Use the Right Tools and Techniques to Improve Processes in the Construction Industry

construction site at dusk

To the layman, the construction industry might seem like one that’s highly dependent on manual labor. And while that’s true, there’s also been a move towards digitizing the industry and introducing artificial intelligence across the entire value chain. 

The use of AI in the construction industry spans across the entire lifecycle of a project — from forecasting prices of raw material before building starts to predictive maintenance after a building is complete. 

Construction Robotics’ MLE helps with the heavy lifting of construction material. Source: Fortune

Companies like Built Robotics and INTSITE produce upgraded heavy machinery with computer vision and AI guidance systems. Their autonomous machines are able to take over the most back-breaking portion of building, thus enabling faster and safer construction processes.

Other companies like Scaled Robotics and INDUS.AI have developed automated monitoring and verification systems that are able to reduce the inefficiencies in construction processes. 

Why the construction industry needs AI

Two things that affect margins in the construction industry are rework and warranty claims. Some industry insight reports have indicated that rework can account for up to 20% of the cost of a project and that the same issues leading to rework also lead to warranty claims. 

Factors that contribute to the need for rework include misinformation during engineering, inefficient material supply, human resources and other communication issues. These are all things that AI can assist with. 

AI can help with predicting raw material requirements and placing orders. It can also assist with scheduling and quality control. It can assist with the more mundane or risky parts of construction. 

Robots can assist its human operator by automating the more routine tasks, allowing the operator to direct his attention to more complicated and higher value parts of the work. It can also assist with safety and progress monitoring by scanning the construction site and comparing it against the building plan. 

Data labeling methods for construction use cases

Two methods of data labeling are typically used to produce the data required to train autonomous machines and monitoring tools — polygon or bounding box annotation and image segmentation.

In polygon or bounding box annotation, data labelers look at pictures and draw a box around the objects that they want to annotate within a specific picture. After drawing the box, they will have to select from a list of labels to provide an attribution for the object within the box. 

These boxes can be 2D or 3D, where the 3D version would also provide information on the approximate depth of the objects. In order to improve accuracy, data labelers will have to ensure that the anchors of these bounding boxes line up as closely as possible with the edges of the objects within the images. 

The data produced through this method is also used for safety monitoring of site workers, as well as for a machine to recognise obstacles and thus, be able to navigate within a construction site safely while getting work done in an efficient manner. 

An example of image segmentation. Source: Analytics Vidhya

For further accuracy, especially in use cases like performance monitoring, image segmentation can be used for feature extraction to add another level of detail to the data. 

While bounding boxes provide a set of coordinates that indicate that there is an object within its lines, image segmentation “creates a pixel-wide mask” for each of the objects. Training machines with this data would allow them to pick up more specific information on objects within the construction site.  

Using the right tools for labeling

When it comes to data labeling, having the right tools for the process is vital. This is why working with the appropriate data labeling partner is imperative. 

The truth is, not all labeling tools are equipped to perform exactly according to a client’s specific requirements.  In many cases, it may be more prudent to work with a third party data labeler, like Supahands, that has the right tool for the job. 

Construction may be a single industry, but the variety of AI solutions within just this one industry are vast. There could be a range of different use cases, specific to each individual client. 

Our annotation tool is completely customizable to accommodate the multiple use cases that might be required. 

Looking for an AI solution? Curious to see what Supahands can do for your business? Request a free demo of our platform! 

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