Build Wise

In construction, doing an early stage feasibility assessment is—of course—an absolutely critical first step. That’s why it’s so surprising that it’s often still such a manual, time-consuming process. Compared to how things are done in other industries, it seems of a different era. 

During an early stage feasibility assessment, a builder will typically engage an architectural office to develop a few different building plan options. After that, the builder will choose one or two to develop in more detail. 

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.