z-logo
open-access-imgOpen Access
Knowledge-based Design Cost Estimation Through Extending Industry Foundation Classes
Author(s) -
Shen Xu,
Kecheng Liu,
Weizi Li
Publication year - 2014
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5220/0004866401610168
Subject(s) - activity based costing , computer science , estimation , process (computing) , representation (politics) , knowledge representation and reasoning , knowledge based systems , divergence (linguistics) , knowledge management , industrial engineering , data mining , systems engineering , artificial intelligence , engineering , business , linguistics , philosophy , marketing , politics , law , political science , operating system
In order to overcome divergence of estimation with the same data, the proposed costing process adopts an integrated design of information system to design the process knowledge and costing system together. By employing and extending a widely used international standard, industry foundation classes, the system can provide an integrated process which can harvest information and knowledge of current quantity surveying practice of costing method and data. Knowledge of quantification is encoded from literatures, motivation case and standards. It can reduce the time consumption of current manual practice. The further development will represent the pricing process in a different type of knowledge representation. The hybrid types of knowledge representation can produce a reliable estimation for construction project. In a practical term, the knowledge management of quantity surveying can improve the system of construction estimation. The theoretical significance of this study lies in the fact that its content and conclusion make it possible to develop an automatic estimation system based on hybrid knowledge representation approach.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom