Before a contractor can operate on subsurface infrastructure, information derived from ground radar scans is needed for decision-making. The extent and quality of this information however is often constrained by time pressure, potentially leading to catastrophic results from a safety and economic perspective.
The case study MapXact in our new ebook "3 practical business cases in asset and maintenance management" illustrates how artificial intelligence can streamline workflows and improve efficiency.
At MapXact, employees used to spend vast amounts of time driving a ground radar scanner over kilometres of roads then working for up to three days to interpret the acquired scans. The MapXact team searched extensively for a solution that could accelerate this process before realizing that the answer lay in intelligent automation, based on artificial intelligence.
BrainCreators BrainMatter platform reduces the time required to analyze ground radar scans from days to minutes, enabling MapXacts customers to focus on more productive tasks.
Intelligent automation is achieved when a machine learns to perform an expert task from examples and by following specific business rules. In this case, there were many examples available because human experts previously performed the task.
The business rules define how the information extracted from the radar scans is visualized in a 3D representation of the underground infrastructure. Once engaged, BrainMatter keeps learning on the job, leading to better performance in an even shorter time. Eventually, Mapxact will offer a real-time underground analysis in an augmented reality interface.
For MapXact, our BrainMatter technology has directly impacted how the company operates by allowing it to continue to use the most effective method of mapping subsurface infrastructure.
MapXact’s services help infrastructure developers and managers ensure their projects don’t interfere with underground cables and pipes. Ground radar produced detailed images, but each of the thousands of images generated at each site had to be viewed manually to distinguish between disturbances created by natural materials and those created by existing infrastructure.
By using artificial intelligence to review these images, MapXact’s analysts can focus on more productive tasks while ‘teaching’ the system to improve its analytical abilities.
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