Analytics for Mechanised Tunnelling
MissionOS Data Object Models
In order to do repeatable feedback on tunnel data first the information has to be gathered in structured automated or semi‐automated ways. The MissionOS platform has a completely configurable and dynamic data model which enables users to create tables.
Monitoring Objects
Monitoring objects include sensor data recorded both manually and in an automated data‐logged way. These include data from environmental, geotechnical and structural monitoring and data streams from machines.
Geospatial Data
The GIS capabilities of MissionOS are used to effectively store layers of geospatial data such as topography, land-use, ground-hazard and settlement sensitivity, rainfall etc. All these can be used as a part of the analytics process.
Construction Objects
Construction objects are the objects to be built either as temporary or permanent works. These are often identified within the project BIM (Building Information Modelling) and are supplemented with records of completion progress and metadata.
Time And Costs
In addition to simple progress records users can define activity recording schemes build around delays codes. These activity schemes can be applied to the Master or Parent activities and can track the activities behind the production of each sub-job element.
Various shift management options are available from simple manual reports to zones based and fully automated production monitoring linked to multiple machines.
Metadata Records
Each time entry can be linked to progress of a job and can also be linked to a set of data records.
For instance, concreting can be linked to a series of records relating to the concreting process from batch requests to delivery notes to QA/QC test results and inspections.
Predictions
Each data model can be adapted to your own requirements including the standardisation of nomenclature and this is the key to the success of any future analytics.
The data models are used to generate reports as required by contract specifications and these can be fully automated to save time and resources.
Data Warehousing for Knowledge Engineering
Before “Big Data” can be analysed and compared it must be summarised into statistics which facilitate analysis. These must have a common basis for comparison.
Cycles and Cycle Statistics
Defining summarising methods to ensure optimum correlation opportunity. Within MissionOS, users can define cycles which have some relevance
to construction. For a building, these might be concrete pours, for a tunnel blasts, rings, PEG rounds etc. Ultimately the various data collected within
MissionOS summarised into statistics based on each of these cycles. Similar cycle types from job to job can then be effectively compared.
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