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.
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.
In construction, value is created by funding and completing the right projects safely and efficiently. Delivering projects faster and at lower cost produces higher returns.
Unfortunately, the construction industry lags behind other industries in ways that hurt those returns. The global economy, for instance, has improved productivity at over twice the rate of the construction industry. Manufacturing boasts a productivity improvement rate over three times as high.