The Potential of AI in Construction

Category: AI Insights

Published date: 16.12.2020

Read time: 6 min

All of the AI digitization technologies used in the construction industry are disrupting traditional processes and making them a lot more efficient. In fact, AI is optimizing the entire value chain starting with ideation to design and, ultimately, all of the phases of construction. Even though AI has already made such an impact in the construction industry, we have not yet scratched the surface of its true potential. Let’s take a look at some of the ways AI is already being used in construction and how they are becoming an invaluable part of construction projects.

Using Drones to Track Progress

In the old days, managers would have to walk around the site and inspect the progress being made on the construction. Nowadays, this is all done by AI-powered drones. They hover over the site and use image recognition to compare how the site looked, for example, one month ago and how it looks now. Previously, the AI could identify the machinery present on the site, but now, it is able to identify various parts of that equipment. For example, an excavator has wheels, a cabin, an arm, and a shovel for excavation. Then, the machine learning is able to understand the particular actions performed by each of these parts such as digging, dumpling, moving, etc. Then the AI needs to possess the human concept of contextualization to put it all together i.e. how many times can an excavator rotate its body in a full workday? How many times can the arm of the excavator move up and down? Such information provides insights into the productivity of the employees and the progress of the overall project. 

In order to create such a product, human data annotators would first need to annotate millions of images first labeling all of the bulldozers, excavators, cranes, and other machinery used in construction. Then they would have to perform more detailed annotation, semantic segmentation, to identify the various parts of the machinery. The actions such as digging, moving and so on would also need to be annotated so the machine learning can distinguish between all of the various actions. As you can imagine, this is very time-consuming work, which is why companies developing such technology choose to outsource their data annotation work to a service provider. 

Improving Construction Site Safety 

A construction site presents many possible situations where an accident can occur and it is difficult for humans to always be aware of potentially hazardous working conditions and scenarios. This is where AI can also be very valuable since it can detect such dangerous conditions before they cause any harm. In fact, even back in 2016, researchers created a competition between humans and the AI technology to see who can identify hazardous scenarios more quickly and accurately. They assembled a team of human experts and showed them 1,080 images of potentially risky situations and showed the same images to the AI technology. It turned out that the AI could analyze such a volume of images in less than 5 minutes while it took the team of human experts more than 5 hours. Over the past four years, researchers have been able to further hone the technology with more training data and now such AI products are even more accurate than in 2016. 

In order to determine whether any hazardous conditions are present, the AI uses computer vision to scan the job site for potential safety violations. This can be something as simple as an employee not wearing gloves or two workers standing too close to one another. Since there are so many possible situations that could lead to an accident, we can imagine the amount of data that would need to be annotated. Also, despite the large volume of data, the quality of the annotation work must be at the highest level since this technology could provide life-saving warnings. 

Mindy Support is Facilitating the Development of New AI Construction Technology

We mentioned that developing AI construction technology requires a lot of data annotation and it would not be very effective for companies to perform this work in-house. Not only is it time-consuming, but it would also be very expensive to hire people in your local market to this work or ask your developers to do it. This is why it would be better to outsource all of your data annotations needs to Mindy Support since we will take this burden off your shoulders and any overhead costs associated with hiring data annotators. We have a successful track record of actualizing data annotation projects of various sizes and we can assemble even the most sizable teams within a short time frame. 


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