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Augenbroe, Godfried

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    Fried Augenbroe: COA Research Forum
    (Georgia Institute of Technology, 2009-10-29) Augenbroe, Godfried
    Since 1997, Fried Augenbroe heads the Building Technology area in the Doctoral Program in the College of Architecture at the Georgia Institute of Technology in the USA, where he teaches graduate courses and conducts research in the fields of building performance concepts and simulation, control of smart systems, e-Business, system monitoring and diagnostics. He has also established an active research record in building process studies, construction project management, web hosted collaboration, and knowledge management, dealing with the development of software tools, their interoperability and their business integration. In the field of energy modeling Augenbroe has led large building energy simulation projects for the development and application of energy saving technologies in buildings and residential construction. He has developed building energy performance metrics for large institutional real estate managers such as the General Services Administration in the US. He is also active in the development of Communities of Practice exploiting the emergence of WEB 2.0 social computing environments. As one of the first applications he is developing a CoP in healthcare design.
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    Uncertainty Analysis in Using Markov Chain Model to Predict Roof Life Cycle Performance
    (Georgia Institute of Technology, 2005) Zhang, Yan ; Vidakovic, Brani ; Augenbroe, Godfried
    Making decisions on building maintenance policies is an important topic in facility management. To evaluate different maintenance policies and make rational selection, both performance and maintenance cost of building components need to be of concern. For roofing sytem Markov Chain model has been developed to simulate the stochastic degrading process to evaluate the life cycle perfornance and cost. [Van Winden and Dekker 1998; Lounis et al. 1999] Taking value in a discrete state space, this model is especially appropriate when scaled rating regular inspections and related mainteance policies are implemented in large organizations. [Van Winden and Dekker 1998] However, many parameters in this Markov Chain model are associated with variance of significant magnitude. The propagation of these variances through the model will result in uncertainties in predicted life cycle performance and cost results. Without a solid uncertainty analysis on the simulation, decisions based on these simulation results can be unrealiable. In this paper we provide methods to estimate the range of parameter values and represent them in a probabilistic framwork. Monte Carlo method is used to analyze simulation output (life cycle cost and performance) variance propagated from these parameters through the model. These probablisitc informnation can be used to make better informed decisions. An example is provided to illustrate the Markov Chain model development, parameter identification method, Monte-Carlo uncertainty assessment and decision making with probabilistic information. It is shown that the uncertainty propagating through this process is not negligible and may significantly influence or even change the final decision