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2018 BayesiaLab Conference in Chicago

Conference Presentation

Metamodeling with Bayesian Networks to Facilitate Intelligent Use of Engineering Simulation in Early Stages of Building Design

Zack Xuereb Conti, Visiting Research Scholar
Harvard University Graduate School of Design
Singapore University of Technology and Design

2018-11-02 BayesiaLab Conference Xuereb Conti


For decades, engineering simulation tools such as finite element analysis have been utilised by
engineers to aid with consulting architects on how proposed building designs are likely to
behave, before they are constructed. More recently, the emergence of computational tools in
architecture, together with faster simulation algorithms, are enabling architects in the early
stages to explore and evaluate a larger variety of design options by searching iteratively through
a so-called ‘design space’. A design space in our context is a multidimensional mathematical
space typically bound by the simulation inputs and outputs. When the number of variables
defining the design space are more than a handful, it becomes cognitively challenging to draw
meaningful inferences on how the input design variables are influencing the simulation
response. Consequently, using simulation blindly leads to a shallow understanding of the
design space.

In response, we adopt Bayesian networks to compress input/output simulation data into a
simulation metamodel whose underlying relationships can be explored. Simulation
metamodels are widely used in fields such as aerospace and automotive engineering for quick
response prediction. However, most metamodels are typically formulated as forward
mathematical functions (inputs to output) whose mapping remains difficult to infer global
knowledge from when studying multiple variables. Bayesian networks on the other hand, do
not distinguish between inputs and outputs. Thus, influence between design variables and
simulation response can be explored bi-directionally to reveal the important dependencies
driving the engineering response, over the entire distribution of sampled data-points in the
design space. Through an applied case study involving structural design, we will illustrate how
designers may utilise a Bayesian network metamodel to reveal valuable insight in practice.