Current methods to develop standard Architecture,
Engineering, Construction (AEC) product models focus on
the definition of product model semantics without concurrent
and formal consideration of the engineering analyses that
such models must support, or formal consideration of the
requirements for sharing information between applications.
We present two case studies that demonstrate a service
to extract data from product models and provide inputs
to component analysis applications. The service was validated
in a proof-of-concept application called the Internet Broker
for Engineering Services (IBES) that extracts information
for component analysis from product models that are external
to the application and accessed across the Internet. IBES
was tested for two research cases. The product model for
the first case, control valve selection is based on STEP
Application Protocol 227. The product model for the second
case, control valve diagnosis, specifies additional semantics
that support the operations and maintenance (O&M) phase
of the facility life cycle. The cases offer evidence that
large standard data models can support routine analyses
for control valves. However, the amount of shared information
between the case applications is small and is largely dependent
upon the concurrence of component behaviors that are necessary
to model analysis. The IBES reference model and reasoning
to support information extraction was consistent for both
cases. This consistency suggests that it is possible to
define a general set of computational methods that integrate
project information models with external component analysis
applications across the product life cycle. We argue that
enabling a web-based link between product models and applications
requires a set of capabilities, including bi-directional
communication between separated data and analysis nodes,
query generation, data translation, and validation of data
extracted from semistandard models. We discuss the tentative
implication that minimal shared information calls into
question the assumption that large core product models
will work effectively in practice.
