Open AccessSmart BIM-AM Journey to Green Buildings
Author(s) -
C. K. Lee,
H. Y. S. Chan,
C. Y. C. Poon,
K. M. G. Yip,
Pong-ming Yuen
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/290/1/012050
Subject(s) - building information modeling , radio frequency identification , real time locating system , closed circuit , asset (computer security) , asset management , information model , reset (finance) , engineering , systems engineering , computer science , architectural engineering , real time computing , telecommunications , computer security , software engineering , finance , compatibility (geochemistry) , chemical engineering , financial economics , economics
Building Information Modelling (BIM) has been widely adopted for buildings design and construction to facilitate coordination works, however, there are a few studies of its application in the long lifecycle of buildings operation and maintenance (O&M). An integrated BIM – Asset Management (BIM-AM) System which enables visual cross-reference from real-world objects to BIM model and even to their maintenance record, O&M manuals, asset relationships, live views of Closed Circuit Television (CCTV) system, real-time data from Building Management System (BMS) and wireless Internet of Things (IoT) sensors as well as location information from a Real Time Location System (RTLS) on one single integrated mobile platform with the aid of Radio Frequency Identification (RFID) scanning technology has been developed. While the BIM-AM System has been proven its novelty, originality, capability and potential towards smart O&M by the patent granted in 2017, BIM can be further developed in green building aspects with the application of Computational Fluid Dynamic (CFD). Different sets of boundary conditions, including supply air temperature, supply air flow, number of people and so on, were simulated for comfort level. A predictive model was formulated to achieve an intelligent control of air-conditioning system, including supply air temperature reset and adjustment of supply and/or fresh air flow, with the balance of human comfort.