Interactive Collaborative Optimization – A Multidisciplinary Optimization Method Applied to the Design of Steel Building and Civil Structures

Architecture, Engineering and Construction (AEC) professionals are capable of accurately simulating the behavior of complex steel structures using modern Finite Element Analysis (FEA) software. The time required, however, to model and run such simulations using conventional processes and technologies has limited the potential of FEA to inform early-stage design decisions. Engineers typically conduct FEA on only a tiny fraction of the design space or range of options under consideration. Consequently, AEC professionals usually make design decisions with little or no information about either the performance of the chosen design with respect to alternatives or the implications of a particular design decision on downstream choices. The focus of my doctoral research is the development and application of a new Multidisciplinary Optimization (MDO) method to the design of structures in the AEC industry, including the determination of structure topology, geometry and the sizing of individual structural steel members. The method, called Interactive Collaborative Optimization (ICO), differs from existing structural optimization approaches in that (1) it is multi-level, i.e. it uses multiple optimizers, and (2) it allows the designer to concurrently consider objective and subjective criteria in choosing the best design. ICO is based upon Collaborative Optimization, a formulation developed by Braun and Kroo in the mid-1990s, which the aerospace industry now routinely uses to design aircraft. It is a bi-level hierarchical scheme, with the top level consisting of a system optimizer that operates on variables that affect multiple disciplines (e.g. topology and geometry). At the lower level, subspace optimizers operate on disciplinespecific design variables (e.g. member sizes) to satisfy design constraints within each specific discipline. To ensure that the decision variables are compatible and converge to an optimal design, the designer interactively manipulates variable sets at each level based on objective and subjective performance criteria – hence the name, Interactive Collaborative Optimization. My hypothesis is that the ICO methodology is better suited to AEC organizations than the existing single-level MDO formulations. Single-level formulations require centralized decision-making at the system-level, which can lead to bottlenecks if the number of decision variables is large. ICO allows for decisions to be distributed between project manager at the system level, but also at the subspace level by group leaders and experts in specific disciplines. Allowing experts and group leaders to make local (subspace) decisions more closely replicates existing organizational structures. Also, ICO’s potential computational advantages include a reduction in communication requirements, since local design variables no longer must be passed to the system optimizer, and the capacity to employ specialized optimizers to make local design decisions. I have already applied the member sizing subspace formulation of ICO to the design of a large stadium roof, significantly reducing design-cycle time as well as cutting steel costs by over 15% when compared to traditional methods. My future research will involve applying the complete ICO formulation, involving topology, geometry, and member sizing, to several academic and industry case-study projects. I will compare ICO to conventional practice and also to equivalent single-level formulations, on the basis of (1) process efficiency, including design cycle time and number of cycles; (2) design knowledge at key decision points; and (3) resulting product performance. Flager GQE Research Proposal 3/31/2009 2