Cost Optimization of Structures Cost Optimization of Structures Fuzzy Logic, Genetic Algorithms, and Parallel Computing Hojjat Adeli The Ohio State University, USA Kamal C. 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Dedicated to Djafar Adeli and Mokarrameh Soofi Vikram, Lona, and Sharmistha Sarma Contents Preface xi Acknowledgments xiii About the Authors xv 1 Introduction 1 1.1 The Case for Cost Optimization 1 1.2 Cost Optimization of Concrete Structures 2 1.2.1 Concrete Beams and Slabs 3 1.2.2 Concrete Columns 11 1.2.3 Concrete Frame Structures 12 1.2.4 Bridge Structures 14 1.2.5 Water Tanks 16 1.2.6 Folded Plates and Shear Walls 17 1.2.7 Concrete Pipes 17 1.2.8 Concrete Tensile Members 17 1.2.9 Cost Optimization Using the Reliability Theory 18 1.2.10 Concluding Comments 20 1.3 Cost Optimization of Steel Structures 20 1.3.1 Deterministic Cost Optimization 20 1.3.2 Cost Optimization Using the Reliability Theory 31 1.3.3 Fuzzy Optimization 33 1.3.4 Concluding Comments 35 2 Evolutionary Computing and the Genetic Algorithm 37 2.1 Overview and Basic Operations 37 2.2 Coding and Decoding 39 viii Contents 2.3 Basic Operations in Genetic Algorithms 40 2.4 GA with the Penalty Function Method 43 2.4.1 Problem Formulation for Axial Force (Truss) Structures 43 2.4.2 Genetic Algorithm with the Penalty Function Method 45 2.5 Augmented Lagrangian Method 47 2.6 GA with the Augmented Lagrangian Method 48 2.6.1 Problem Formulation for Axial Force (Truss) Structures 48 2.6.2 Genetic Algorithm with the Augmented Lagrangian Method 49 3 Cost Optimization of Composite Floors 53 3.1 Introduction 53 3.2 Minimum Cost Design of Composite Beams 54 3.2.1 Cost Function 54 3.2.2 Constraints 55 3.2.3 Problem Formulation as a Mixed Integer–Discrete Nonlinear Programming Problem 61 3.3 Solution by the Floating-Point Genetic Algorithm 62 3.3.1 Binary Versus Floating-Point GA 62 3.3.2 Crossover Operation for the Floating-Point GA 62 3.3.3 Mutation Operation for the Floating-Point GA 63 3.3.4 Floating-Point GA for Cost Optimization of Composite Floors 63 3.4 Solution by the Neural Dynamics Method 65 3.5 Counter Propagation Neural (CPN) Network for Function Approximations 68 3.6 Examples 71 3.6.1 Example 1 71 3.6.2 Example 2 72 4 FuzzyGeneticAlgorithmforOptimizationofSteelStructures 77 4.1 Introduction 77 4.2 Fuzzy Set Theory and Structural Optimization 79 4.3 Minimum Weight Design of Axially Loaded Space Structures 82 4.4 Fuzzy Membership Functions 85 4.5 Fuzzy Augmented Lagrangian Genetic Algorithm 87 Contents ix 4.6 Implementation and Examples 92 4.6.1 Example 1 93 4.6.2 Example 2 93 4.7 Conclusion 98 5 Fuzzy Discrete Multi-criteria Cost Optimization of Steel Structures 101 5.1 Cost of a Steel Structure 101 5.2 Primary Contributing Factors to the Cost of a Steel Structure 102 5.3 Fuzzy Discrete Multi-criteria Cost Optimization 105 5.4 Membership Functions 110 5.4.1 Membership Function for Minimum Cost 110 5.4.2 Membership Function for Minimum Weight 110 5.4.3 Membership Function for Minimum Number of Section Types 110 5.5 Fuzzy Membership Functions for Criteria with Unequal Importance 112 5.6 Pareto Optimality 112 5.7 Selection of Commercially Available Discrete Shapes 114 5.8 Implementation and a Parametric Study 117 5.9 Application to High-Rise Steel Structures 118 5.9.1 Example 1 118 5.9.2 Example 2 119 5.10 Concluding Comments 123 6 Parallel Computing 125 6.1 Multiprocessor Computing Environment 125 6.2 Parallel Processing Implementation Environment 128 6.2.1 OpenMP Data Parallel Application Programming Interface (API) 128 6.2.2 Message Passing Interface (MPI) 130 6.3 Performance Optimization of Parallel Programs 130 7 Parallel Fuzzy Genetic Algorithms for Cost Optimization of Large Steel Structures 133 7.1 Genetic Algorithm and Parallel Processing 133 7.2 Cost Optimization of Moment-Resisting Steel Space Structures 135
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