Facility management (FM) accounts for more than two thirds of the total cost of the
whole life cycle of a building. FM staff do have inadequate visualization and often have
difficulty in querying information using 2D drawings and traditional facility management
systems. Currently, building information modeling (BIM) is increasingly applied to FM in the
operations and maintenance (O&M) stage. BIM represents the geometric and semantic
information of building facilities in 3D object-based digital models and enables facility
managers to manage building facilities better in the O&M stage. At the same time, the Internet
of Things (IoT) technology can be used to acquire operational data of building facilities and
real-time environmental data to support FM. However, few studies have used BIM and IoT
technologies together for automated management and maintenance of building facilities.
Around 65%~80% of the FM comes from facility maintenance management (FMM). However,
there is a lack of efficient maintenance strategies and appropriate decision making approaches
that can reduce FMM costs. Facility managers usually undertake reactive maintenance or
preventive maintenance strategies in the O&M stage. However, reactive maintenance cannot
prevent failures and preventive maintenance cannot predict the future condition of building
components, which leads to maintenance actions being performed after failure has occurred
and it cannot keep the functionality of a building consistent. This study aims to apply a
predictive maintenance strategy with BIM and IoT technologies to overcome these limitations.
In addition, there is an information interoperability problem among BIM, IoT and the FM
system. Therefore, this study aims to leverage the BIM and IoT technologies to improve the
efficiency of FMM and to address the information interoperability problem of integrating BIM,
IoT and the FM system.
In order to improve the efficiency of FMM, an FMM framework is proposed based on
BIM and facility management systems (FMSs), which can provide automatic scheduling of
maintenance work orders (MWOs) to enhance good decision making in FMM. In this
framework, data are mapped between BIM and FMSs according to the developed Industry
Foundation Classes (IFC) extension of maintenance tasks and MWO information in order to
achieve data integration. Geometric and semantic information of the failure components is
extracted from the BIM models in order to calculate the optimal maintenance path in the BIM
environment. Moreover, the MWO schedule is automatically generated using a modified
Dijkstra algorithm that considers four factors, namely, problem type, emergency level, distance
among components, and location.
In order to provide a better maintenance strategy for building facilities, a data-driven
predictive maintenance framework based on BIM and IoT technologies for FMM has been
developed. The framework consists of an information layer and an application layer. Data
collection and data integration among the BIM models, FM system, and IoT system are
undertaken in the information layer, while the application layer contains four modules to
achieve predictive maintenance, namely: (1) condition monitoring and sensor data acquisition,
(2) condition assessment module, (3) condition prediction module, and (4) maintenance
planning module. In addition, machine learning algorithms, i.e. artificial neural network (ANN)
and support vector machine (SVM), are used to predict the future condition of building
For the information interoperability problem among BIM, IoT and FM system, an
ontology-based methodology framework is proposed for data integration among the BIM, IoT
and FM domains. The ontology-based approach is developed as a tool to facilitate knowledge
management in BIM- and IoT-based FMM and improve the data integration process. First,
three ontologies are developed for BIM, IoT, and FMM respectively according to the ontology
development process and facility information requirement. Second, an ontology mapping
method is designed to integrate the three developed ontologies based on mapping rules.
Moreover, ontology reasoning rules are developed based on description logics to infer implicit
facts from the integrated ontology and support quick information querying on FMM. The
developed framework is validated through an illustrative example.
This research provides an automatic work order scheduling approach in FMM and
predictive maintenance strategy for building facilities, thereby enabling great saving in time
and labor costs for facility staff. In addition, the proposed ontology-based methodology can
address the information interoperability problem and integrate data from BIM, IoT and FM
system for facility maintenance activities. In the future, the ontology-based methodology will
be applied for the operation management of building facilities.
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