The following is a guest post by Jennifer Wilson, a writer at Qeedle.com.
How machine learning in construction helps workers be more effective
This phrase doesn’t sound like anything particularly human, does it? Most people are more likely to picture T-800 from the Terminator than something that even remotely displays human characteristics.
In an age where new technologies have been taking over a lot of our tasks and executing them more quickly and accurately than regular employees, it’s nothing strange that automation, AI, or machine learning are perceived as threats.
But, in reality, all these advanced technologies can significantly improve our lives and the way we do business. This particularly refers to the construction industry, in which machine learning does add a human touch by allowing workers to do their jobs more effectively and safely.
1. Prevent Cost Overrun
Construction teams tend to have a lot of problems regarding cost overruns when they’re working on large scale projects.
No matter how efficient the team is, unrealistic timelines can wreak havoc on their project management, which results in the costs skyrocketing.
Machine learning and artificial neural networks can prevent such a scenario by analyzing the size of the project, contract type, and project managers’ level of competence. These factors, paired with historical data, make it possible to create predictive models and set realistic timelines.
Besides, artificial intelligence provides workers with real-time resources for remotely enhancing the skills necessary for a particular project. This practice significantly reduces the onboarding time and facilitates the entire process. All this will speed up the project delivery and prevent it from going over budget.
For example, implementing employee chatbots can help with
- automating different routine tasks,
- boosting team productivity,
- facilitating the information search during employee safety training,
- streamlining the onboarding process.
2. Improve Construction Site Safety
The construction industry accounted for 21.1% of all worker fatalities in 2018.
According to relevant stats, the most common causes of death in this industry are falls, being struck by an object, electrocution, and getting caught in/between.
A Boston-based general contractor developed an algorithm for analyzing photos from its construction sites in order to spot potential safety hazards and then compare these photos with its incident records. In other words, if workers don’t wear protective gear or if some site areas aren’t properly secured, this predictive algorithm will record that.
This AI-powered algorithm allows construction companies to identify elevated safety risks and prevent accidents from happening.
3. Allow Risk Mitigation
Injury and death hazards aren’t the only risks in the construction industry.
Quality, safety, time, and cost risks also play an important role, and it’s essential to prevent and mitigate them in case things go south.
Machine learning is capable of identifying different risks and measuring their impact before they emerge. Construction project managers have a tough job of overseeing the entire site and making sure that every segment of the building process is progressing at a predefined speed. Also, it’s their responsibility to ensure on-time and on-budget project delivery.
All these challenges that leaders in the field of construction face can be solved with the help of machine learning and AI. By analyzing and sifting through massive amounts of data, these technologies can forecast potential risks, understand them, and identify consequences if they’re not handled properly.
This way, it’s possible to prioritize issues based on the level of risk and assist construction project managers in monitoring and addressing the most pressing problems first.
4. Take Advantage of Generative Design
One of the biggest challenges in constructing a building lies in the fact that it has to take into consideration tasks from different fields such as architecture, engineering, electricity, and plumbing. All these elements have to be perfectly planned out, executed and aligned.
Generative design allows construction professionals to create 3D-based models that contain all these plans and map out the activities of the teams involved in the project. These models unite all the plans that these different teams create and prevent potential clashes between them.
Generative design software supported by machine learning is used to analyze and generate different variations of a solution and offer design alternatives. In other words, it creates 3D models of different systems – mechanical, electrical, and plumbing, at the same time ensuring that the MEP routes don’t clash with the architecture of the building. This advanced technology will also generate an optimal solution based on all the data it has.
Such an approach prevents reworks, thus saving a lot of time and money.
5. Distribute the Workforce Accurately
Machine learning can help with the accurate distribution of the workforce and machinery across different construction sites, thus preventing teams from going over budget.
Analyses show when a certain site is understaffed or if it lacks machinery so that it’s possible to reposition workers efficiently and making sure that the workforce is evenly distributed.
By implementing software that constantly monitors and evaluates work progress, as well as the location of workers and equipment, companies can better plan their workforce and machinery distribution and meet their deadlines.
It’s clear that the construction industry can greatly benefit from machine learning. Increasing safety, reducing costs, improving quality, and boosting workforce productivity are only some of the segments in which this technology will help construction companies grow and thrive.
Jennifer Wilson is a writer at Qeedle.com She knows business processes and operations management inside out. As she understands all the challenges of running a small business firsthand, it’s her mission to tackle the topics that are most relevant to entrepreneurs and offer viable solutions.
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