How Machine Learning Is Improving Construction Safety

Affiliate Disclaimer: Some of the links below are affiliate links. As an Amazon Associate, I earn from qualifying purchases.

Some of the most important technological developments of the last decade have happened around artificial intelligence, also known as AI. In nearly all areas of our lives, artificial intelligence has improved human efficiency, safety, and accuracy. But what does it have to offer in construction safety?

Machine learning is improving construction safety by analyzing construction sights and projects to identify both the highest risk and most likely safety issues, as well as identifying human failures in safety.

The impact of machine learning on construction, particularly construction safety, is a very complex topic, but it’s also very rewarding to understand. Read on to learn more about machine learning, how it works, how it relates to construction, and how it is improving safety.

Machine Learning and Construction Safety

AI is helping construction in many ways, but perhaps the most important one is ensuring that those working on construction sites, as well as those who will operate in the space after construction, are kept safe.

From environmental monitoring to building safety, machine learning and artificial intelligence are changing construction safety.

Physical Risk Management

One of the most important things a worksite can do is improve the safety of those building the project.

With artificial intelligence and machine learning, supervisors and project planners can input the materials, tools, and design of the project and understand which parts of the project will be the most dangerous, as well as how a team can help to mitigate this risk the most.

Similarly, artificial intelligence can identify what job on the worksite carries the highest degree of risk and can help develop training programs to mitigate this risk. Machine learning, in particular, is helping improve workforce safety as more information is provided regarding safety outcomes.

If AI is able to learn that its safety recommendation was wrong, it will be better able to process the data and improve safety solutions in the future.

That said, here are a few notable ways machine learning comes in handy in physical risk management in construction projects:

Machine Learning Is Helping Spot Potential Hazards

One of the most important ways machine learning is being used to improve safety in the construction industry is by helping to identify potential hazards. Most notably, machine learning algorithms analyze data sets and look for patterns, providing information you can use to identify potential hazards on a construction site.

For example, if the data shows more accidents on construction sites with a certain type of scaffolding, the machine learning system can flag that as a potential hazard. This information can then be used to improve safety on future construction sites.

Robotics Is Helping With Bricklaying and Welding

Robotics is also being used to help with some dangerous tasks. For example, robots can now help with bricklaying and welding. This is important because these are two of the most dangerous tasks on a construction site.

Using robotics to help with these tasks will help improve safety on future construction sites. The robots can be programmed to follow safety protocols, and they can be monitored by machine learning algorithms.

Computer Vision Is Making Scaffolding Safer

Scaffolding is one of the most common pieces of equipment on a construction site, and it’s also one of the most dangerous. Many accidents in the construction industry are caused by scaffolding.

However, computer vision is now being used to make scaffolding safer. For instance, you can use machine learning algorithms to inspect scaffolding for potential hazards. You can then use this information to make changes to the scaffolding and improve safety.

Environmental Safety

Artificial intelligence is also making drastic improvements in maintaining a safe environment for workers to operate in.

Many construction projects today offer unique challenges with dangerous work conditions caused by environmental factors. In Chernobyl, for example, artificial intelligence is helping those working within the exclusion zone understand where it’s safe to work and which places they should avoid.

AI is also ensuring the safety of workers in harsher climates. It can be used to combat dehydration and heat exhaustion in arid environments, and to calculate the risk that comes with working in cold and icy environments.

Here’s a rundown of a few ways this technology helps in environmental safety:

Drones Are Helping to Survey and Map Sites

One of the ways machine learning is being used to improve safety in the construction industry is by helping to survey and map construction sites. Construction companies then use this data to create 3D models of job sites, which helps reduce accidents.

For example, construction workers can use the 3D model to plan the safest route when carrying materials to and from the construction site. They can also use drones to inspect the construction site for potential hazards.

Machine Learning Is Predicting Weather Patterns

Weather is one of the most important factors in construction. If the weather is bad, it can delay construction projects or even cause accidents.

Machine learning is now being used to predict weather patterns. This information can then be used to improve safety on future construction sites. For example, if the data shows a high chance of a storm, the construction site can be shut down for the day.

Worker Safety

Perhaps the most important way AI is improving construction safety is by ensuring that workers observe important ergonomic and safety practices.

Artificial intelligence was used throughout the COVID-19 pandemic to ensure that workers were not exposing themselves or others to the disease, and, if exposure did occur, track those who were positive to limit spread.

Machine learning helps ensure that everything a worker does, even in their smallest movements, is protected from harm, by predicting it before it happens and suggesting solutions.

AI can also identify workers ignoring or breaking safety regulations, and has monitored the workforce writ large to see if certain safety training or materials are missing.

Here are some specific ways in which machine learning is improving worker safety:

Algorithms Are Helping Monitor Safety Performance

Machine learning is being used to monitor safety performance. Many different safety metrics can be tracked on a construction site, including the number of accidents, injuries, and the number of near-misses.

You can use machine learning algorithms to track these safety metrics and use this information to improve safety on future construction sites.

For example, if the data shows a high rate of accidents on a construction site, the machine learning system can flag that as a potential hazard. This information can then be used to make changes to the construction site to improve safety.

Virtual Reality Helping in Safety Training

According to a 2013 study published in the Construction Management and Economics Journal , one of the most exciting ways machine learning is being used to improve safety in the construction industry is by helping to create virtual reality simulations. These simulations can be used to train construction workers on how to work on a construction site safely.

For example, if there is a new type of scaffolding that the company will use on a construction site, workers can train in a virtual reality simulation before they ever set foot on the site. This will ensure that they are familiar with the scaffolding and know how to use it safely.

AI and Machine Learning in Construction

One of the biggest achievements of AI in construction has been an increase in safety, but its contributions go way beyond that.

Project Planning and Resource Management

There are many moving parts to any project, and it can be hard to keep track of all of them. Artificial intelligence and machine learning help determine what exactly is needed to complete a construction project, allowing for better planning and cost management.

Similarly important is the ability of AI to limit cost overruns. Most major construction projects go over budget. AI accesses huge databases to assess how much a project will likely cost, and can determine the risk of a project running over budget.

Machine learning lets AI use data from previous projects to understand cost management and predict with greater accuracy the degree of error that may occur.

Project Design

One of the greatest contributions of AI in project design is its ability to visualize models and change them in real-time as adjustments are made.

Artificial intelligence is able to apply physics and statistical analysis to building design. This way, designers can know exactly what each piece of the construction puzzle is doing, allowing for more adaptability.

Machine learning only makes these processes more precise, allowing builders and designers to understand the structural integrity of a project as it’s being built.

Big Data and Construction

Machine learning brings out relationships and causes that would otherwise remain unnoticed. For example, it can be used to estimate building upkeep and understand how the construction project interacts with the environment around it.

In many places, laws require construction projects to undergo an environmental impact assessment, which determines how a planned project would impact the surrounding environment. AIs are able to do calculations in the long term and take into account a myriad of environmental factors.

Understanding Machine Learning and AI

In order to understand how machine learning and artificial intelligence are improving construction safety, it is important to have some grasp on what both machine learning and artificial intelligence actually are.

While computers are able to process massive amounts of information at faster speeds than humans, human brains use far less energy in comparison to computers and are able to do many things computers cannot.

One example, of course, is thinking independently. Even if your computer performs tasks quicker than you, it ultimately needs to be taught how to do it.

Artificial intelligence is trying to, at least to a degree, close this gap. AI uses complex algorithms to allow computers to mimic the problem solving and decision making skills of humans.

Machine learning is a specialized part of artificial intelligence that allows the computer to use historical data and previous outcomes to impact this decision-making process.

For example, a computer might say “drink coffee at night so you can finish the task” but won’t realize you will not be able to fall asleep after. Machine learning allows the computer to remember this and consider it the next time it makes a decision.

How Machine Learning and AI Work

When we make conscious decisions, like what we wear in the morning or what to eat for lunch, our brain goes through several steps. We consider the facts and generate alternatives, we weigh these alternatives, decide on one, and execute our plan.

AI works by undertaking the same steps. In traditional AIs, programmers write algorithms that let a computer determine if a decision is necessary, provide the computer with decisions it could make, establish criteria for ranking how a decision is made, and then make it tell us what it computes to be the best decision.

However, sometimes these machines will make the wrong decision. The machine might use the wrong criteria or miss important relationships.

This is where machine learning comes in. Machine learning lets an AI modify its own algorithm with each wrong decision and new piece of data.

AIs are trained with machine learning by making millions of decisions every day in a controlled virtual environment. This lets the program “evolve” by itself and find its own solutions, rather than simply waiting for the input of a programmer.

Here’s a video that explains the typical steps in machine learning:

For more insights into machine learning and its uses, I recommend reading Machine Learning and its Applications by Peter Wlodarczak (Available on Amazon.com). The author explains the various applications of machine learning, as well as some of the common challenges and best practices for using this technology.

Final Thoughts

Artificial intelligence and machine learning are helping in nearly every aspect of construction safety.

With its ability to run complex simulations and bring up factors and causes that would otherwise remain invisible, AI is helping us make the best decisions possible and identify previous mistakes.

AI and machine learning have already greatly impacted construction and will continue doing so in the future. With old challenges we face and new challenges to come, machine learning is paving the way to the future.