In the previous article we gave you an introduction to machine learning in construction, and we mentioned that machine learning can be broken into two broad categories: supervised learning and unsupervised learning. While supervised learning has an output or response variable that analysts are focused on (for example, a cost performance metric), unsupervised learning relies on having no response variable – the objective is to simply explore the data available.

We're delighted to announce the acquisition of Lifecycle Technology Ltd, a global technology and consulting firm, based in the UK, that focuses on delivering information management solutions to the engineering, construction and asset intensive industries. The acquisition will strengthen our deep digital capabilities and offerings to the capital projects market.

Machine learning is an exciting concept in the construction industry. To us at Enstoa, where we specialize in finding meaning in construction project data, machine learning helps improve processes and project outcomes.

By applying machine learning techniques to the project record, we can improve our understanding of performance trends, identify areas of opportunity for continuous improvement initiatives, and make better decisions faster by leveraging our data as a decision support system.