Rafal P. Kicinger

Morphogenic Evolutionary Design: Cellular Automata Representations in Topological Structural Design

This paper provides the initial results of a study on the applications of cellular automata representations in evolutionary design of topologies of steel structural systems in tall buildings. In the paper, a brief overview of the state of the art in cellular automata and evolutionary design representations is presented. Next, morphogenic evolutionary design is introduced and illustrated by several types of cellular automata representations. Further, Emergent Designer, a unique evolutionary design tool developed at George Mason University, is briefly described. It is an integrated research and design support tool which applies models of complex adaptive systems to represent engineering systems and analyze design processes. The paper also reports the initial results of several structural design experiments conducted with Emergent Designer. The objective of the experiments was to determine feasibility of various types of cellular automata representations in topological structural optimization. Finally, initial research conclusions and recommendations for the further research are provided.

Morphogenesis and structural design: cellular automata representations of steel structures in tall buildings

This paper provides the initial results of a study on the application of generative cellular, automata-based representations in evolutionary structural design. First, recent developments in evolutionary design representations and an overview of cellular automata are presented. Next, a complex problem of topological design of steel structural systems in tall buildings is briefly described. Further, morphogenic evolutionary design is introduced and exemplified by cellular automata representations. The paper also reports the initial results of several structural design experiments whose objective was to determine feasibility of the proposed approach. Finally, initial research conclusions are provided.

Evolutionary multiobjective optimization of steel structural systems in tall buildings

This paper presents results of extensive computational experiments in which evolutionary multiobjective algorithms were used to find Pareto-optimal solutions to a complex structural design problem. In particular, Strength-Pareto Evolutionary Algorithm 2 (SPEA2) was combined with a mathematical programming method to find optimal designs of steel structural systems in tall buildings with respect to two objectives (both minimized): the total weight and the maximum horizontal displacement of a tall building. SPEA2 was employed to determine Pareto-optimal topologies of structural members (topology optimization) whose cross-sections were subsequently optimized by the mathematical programming method (sizing optimization). The paper also presents the shape of the Pareto front in this two-dimensional objective space and discusses its dependence on the buildings aspect ratio. The results reported provide both qualitative and quantitative knowledge regarding the relationship between the two objectives. They also show the trade-offs involved in the process of conceptual and detailed design of complex structural systems in tall buildings.

Multiobjective evolutionary design of steel structures in tall buildings

This paper presents initial results of a study on the application of evolutionary multiobjective optimization methods in the design of the steel structural systems of tall buildings. In the paper, a brief overview of the state-of-the-art in evolutionary multi-objective optimization in structural engineering is provided. Next, conceptual design of steel structural systems in tall buildings is overviewed and the representations of steel structural systems used in the paper are discussed. Furthermore, Emergent Designer, a unique evolutionary design tool developed at George Mason University, is briefly described. It is an integrated research and design support tool which applies models of complex adaptive systems to represent engineering systems and to analyze design processes and their results. The paper also presents the results of several multi-objective structural design experiments conducted with Emergent Designer in which steel structural systems in tall buildings were optimized with respect to their total weight and maximum deflection (two-objective minimization problem). The goal of these experiments was to determine feasibility of evolutionary multi-objective optimization of steel structural systems of tall buildings as well as to qualitatively and quantitatively compare the results with the previous findings obtained with single-objective evolutionary optimization methods. Finally, initial research conclusions are presented as well as promising research directions.

EMPIRICAL ANALYSIS OF MEMETIC ALGORITHMS FOR CONCEPTUAL DESIGN OF STEEL STRUCTURAL SYSTEMS IN TALL BUILDINGS

This paper discusses the results of extensive design experiments in which memetic algorithms were applied to optimize topologies of steel structural systems in tall buildings. In these experiments, evolutionary algorithms were employed to determine optimal configurations of structural members (topology optimization) while the optimal cross-sections of members (sizing optimization) were found using continuous/discrete optimization algorithm implemented in SODA. The impact of all major evolutionary computation parameters on the performance of memetic algorithms was investigated. Two classes of complex structural design problems were considered: design of a wind bracing system in a tall building and design of the entire steel structural system in a tall building. The total weight of the structural system was assumed as the optimality criterion with respect to which the designs were optimized while satisfying all design requirements specified by appropriate design codes.

Conceptual model of a self-organizing traffic management hazard response system

The terrorist attacks of September 11, 2001, have sparked renewed interest in developing effective policies and strategies for evacuating densely populated areas. The current analytical tools for dealing with such evacuations are sorely lacking in both theory and practice. The conceptual model presented joins the technical areas of cellular automatons, evolutionary computation, and transportation science with some recent research on infrastructure security to make significant progress in traffic management and hazard response systems. The overall goal of this research is to develop a fundamental understanding of the evolutionary and emergent behavior of transportation systems that are operating under emergency evacuation conditions. This new knowledge can be used to develop more effective operational strategies and consequently more robust hazard response systems. Furthermore, the specific research objective is to investigate the formulation and application of cellular automaton models of metropolitan transportation systems, with a focus on systems operating under emergency evacuation conditions. The basic context is evacuation of a defined urban area, such as the urban core of Washington, D.C., under terrorist attacks. The conceptual model proposes the use of evolutionary algorithms to search the space of the evacuation control strategies and determine the most successful strategies for a given urban area.

Model of a Self-Organizing Traffic Management Hazard Response System

The terrorists attacks of September 11, 2001 and afterwards have caused renewed interest in developing effective policies and strategies for evacuating densely populated areas. The large-scale evacuations cause by Hurricane Katrina and other recent hurricane events have reinforced this need. Unfortunately, the current analytical tools for dealing with such evacuations are sorely lacking, in both theory and practice. The model and its computational implementation presented in this paper attempt to close this gap and make significant progress in traffic management and hazard response time. The overall goal of this research is to develop a fundamental understanding of the evolutionary and emergent behavior of transportation systems that are operating under emergency evacuation conditions. Initial ideas on building conceptual models of evacuation scenarios utilizing cellular automata, evolutionary computation, and advanced traffic simulators were presented in the authors’ previous paper. This paper describes computational implementations of proposed conceptual models. It also discusses preliminary results of several computational experiments in which the modes were used to determine robust configurations of traffic control systems operating under emergency conditions. In these experiments, optimal evacuation strategies were sought for vehicles located within a representative urban area affected by various types of terrorist attacks.

Generative Representations in Structural Engineering

This paper proposes a new approach to representing structural system inspired by various models of complex systems. Several types of generative representations of steel structural systems are provided and empirically investigated. These representations utilize various kinds of cellular automata to generate design concepts of steel structures in tall buildings. In the paper, a brief overview of the state-of-the-art in cellular automata and generative design is presented. Next, several types of generative representations of steel structural systems in tall buildings are described. The paper also reports the results of several design experiments. They have shown that generative representations produce novel structural shaping patterns which are qualitatively different than the patterns obtained using traditionally used parameterized representations. They also significantly improve the performance of evolutionary algorithms optimizing the structural systems. Finally, research conclusions are presented and most promising paths of future research are discussed.

Intelligent agent for designing steel skeleton structures of tall buildings

The paper discusses a study on the application of intelligent agents (IAs) to conceptual designing. It provides an overview of the state-of-the-art in the areas of ontologies and IAs. Next, the system Disciple, a learning intelligent agent shell, and system Inventor 2001, evolutionary design support tool, both developed at George Mason University, are briefly presented. Further, the paper introduces the developed ontology for a class of steel skeleton structures of tall buildings. The ontology was used to build an IA for the selection of initial parent design concepts in evolutionary designing. A description of the developed agent is provided as well. Finally, examples of design concepts proposed by the agent are presented. The paper also contains conclusions and recommendations for further research.

Coevolutionary Structural Design for Robustness

This paper proposes a new nature-inspired method for achieving robustness of structural designs. In particular, biological processes of coevolution encoded in coevolutionary algorithms are proposed here to concurrently search the representation spaces of structural designs and of their loading cases. In the proposed model, coevolutionary algorithms use two populations which competitively coevolve. The first population contains structural designs, while the second one contains loading cases. The quality of each individual structural design in the first population is determined by measuring how well it performs against the loading cases from the second population. On the other hand, the goodness of each loading case depends on the number of designs it “defeated,” i.e., how many designs didn’t succeed in satisfying design requirements (e.g., maximum internal forces, maximum displacement, etc.). This approach encourages generation of robust structural designs performing well against various loading cases. The proposed coevolutionary design method is illustrated by a simple demonstration problem of a welded beam design. The paper also presents initial research conclusions and discusses the most promising paths for future research.