A Cloud Model-based Knowledge Mapping Method for Historic Building Maintenance based on Building Information Modelling and Ontology

被引:15
|
作者
Lee, Pin-Chan [1 ]
Xie, Wei [1 ]
Lo, Tzu-Ping [2 ]
Long, Danbing [1 ]
Tang, Xiaofei [3 ]
机构
[1] Southwest Jiaotong Univ, Sch Civil Engn, Chengdu 610031, Sichuan, Peoples R China
[2] Chengdu Liangzi Intelligent Technol Ltd, Chengdu 610041, Sichuan, Peoples R China
[3] Chengdu Hitech Zone Management Off, Chengdu 610041, Sichuan, Peoples R China
关键词
BIM; cloud model; FMEA; historic building; knowledge mapping; ontology; SEMANTIC WEB; RISK-ASSESSMENT; ARCHITECTURAL HERITAGE; CONSTRUCTION; BIM; MANAGEMENT; DESIGN; COST; SUSTAINABILITY; VULNERABILITY;
D O I
10.1007/s12205-019-2457-0
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The maintenance of historic buildings requires a systematic approach to construct and reuse maintenance knowledge. Maintenance knowledge of historic buildings has extensive and specific knowledge, but a small number of experts provide most of experience. With the rapid development of building information modelling (BIM), it can facilitate maintenance management of historic buildings. Recently, research on connecting ontology and BIM was discussed to promote building knowledge management (BKM). However, BKM is less applied in historic buildings and connection strength is also less discussed. Connection strength of knowledge mapping can increase the performance of knowledge retrieval. Therefore, this study aims to build connection between maintenance ontology of historic buildings and BIM, and also proposes a cloud model-based knowledge mapping method to evaluate the connection strength. This study uses FMEA (failure mode and effects analysis) to connect ontology and BIM, and the grey relational analysis to evaluate the connection strength. Meanwhile, cloud model is integrated into FMEA to better deal with uncertain information to increase the reliability. This study adopts a real case to valid the practicability. The results show the proposed method can evaluate the connection strengths with uncertain information and obtain the maintenance knowledge more efficiently.
引用
收藏
页码:3285 / 3296
页数:12
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