Author:
STAUB–FRENCH SHERYL,FISCHER MARTIN,KUNZ JOHN,ISHII KOS,PAULSON BOYD
Abstract
Construction cost estimators are confronted with the challenging task
of estimating the cost of constructing one of a kind facilities. They
must first recognize the design conditions of the facility design that
are important (i.e., incur a cost) and then determine how the design
conditions affect the cost of construction. Current product models of
facility designs explicitly represent components, attributes of
components, and relationships between components. These
designer-focused product models do not represent many of the
cost-driving features of building product models, such as penetrations
and component similarity. Previous research efforts identify many of
the different features that affect construction costs, but they do not
provide a formal and general way for practitioners to represent the
features they care about according to their preferences. This paper
presents the formal ontology we developed to represent construction
knowledge about the cost-driving features of building product models.
The ontology formalizes three classes of features, defines the
attributes and functions of each feature type, and represents the
relationships between the features explicitly. The descriptive
semantics of the model allow estimators to represent their varied
preferences for naming features, specifying features that result from
component intersections and the similarity of components, and grouping
features that affect a specific construction domain. A software
prototype that implements the ontology enables estimators to transform
designer-focused product models into estimator-focused, feature-based
product models. Our tests show that estimators are able to generate and
maintain cost estimates more accurately, consistently, and
expeditiously with feature-based product models than with industry
standard product models.
Publisher
Cambridge University Press (CUP)
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering
Cited by
33 articles.
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