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How Machine Learning Is Useful in Construction Industry

Construction Tech Review | Tuesday, June 29, 2021

Machine learning can significantly improve the daily lives of humans on the job site, particularly in construction.

FREMONT, CA: While machine learning in construction may seem like a distant concept decade away from implementation, the technology's future is nearer than people think. Indeed, machine learning has been gaining momentum in the construction industry. While this may appear to be a highly technical, non-human approach, it can humanise things. Rather than eliminating humans from the equation, machine learning enables people to perform their actual jobs more effectively. While it may sound like science fiction, its applications are far more technical and extremely useful. The following are some practical applications of machine learning in construction.

Improve design quality

Machine learning can enhance overall designs to make spaces better for end-users. Startup workspaces use machine learning to help understand and predict the frequency of use for these meeting rooms, and the company can design the space to best fit people's needs before starting construction. Machine learning benefits in design don't end there. Machine learning can also help workers identify errors and omissions that may occur in the design before building. Instead, workers can leave that to machine learning, which ultimately saves critical times for teams to use for more productive tasks. With machine learning, the model can even test various environmental conditions and situations. The technology can help determine if a particular design element is optimal or can predict if it could create a problem down the road.

Risk Assessment and Mitigation

One of the truly remarkable aspects of machine learning is its ability to predict risks before they occur. Machine learning can help identify risks, quantify their impact, and use predictive analytics to assist in risk mitigation.

Extend the Life of the Project

Apart from design and construction, machine learning can also extend the life of an asset through facility management. In general, critical information in facility management is frequently missing. As a result, managing repairs and renovations on-site efficiently and economically is challenging. Machine learning can assist in streamlining the process by collecting and utilising data and information more effectively. This is accomplished by accurately classifying documents and data such as work orders and assessing pertinent conditions in real-time. This relieves people of these tedious and time-consuming administrative tasks, allowing them to concentrate on the real issue at hand. Additionally, when machine learning is integrated into a BIM model for operations and maintenance, it can determine the optimal method for performing maintenance and repairs by visualising when and where problems will occur.

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