Evaluating the Implementation of Machine Learning Techniques in the South African Built Environment

The future of machine learning (ML) in building may seem like a general idea that will take decades to materialize, but it is far closer than previously believed. In reality, the built environment has progressively increased interest in machine learning. Although it could appear to be a very technical, impersonal approach, it can make things more personable. Instead of eliminating humans from the equation, machine learning allows people to do their real work more efficiently. It is, therefore, vital to evaluate the factors influencing the implementation of machine learning techniques in the South African built environment. The study's design was one of a survey. In South Africa, construction workers and professionals were given one hundred fifty (150) questionnaires, of which one hundred and twenty-four (124) were returned and deemed eligible for the study. The collected data were analyzed using percentages, mean item scores, standard deviation, and Kruskal-Wallis. The results demonstrate that the top factors influencing the adoption of machine learning are knowledge level and a lack of understanding of its potential benefits. In comparison, lack of collaboration among stakeholders and lack of tools and services are the key hurdles to deploying machine learning within the South African built environment. The study concluded that ML adoption should be promoted to increase safety, productivity, and service quality within the built environment.