Thesis (M.S. in Mechanical Engineering)--Naval Postgraduate School, March 2005. / Thesis Advisor(s): Fotis Papoulias, Roman B. Statnikov. Includes bibliographical references (p. 241). Also available online.

In this thesis, the optimum design algorithm is presented for reinforced concrete special moment frames. The objective function is considered as the total cost of reinforced concrete frame which includes the cost of concrete, formwork and reinforcing steel bars. The cost of any component is inclusive of material, fabrication and labor. The design variables in beams are selected as the width and the depth of beams in each span, the diameter and the number of longitudinal reinforcement bars along the span and supports. In columns the width and the depth of the column section, the number and the diameter of bars in x and y directions are selected as design variables. The column section database is prepared which includes the width and height of column section, the diameter and the number of reinforcing bars in the column section is constructed. This database is used by the design algorithm to select appropriate sections for the columns of the frame under consideration. The design constraints are implemented from ACI 318-05 which covers the flexural and shear strength, serviceability, the minimum and maximum steel percentage for flexural and shear reinforcement, the spacing requirements for the reinforcing bars and the upper and lower bound requirements for the concrete sections. The optimum design problem formulated according to ACI 318-05 provisions with the design variables mentioned above turns out to be a combinatorial optimization problem. The solution of the design problem is obtained by using the harmony search algorithm (HS) which is one of the recent additions to meta-heuristic optimization techniques which are widely used in obtaining the solution of combinatorial optimization problems. The HS algorithm is quite simple and has few parameters to initialize and consists of simple steps which make it easy to implement. Number of design examples is presented to demonstrate the efficiency and robustness of the optimum design algorithm developed.

In the building automation domain, there are gaps among various tasks related to design engineering. As a result created system designs must be adapted to the given requirements on system functionality, which is related to increased costs and engineering effort than planned. For this reason standards are prepared to enable a coordination among these tasks by providing guidelines and unified artifacts for the design. Moreover, a huge variety of prefabricated devices offered from different manufacturers on the market for building automation that realize building automation functions by preprogrammed software components. Current methods for design creation do not consider this variety and design solution is limited to product lines of a few manufacturers and expertise of system integrators. Correspondingly, this results in design solutions of a limited quality. Thus, a great optimization potential of the quality of design solutions and coordination of tasks related to design engineering arises. For given design requirements, the existence of a high number of devices that realize required functions leads to a combinatorial explosion of design alternatives at different price and quality levels. Finding optimal design alternatives is a hard problem to which a new solution method is proposed based on heuristical approaches. By integrating problem specific knowledge into algorithms based on heuristics, a promisingly high optimization performance is achieved. Further, optimization algorithms are conceived to consider a set of flexibly defined quality criteria specified by users and achieve system design solutions of high quality. In order to realize this idea, optimization algorithms are proposed in this thesis based on goal-oriented operations that achieve a balanced convergence and exploration behavior for a search in the design space applied in different strategies. Further, a component model is proposed that enables a seamless integration of design engineering tasks according to the related standards and application of optimization algorithms.:1 Introduction 17 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.3 Goals and Use of the Thesis . . . . . . . . . . . . . . . . . . . . . 21 1.4 Solution Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . 24 2 Design Creation for Building Automation Systems 25 2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.2 Engineering of Building Automation Systems . . . . . . . . . . . 29 2.3 Network Protocols of Building Automation Systems . . . . . . . 33 2.4 Existing Solutions for Design Creation . . . . . . . . . . . . . . . 34 2.5 The Device Interoperability Problem . . . . . . . . . . . . . . . . 37 2.6 Guidelines for Planning of Room Automation Systems . . . . . . 38 2.7 Quality Requirements on BAS . . . . . . . . . . . . . . . . . . . 41 2.8 Quality Requirements on Design . . . . . . . . . . . . . . . . . . 42 2.8.1 Quality Requirements Related to Project Planning . . . . 42 2.8.2 Quality Requirements Related to Project Implementation 43 2.9 Quality Requirements on Methods . . . . . . . . . . . . . . . . . 44 2.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3 The Design Creation Task 47 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2 System Design Composition Model . . . . . . . . . . . . . . . . . 49 3.2.1 Abstract and Detailed Design Model . . . . . . . . . . . . 49 3.2.2 Mapping Model . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3 Formulation of the Problem . . . . . . . . . . . . . . . . . . . . . 53 3.3.1 Problem properties . . . . . . . . . . . . . . . . . . . . . . 54 3.3.2 Requirements on Algorithms . . . . . . . . . . . . . . . . 56 3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4 Solution Methods for Design Generation and Optimization 59 4.1 Combinatorial Optimization . . . . . . . . . . . . . . . . . . . . . 59 4.2 Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.3 Examples for Metaheuristics . . . . . . . . . . . . . . . . . . . . . 62 4.3.1 Simulated Annealing . . . . . . . . . . . . . . . . . . . . . 62 4.3.2 Tabu Search . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3.3 Ant Colony Optimization . . . . . . . . . . . . . . . . . . 65 4.3.4 Evolutionary Computation . . . . . . . . . . . . . . . . . 66 4.4 Choice of the Solver Algorithm . . . . . . . . . . . . . . . . . . . 69 4.5 Specialized Methods for Diversity Preservation . . . . . . . . . . 70 4.6 Approaches for Real World Problems . . . . . . . . . . . . . . . . 71 4.6.1 Component-Based Mapping Problems . . . . . . . . . . . 71 4.6.2 Network Design Problems . . . . . . . . . . . . . . . . . . 73 4.6.3 Comparison of Solution Methods . . . . . . . . . . . . . . 74 4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5 Automated Creation of Optimized Designs 79 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.2 Design Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.3 Component Model . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.3.1 Presumptions . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.3.2 Integration of Component Model . . . . . . . . . . . . . . 87 5.4 Design Generation . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.4.1 Component Search . . . . . . . . . . . . . . . . . . . . . . 88 5.4.2 Generation Approaches . . . . . . . . . . . . . . . . . . . 100 5.5 Design Improvement . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.5.1 Problems and Requirements . . . . . . . . . . . . . . . . . 107 5.5.2 Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 5.5.3 Application Strategies . . . . . . . . . . . . . . . . . . . . 121 5.6 Realization of the Approach . . . . . . . . . . . . . . . . . . . . . 122 5.6.1 Objective Functions . . . . . . . . . . . . . . . . . . . . . 122 5.6.2 Individual Representation . . . . . . . . . . . . . . . . . . 123 5.7 Automated Design Creation For A Building . . . . . . . . . . . . 124 5.7.1 Room Spanning Control . . . . . . . . . . . . . . . . . . . 124 5.7.2 Flexible Rooms . . . . . . . . . . . . . . . . . . . . . . . . 125 5.7.3 Technology Spanning Designs . . . . . . . . . . . . . . . . 129 5.7.4 Preferences for Mapping of Function Blocks to Devices . . 132 5.8 Further Uses and Applicability of the Approach . . . . . . . . . . 133 5.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 6 Validation and Performance Analysis 137 6.1 Validation Method . . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.2 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.3 Example Abstract Designs and Performance Tests . . . . . . . . 139 6.3.1 Criteria for Choosing Example Abstract Designs . . . . . 139 6.3.2 Example Abstract Designs . . . . . . . . . . . . . . . . . . 140 6.3.3 Performance Tests . . . . . . . . . . . . . . . . . . . . . . 142 6.3.4 Population Size P - Analysis . . . . . . . . . . . . . . . . 151 6.3.5 Cross-Over Probability pC - Analysis . . . . . . . . . . . 157 6.3.6 Mutation Probability pM - Analysis . . . . . . . . . . . . 162 6.3.7 Discussion for Optimization Results and Example Designs 168 6.3.8 Resource Consumption . . . . . . . . . . . . . . . . . . . . 171 6.3.9 Parallelism . . . . . . . . . . . . . . . . . . . . . . . . . . 172 6.4 Optimization Framework . . . . . . . . . . . . . . . . . . . . . . . 172 6.5 Framework Design . . . . . . . . . . . . . . . . . . . . . . . . . . 174 6.5.1 Components and Interfaces . . . . . . . . . . . . . . . . . 174 6.5.2 Workflow Model . . . . . . . . . . . . . . . . . . . . . . . 177 6.5.3 Optimization Control By Graphical User Interface . . . . 180 6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 7 Conclusions 185 A Appendix of Designs 189 Bibliography 201 Index 211