Machine Learning in Construction: Predicting Oil Temperature Anomalies in a Tunnel Boring Machine

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Today, we continue our series of blog posts highlighting presentations from the 2nd Edition of Seville Machine Learning School (MLSEV). You may read the first post about the ‘6 Challenges of Machine Learning’ here.

One of the very interesting real-world use case presentations during the event was that of Guillem Ràfales from SENER. Founded in 1956 in Spain, SENER is a multi-national private engineering and technology group active in a set of diverse industrial activities such as construction, energy, environment, aerospace, infrastructure and transport, renewables, power, oil & gas, and marine.

SENER Projects
SENER’s Tunnel Construction Projects
Under its construction activities, SENER has successfully completed 19 large scale tunnel boring projects amounting to 80 kilometers of urban tunnels and a total of 224 kilometers of tunnels in the last 20 years. A great example is the high-speed railway service project in Barcelona. SENER delivered the 5.25 km segment near Gaudi’s architectural masterpiece Basílica de la Sagrada Família, a UNESCO World Heritage site.
Tunnel Boring BCN
Select technical specs of SENER’s project in Barcelona, Spain
Tunnel Boring Machines (TBM) are used to perform rock-tunneling excavation by mechanical means. The main bearing of a TBM is the mechanical core of the colossal machine. It enables the turning cutter head and transmits the machine’s torque to the terrain. At all times, It is critical to keep the bearing properly lubricated, often to the tune of 5000 liters of oil. One of the ways to monitor TBM performance is to analyze the physical and chemical properties of the lubricant oil in regular intervals.


The operational benefits of applying Machine Learning and advanced analytics in the context of TBMs can be summed up as avoiding unnecessary wear, costly equipment breakdowns, and overall suboptimal performance that may result in cost and project delivery overruns.


With this consideration in mind, BigML has worked closely with SENER engineering teams to build models to predict changes in the gear oil temperature variations for their TBMs. There two main objectives of the project were to:
  • understand how various internal TBM parameters are related to temperature changes
  • try and predict such temperature changes to avoid machinery wear or failure

The team worked on a large dataset from a past SENER project that contained hundreds of measurements internal to TBM operations sampled every 10 seconds. Some of the key measurements included torque, speed, pressure and chamber material attributes. The fact that notable oil temperature variations tend to take place gradually and infrequently added to the overall challenge in the form of a highly unbalanced dataset. Despite these, BigML’s feature engineering, algorithmic learning resources were put to great use. The team was able to uncover key insights with the help of Association Discovery during the data exploration phase followed by Anomaly Detection and Classification modeling that ultimately helped SENER technicians isolate an important subset of instances, where the oil temperature increases could be anticipated in advance. The entire custom workflow was captured in the BigML platform for traceability, easy re-training and automation purposed as seen in the plot below.

BigML for Construction
Custom BigML workflow for the SENER project.

If you’ve hung around thus far, it’s time for you to take a more in-depth look into this exciting project pushing the limits of Machine Learning-powered smart applications in the field of Construction Engineering. The end-to-end Machine Learning process underlying this endeavor was managed and presented by BigML’s own Guillem Vidal. Now, please click on the Youtube video below and/or access the slides on our SlideShare channel:

Do you have a Construction Engineering challenge?

Depending on your specific needs, BigML provides expert help and consultation services in a wide range of formats including Customized Assistance to turn-key smart application delivery — all built on BigML’s market-leading Machine Learning platform. Do not hesitate to reach out to us anytime to discuss your specific use case at


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