Machine learning and the future of construction

Karthik Venkatasubramanian

Vice president of data and analytics at Oracle Construction and Engineering

Machine learning (ML) in the construction industry has come a long way in terms of understanding what can be achieved using data science.

We’ve seen increases in its capabilities to process big data through distributed computing, as well as the emergence of Machine Learning as a Service (MLaaS), from leading cloud providers including Oracle, to democratise artificial intelligence (AI). We are only beginning to scratch the surface of ML-driven possibilities in the construction industry. But are industry professionals and project managers ready to embrace it?

Data is the key Data is the lifeblood for any AI and ML strategy to work, and while many construction businesses have data available to them, not many realise its full potential. This data, traced from previous projects and activities, and collected over several years, can become the source of data that ML models require for training. Models can use this existing data repository to train on, and then compare against, a validation test before it is used for real world prediction scenarios. Model accuracy can improve with not just more of the same data but also greater amounts of different data. Often called feature selection, the vast amount of data from different systems allows identification “markers” of project success and delays, contributing to building ML models with greater accuracy than was possible before.

Machine learning in construction The industry boasts many emerging use-cases of ML, in particular ideas that will positively impact important industry-specific metrics – schedule, budget, quality, safety and risk. Data and ML is being used to change the statusquo across all these key dimensions. Computer vision is being used to solve problems such as identifying progress on site, tracking delivery of materials, understanding movement of labour and material on-site, as well as ensuring compliance of physical distancing rules on construction sites. The application of ML techniques to unstructured data derived from videos and photos is becoming increasingly pervasive in solving several use-cases that were often tricky to solve previously. Use of natural-language processing (NLP) is also now being deployed for use-cases that should reduce manual error, improve productivity, and mitigate risks. For example, NLP is being used to track submittals required for different jobs, identify non-standard terms in a contract, highlight a potential HSE issue, or escalate the risk of an upcoming change request. These allow contractors and owners to better plan and respond to situations. The use of ML for predicting schedule delays and cost-blowouts is another area where it really helps, particularly as there is a lot of prior data on schedule and budget performance which can be used as training data to make predictions. Schedules and budgets are becoming smart by incorporating ML-driven recommendations, supply chain selection is becoming smart by using data across disparate systems and comparing performance, also risk planning is getting smart by using ML to identify and quantify risks from the past that might have a bearing on the present.

The future As smartification drives datafication, AI driven transformation will naturally happen as companies begin to question how to leverage all the data that they have. The cost of ML is already decreasing with infrastructure that leverages pay-as-you-go models in the cloud. New tools are democratising ML to the nondata scientists by way of drag-drop modelling, visualising predictions and simplifying the creation of easy to consume insights. As ML is made more available across the industry, the next five years will see a significant uptake in this technology being adopted as part of ongoing digital transformation efforts. CT

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