Emergent Engineering Design: Feasibility Study

The goal of this high-risk and high-payoff project is the development of an outline of a new method for engineering design.  The new method will be based on the synthesis of several computational mechanisms inspired by biological processes of evolution, development, and coevolution. Specifically, the research will focus on efficient methods of decomposition of complex engineering systems and on ways of making engineering designs more robust.  One of the important contributions of the proposed research will include the development of a modern computational tool supporting and enhancing design processes.

So far, nature-inspired engineering design was almost exclusively focused, with relatively few exceptions, on design optimization issues in which the principles of evolution encoded as evolutionary algorithms were used to optimize designs. The goal of this project is to show how other computational mechanisms inspired by biological processes can be efficiently used to decompose complex engineering designs and to make designs more robust. Thus, this research will build a bridge between modern biology, computer science, and engineering design.

For more information on this project, please visit the project homepage, or contact Dr. Rafal Kicinger.

Transportation Systems Security: Self-Organizing Traffic Management Hazard Response System

The 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 utilized to develop more effective operational strategies and consequently more robust hazard response systems.

The specific research objective is to investigate the formulation and application of cellular automata 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, DC under terrorist attacks. The model studies 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. The developed evacuation control strategies are subsequently simulated by CORSIM and evaluated using several measures of effectiveness.

For more information on this project, please contact Dr. Rafal Kicinger or Dr. Michael Bronzini.

Bridge Security: Intelligent Vulnerability Assessment

The goal of this project is to develop a novel method for assessing vulnerabilities of existing bridge structures which nowadays face new types of security challenges (e.g., terrorist threats). Hence, these complex engineering systems need to be subjected to a new type of analysis and assessment in which the impact of untypical loads and their combinations need to be evaluated. In order to achieve this goal, evolutionary algorithms are used to identify the worst possible threat scenarios for a given bridge. The generated threat scenarios are translated into loads and load combinations which are applied to a bridge structure and subsequently evaluated using a structural analysis package.

The initial part of this project focused on simplified models of steel truss bridges and threat scenarios involving blasts occurring at several locations along the bridge structure. The blasts damage or destroy steel members and subsequently change the topology of a structural system. This in turn may lead to large deformations of a bridge and initiation of its collapse mechanisms. The intelligent evolutionary search identifies most vulnerable members of a given bridge structure and in this way helps decision makers to select most critical part of the structure which need to be protected.

For more information on this project, please contact Dr. Rafal Kicinger or Dr. Tomasz Arciszewski.

Capital Budgeting and Resource Allocation Methodology for Critical Infrastructure Protection

This project was sponsored by the National Capital Region (NCR). Its goal was to develop a methodology which would support decision-makers in making choices on optimal resource allocation for critical infrastructure protection. The project established a set of metrics that can be used to measure the benefits associated with the programs to mitigate identified vulnerabilities of critical infrastructure systems. It also specified models that can be used to estimate the values of these metrics and their distribution across sectors and stakeholders in the Region. In order to achieve it, decision analytic methods were employed to quantify their relative importance and to prioritize alternative mitigation programs.

As a part of the deliverables of the project a multiobjective optimization model for resource allocation was developed. It was implemented in a computer system called Emergent Designer and solved using evolutionary multiobjective algorithms. This model can recommend sets of mitigation programs for funding that maximize the benefit realized, and subject to constraints on cost and other resources

For more information on this project, please contact Dr. Rafal Kicinger or Dr. Andrew Loerch.

Multiobjective Structural Design

This project investigates the use of evolutionary multiobjective algorithms in structural design. The subject of multiobjective design are steel structural systems in tall buildings which belong to the class of most complex problems in structural engineering. The project is a joint effort of researchers from George Mason University and Tohoku University, Sendai, Japan.

Traditionally, steel structural systems in tall buildings were optimized with respect to a single criterion (usually cost or the total weight) while satisfying constraints on maximal strains/stresses, maximal deflections, etc. In this project we extend this approach by investigating quantitative and qualitative changes in optimal topologies of structural systems when both the total weight and the maximum displacement of a tall building are treated as equally important objectives. This approach resulted in additional insights into the structure of this complex design problem and provided a broader perspective on the trade-offs involved.

For more information on the project, please contact Dr. Rafal Kicinger.

Emergent Designer: A Design Support Tool Based on Models of Complex Adaptive Systems

The goal of this project was to develop a novel computer system which would not only provide efficient methods for optimizing complex engineering systems but also support creativity and invention in engineering. In order to achieve this objective, an integrated research and design support tool was developed and named Emergent Designer.  It represents a new generation of computer tools which automate and optimize design processes by integrating state-of-the-art models, algorithms, and analysis and visualization methods.  The distinguishing feature of Emergent Designer is the fact that it addresses both important objectives of engineering design, namely creativity and optimality.  It does that by utilizing bio-inspired search and optimization methods and algorithms, including single- and multiobjective evolutionary algorithms, as well as novel representations of engineering systems inspired by the developmental processes in nature.

Emergent Designer has been implemented in Java with a fully functional graphical user interface. The system provides comprehensive set of statistical and time series analysis tools which support advanced analysis of design processes and their results. It also implements advanced visualization methods for presenting results of computational experiments as well as automatic report generation capabilities. Emergent Designer has been applied to several complex engineering design and critical infrastructure protection problems.

For more information on the project, please visit Emergent Designer's homepage, or contact Dr. Rafal Kicinger.

Engineering Education: Personal Air Vehicles

This project was an effort of George Mason University researchers working with NASA to develop enhanced education and training environments of future aerospace workforce. GMU is a member Hierarchical Learning Network, a consortium of universities cooperating with NASA Langley Research Center. The research had two major interrelated components. The first component was the development of a novel methodology for building intelligent tutoring systems utilizing state-of-the-art computer science, including the technology of intelligent agents. The second component was actually building a tutoring system based on the developed methodology to demonstrate its feasibility. The prototype was build for the domain of personal air vehicles.

For this prototype system, state-of-the-art technology in the area of personal air vehicles has been analyzed with the help of NASA experts. The acquired knowledge was structured into an ontology using the ontology editor Protégé. Assessment and refinement of the knowledge base was conducted in cooperation with NASA. Subsequently, a Macromedia environment was used to integrate various pieces of the tutoring system. A Macromedia JRun web application server was used to communicate with Protégé through the Java environment and to provide an interactive interface between the user and the knowledge base. The graphical user interface was developed using Macromedia Flash and integrated with Macromedia JRun. The shell was integrated with the ontology and with other parts of the system. A working demo of the system was developed and presented to the NASA and Old Dominion University engineering experts in February, 2003.

For more information on the project, please visit the project homepage, or contact Dr. Rafal Kicinger, Dr. Emeka Oguejiofor, Dr. Tomasz Arciszewski, Dr. Kenneth De Jong, or Elena Popovici.