Developing A Knowledge-Based System For Semantic Enrichment And Automatic BIM-Based Quantity Take-Off

Abstract

In Construction Cost Estimation, Building Information Modelling (BIM) Has Been Commonly Utilized To Support Automatic Quantity Take-Off (QTO). However, Conventional BIM Models Do Not Contain All The Necessary Information For QTO, And The Calculation Does Not Follow The Descriptive Rules In Standard Method Of Measurement (SMM), Which Impact The Cost Estimation Accuracy. Therefore, This Paper Presents A New Data Model And Knowledge-Based System To Incorporate The Required SMM Rules, Which Greatly Facilitates BIM Software In Automatic And Accurate QTO. The Proposed New Methods Involve The Development Of A Generic Data Model By Identifying And Incorporating The Required Information (E.G., Geometry, Semantics) In SMM. Following This, Information Checking Algorithms Are Developed To Check The Information Completeness And Textural Errors In QTO Practices. Furthermore, The Descriptive Rules In SMM Are Defined To Create A Knowledge Library That Guides The BIM Software In Performing Automatic QTO. Results Of Illustrative Examples Indicate That The Proposed New Methods Can Accurately Compute The Quantities Of Building Components In Compliant With SMM, Regardless Of Different Approaches For Model Creation. The Proposed Methods Also Linguistically Identify The Textural Errors Of Parameters And Check The Compliance Of Descriptive Rules For Better QTO Practices. Practitioners Can Automate The BIM-Based QTO Process To Reduce The Inaccuracies, Time, And Errors Of Cost Estimation.