Although there are those in the construction industry who are hesitant to embrace machine learning, it has the potential to improve the lives of people involved during and after a project.
Despite it being part of the field of artificial intelligence (AI), machine learning in construction is not as futuristic as some people may think. Following are 4 ways you could use machine learning on your construction projects:
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- Improve the quality of your designs.
Machine learning, for instance involving Autodesk Dynamo, can be used to design building spaces to best fit the needs of the people who will be using it. It can also help teams discover in advance of construction any errors and design problems that could require costly rework later. Machine learning can even be used to test environmental conditions and scenarios using the 3D model.
2. Create a safer jobsite.
Safety is likely one of your top priorities. Machine learning can help.
People can input the tools and materials on a job, and the machine can interpret what activities will be most dangerous. AI can also pore over jobsite photos for safety risks and potential hazards, like a particular type of equipment that has been involved in a jobsite accident previously. It can check thousands of images in a fraction of the time it takes people to do the same.
When a person tells a machine what to look for, an AI tool can quickly sort through data and report relevant alerts. People can then study the alerts further.
3. Assess and reduce risk.
With machine learning, you can predict risks before bad things happen. For instance a machine can identify the risk of a problem arising, measure its impact and use predictive analytics to help you avoid it. This might include action needed to close communication gaps among design, construction and operations teams. Teams can identify potential problems and take action to prevent them. A tool like Construction IQ, a product in Autodesk Construction Cloud, identifies and prioritizes construction risk using AI algorithms, so you understand the consequences of not handling an issue. Thus, construction managers can streamline their workflow and prevent problems.
Another example is Pype. It uses AI and machine learning to study data from project plans and specifications. The data provides insights on project management workflows and helps achieve better quality and efficiency.
4. Lengthen the project’s useful life.
After construction, machine learning can be useful for facility management to extend the useful life of a project. Historically, there have been gaps in important information needed for effective facility management, which complicates repairs and renovations.
Machine learning can collect and use information from documents and things like work orders. It can assess site conditions in real time. The machine handles such tedious, time-consuming duties, so people can focus on other things. When machine learning is integrated into a BIM model for operations and maintenance, it can calculate the best way to approach maintenance and repairs by predicting when and where issues will arise.
When it comes to machine learning and its potential to improve the lives of people involved in and after construction, it’s time to take a closer look.
AI and machine learning are already improving building designs, making jobsites safer, reducing risk, and making spaces better for the humans who ultimately inhabit them.
If you need a partner to help you investigate or implement computational design, contact Applied Software today. The experts of Applied will help you champion the solution that is right for your company.