## 2 Existing Studies
### Logical Inference through a Domain Query Language  
On the other hand, domain query languages that specify the lexicon and syntax to generate correct topological information statements [21] have been used in BIM for the integrated object query based on attribute value conditions, including shape [22]. A domain-specific language for BIM can provide a more direct access to the required data, as it is tailored to the domain model and it hides the details of the internal data [23]. MEP knowledge is always presented in nature language and, thus, it must be transformed into a query language to become potential for automatically generating the logic chain. Sensors, a kind of MEP equipment, can optimize retrieval and analysis methods according to the characteristics of monitoring information collected by them, such as the types of monitoring information (humidity, temperature, etc.), monitoring time, monitoring range, etc. Some scholars [24] utilized a logic-based modeling language TCOZ (Timed Communicating Object Z) to describe constraints of the sensor imposed by its environmental conditions or relations with other sensors. Integration of spatial operations into standardized structured query language (SQL) queries makes the BIM data much more visible for wide ranges of query capabilities and decision-making processes [25]. Apart from the query language, some visual programming languages, including QL4 BIM and VCCL, represent a new, more intuitive mechanism for data retrieval [26]. During the logic chain generation process, the prior knowledge provides relevant information that helps to guide and accelerate the processes. Specifically, the pieces of equipment and valves in MEP system have a relationship with neighboring components. In the cases of industrial plants, such information can be derived from the piping and instrumentation diagrams (P and IDs). These diagrams, which are the overall documents used to define the industrial process, contain logic information about their functional interrelationships [27]. There has been a growing consensus as to how prior knowledge can guide and facilitate the process of reconstruction of an existing large-scale facility. Prior knowledge is also used for 3D reconstruction of as-built industrial instrumentation models from laser-scan data [28]. Incorporation of prior knowledge into the reconstruction process provides supports for the automatedprocedures instead of the manual input [29]. However, the above-mentioned studies related to prior knowledge are not considered in the design and facility management of public buildings, therefore there are still some challenges for generate MEP logic tree during O and M phases. To automatically generate the MEP logic chain, this paper defines some descriptions to specify the prior knowledge of MEP systems in public buildings.  
Current BIM adoptions in MEP engineering have been concentrated on modeling [30; 31], measurement [32], collision detection [33] or collaborative design [34]. Several studies focused on MEP logic chains and the implementation of BIMs for facilitating FM tasks. Some prototype systems [35] have been developed to improve FM based on BIM and prior knowledge, including the reconstruction of 3D models from laser-scan data and a 3D CAD database in the O and M phase [24]. A multi-scale BIM management system was also developed for FM of large public buildings, containing the upstream and downstream relationship management of MEP components [31] and a method of generating schematic diagrams from 3D models of MEP systems was proposed. A cross-platform O and M management system was developed using the MEP-related information in the as-built model to run routine O and M tasks and to effectively response to MEP-related emergencies. Another research [8] studied logic relation retrieval for MEP systems based on the Revit database. With regard to the data mining applications of MEP systems, an object-oriented data capture approach was also developed for MEP information models [36].