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A Parametrics Architecture for Design, Management, and Coordination in a Collaborative AEC Environment
Author(s) -
ElBibany Hossam E.,
Paulson Boyd C.
Publication year - 1999
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/0885-9507.00125
Subject(s) - computer science , knowledge representation and reasoning , domain knowledge , inference , representation (politics) , knowledge base , software engineering , domain (mathematical analysis) , context (archaeology) , knowledge management , artificial intelligence , mathematics , mathematical analysis , paleontology , politics , political science , law , biology
This article describes a unified system architecture for representation and reasoning in a concurrent, collaborative architectural/engineering/construction (AEC) environment. The architecture is built on a formal unified modeling methodology for products and processes based on parametrics techniques. The architecture forms the core of a domain‐independent shell that could be used for incremental knowledge integration. The integrated knowledge base could be used at any point of inference to propagate changes in data values or knowledge items using parameter‐dependency networks. The article concentrates on the representation standards of the core elements of the collaborative architecture. It starts by describing the needs set by the nature of project organization in the AEC industry as well as the research philosophy. The article briefly illustrates the generic parametric representation and inference foundation, including the role of exogenous variables in domain knowledge control. The article concentrates on the standards of object‐oriented representation that form the core of domain modeling and knowledge integration. The representation covers various tasks such as context knowledge control for version management, knowledge integration with other systems, and efficient propagation control. The article shows that the careful use of domain knowledge in modeling the problem and controlling inference through exogenous variables provides guidelines toward creating sound standards for representation.