# Building Information Modelling Facility Management (BIM-FM)
## 7 Theoretical Requirements for BIM-FM
### Theoretical Functional Requirements
#### 7.2.1 Inputting and Accessing Data in the Field  
One simple approach for locating corresponding BIM objects in the field involves the use of physical markers like QR codes [41], a tool already utilised by FM practitioners. [PERSON] and [PERSON] [56] incorporated pre-existing QR asset tags within Navisworks to set up a BIM for an Egyptian University building. The QR codes allowed pre-existing information associated with a specific object, stored in an Excel format, to be automatically populated within the corresponding BIM object. Whilst the case study given was for the population of a new BIM, it is reasonable to assume that the QR codes could be used to populate new information over time. It is currently unclear if QR codes could be widely applied in a heritage context especially if the listed building status has to be abided by. [PERSON] et al. [16] suggest that identifying objects via other location data is more reliable than tags or QR codes because physical markers can be moved. This suggestion relies on an asset having a fixed position in space, so would exclude moveable assets such as furniture, whose location/quantity is often required to inform FM decisions. Alternatively, both [PERSON] et al. [85] and [PERSON] et al. [79] used Bluetooth Low Energy Beacons [86] to attribute real-world locations to a digital model. Having a shorter range than conventional Bluetooth, the beacons were able to locate the room/zone where the worker was located. Another \begin{table}
\begin{tabular}{c c c} \hline \hline
**Information Category** & **Specific Information Type** & **Sources** \\ \hline \multirow{2}{*}{1. Safety and security} & Fire safety & \\  & Health and safety & \\  & Potential threats/risks and vulnerabilities & [5,73] \\  & Security & \\ \hline \multirow{2}{*}{2. Location data} & Asset location & [16; 42; 54; 60; 73; 74; 81] \\  & Maps of surrounding area & \\ \hline \multirow{4}{*}{3. Space data} & Space usage & \\  & Occupation limits & \\  & Space breakdown & [22; 35; 53; 54; 60; 74; 81] \\  & Room allocations & \\ \hline \multirow{4}{*}{4. Maintenance manuals/instructions} & Required equipment & \\  & Minimum level of performance & \\  & Maintenance to be performed by user & \\  & Maintenance to be performed by skilled personnel & [16; 18; 22; 35; 40; 42; 44; 53; 54; 73; 78; 81; 82] \\  & Intervention type & \\  & Intervention frequency & \\ \hline \multirow{4}{*}{5. Information about the fabric of the asset} & Materials data (including properties) & \\  & Architectural data & \\  & Structure type & [16; 18; 22; 34; 40; 42; 44; 53; 54; 60; 74; 81; 82] \\ \cline{1-1}  & Condition assessments and surveys & \\ \hline \multirow{4}{*}{6. Environmental data} & Light levels & \\  & Relative humidity & [22; 35; 53; 60; 79; 83; 84] \\ \cline{1-1}  & Temperature & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information requirements for FM identified in the reviewed literature. approach involves RFID integration, which has been shown to significantly reduce the time taken to carry out normal FM tasks [50]. An emerging alternative to physical markers is automatic feature recognition. However, whilst specifically trying to locate a worker in the field, [PERSON] and [PERSON] [87] state that feature recognition is not sufficient indoors due to repetitiveness and the uniform nature of some buildings (particularly office buildings). They chose to use a combination of marker tracking (a similar approach to RFID) and feature tracking. [PERSON] et al., [PERSON] and [PERSON] and [PERSON] and [PERSON] [41; 76; 87] also employed a virtual solution, utilising AR to allow field personnel to associate digital objects with their real-word equivalent and collaborate with remote workers. For the population of occupant feedback on facilities, [PERSON] et al. [79] allowed occupants to pick the exact issue location on a 2D or 3D model. This assumed a degree of comprehension of the 2D drawing, which may not be true for all users and locations, an issue later identified as part of the location validation stage.