieszczanski-Sobieski J (2003) Particle swarm optimization. AIAA J 41(8):1583–1589
Wang Q, Arora JS (2006) Alternative formulations for structural optimization: An evaluation using frames. J Struct Eng 132(12):1880–1189
Wolsey L (1998) Integer Programming. Wiley
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Appendices
Appendix A: Mixed-integer linear programming problem
1.1 A.1 Equilibrium equations
The nodal equilibrium is imposed by the equality constraints of (5). In this equation, B i is a 6 × n dof binary location matrix that maps the system degrees of freedom to the element degrees of freedom, T i is a 6 × 6 transformation matrix that accounts for the orientation of the element (Kassimali 1999), and f is the n dof × 1 nodal load vector. Element loads are taken into account as equivalent nodal loads in the nodal load vector f. R i is a 6 × 3 matrix giving the relation between the six member end forces as shown in Fig. 1, and the three independent force variables q ij (see (1)) as follows:
1.2 A.2 Member stiffness relations
In addition to nodal equilibrium, the material law and compatibility conditions are needed in structural analysis. For trusses, Hooke’s law and compatibility conditions can be written as a single equation, because the normal force is the only stress resultant appearing in the members (Rasmussen and Stolpe 2008). As frame members have three (6 in 3D) stress resultants in each node, altogether six (12 in 3D) force-displacement relations are needed. Thus, the relation between the member end forces and the nodal displacements can be written as:
where the matrix K ij assembles the first, third and sixth row of the element stiffness matrix:
where E is the Young’s modulus of the material, L i is the length of member i, and A ij and I ij are the section area and second moment of area of profile j for member i, respectively.
Equation (41) ensures that the force variables q ij become zero when profile j is not selected for member i (y ij = 0) and q ij = K ij T i B i u when profile j is selected for member i (y ij = 1).
In a regular finite element analysis, the global stiffness matrix K is assembled by replacing q ij in (5) with the expression given by (41). The resulting equilibrium equation can not be reformulated as a linear system of equations in terms of the design variables since the global stiffness matrix depends on the binary decision variables. Therefore, the linear nodal equilibrium, (5), and the member stiffness relation, (41), are adopted as separate constraints.
The member stiffness relation in (41) is nonlinear in terms of the design variables but it can be equivalently reformulated as a set of linear inequality constraints by introducing artificial upper and lower bounds (Rasmussen and Stolpe 2008) of (6). In this equation, the force variables become equal to q ij = K ij T i B i u when profile j is selected for member i (y ij = 1). When profile j is not selected for member i (y ij = 0), the force variables do not become zero but are bounded by \(\mathbf {K}_{ij}\mathbf {T}_{i}\mathbf {B}_{i}\mathbf {u}-\bar {\mathbf {q}}^{\prime }_{ij}\leqslant \mathbf {q}_{ij}\leqslant \mathbf {K}_{ij}\mathbf {T}_{i}\mathbf {B}_{i}\mathbf {u}-{\mathbf {\underline {q}}^{\prime }_{ij}}\). In order to ensure that the force variables become zero when profile j is not selected for member i, additional constraints given by (7) are introduced.
The artificial upper and lower bounds \(\bar {\mathbf {q}}^{\prime }_{ij}\) and \({\mathbf {\underline {q}}^{\prime }_{ij}}\) ensure that, when profile j is not selected for member i, the nodal displacements are not bounded by (6). Each element k of the artificial bound vectors is calculated as follows (Stolpe and Svanberg 2003):
where k r, ij represents row \(r\in \left \{1,3,6 \right \}\) of the element stiffness matrix K ij , and \(\mathbf {\underline {\mathbf {u}}}\) and \(\mathbf {\overline {\mathbf {u}}}\) are the prescribed minimum and maximum allowed nodal displacements, respectively. Equations. (42) and (43) are linear optimization problems with bound constraints, that can be solved without effort (Stolpe and Svanberg 2003).
1.3 A.3 Displacement vectors
1.4 A.4 Stress vectors
Appendix B: Detailed results for the portal frame
Appendix C: Detailed results for the three-bay three-story frame
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Van Mellaert, R., Mela, K., Tiainen, T. et al. Mixed-integer linear programming approach for global discrete sizing optimization of frame structures. Struct Multidisc Optim 57, 579–593 (2018). https://doi.org/10.1007/s00158-017-1770-9
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DOI: https://doi.org/10.1007/s00158-017-1770-9