Empirical Research on BIM Application for Cost Estimation of HVAC System in Buildings
Date Issued
2016
Date
2016
Author(s)
Chong, Un-On
Abstract
Building information modeling (BIM) is considered as a major trend of the construction industry in the future. Some owners have required implementation of BIM to help improve collaboration between different professions, conflict detection and material quantity takeoff (QTO), etc. However, the development of BIM in cost estimation is still limited by the negative willingness of the consultants to use BIM as a design tool for buildings because of the cost and time to adapt the new technique. In traditional way to estimate the cost of the HVAC system in buildings, a part of the total cost is evaluated as a rough approximation, as known as lump sum, instead of detailed calculation and that leads to an inaccurate cost and making the origin of it invisible. The research aims to build the building information model of a completed public building and clearly export the detailed quantity and cost of the items which can only be budgeted approximately in traditional estimation method. By the process of modeling and comparing the numbers of both the BIM method and traditional method, verified the availability and accuracy of BIM QTO and cost estimation of HVAC system. One of the purposes of this study is to promote BIM to the owners and MEP designers. After a thorough investigation into the parameter setting of the BIM software, some limits in the cost estimating capability of the software were found and it shows that the software is still waiting to be perfected. However, by building detailed HVAC system components of a completed public office building, overestimation of HVAC budgets has been found and leads to contractors’ extra profit equivalent to 3.9% of the total cost of all the built items in the building information model.
Subjects
HVAC system
BIM
lum sum
QTO
cost estimation
SDGs
Type
thesis
File(s)
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Name
ntu-105-R03521705-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):14dd9b4b8061978fecd516be2f4424cd