It is estimated that about $10 trillion is spent across the globe in construction and civil engineering-related activities yearly. This has a projected growth of about 4.2% until 2023. A major part of this spending is on the latest technological things, which touches the field of construction in light of maintaining a proper ecosystem. In a 2020 report of McKinsey, they have underlined an increased focus in terms of artificial intelligence (AI) in terms of shaping a proper construction ecosystem.
AI in construction engineering is more focused on helping the players in this industry realize the actual value of the tasks, including design, financing, bidding, procurement, and construction. It also helps in operations of business transformation and asset management etc. AI in the field of construction can help various industries, and it can also overcome some of our major challenges, including labour shortages, safety concerns, and overheads in cost and time.
As these barriers are now lowering steadily, advancement in AI and supplementing technologies like machine learning, analytics, big data, etc., is now playing a critical role in construction and civil engineering for many years to come. This article will further review some of the major benefits of AI usage in construction.
Artificial Intelligence with Machine Learning in the field of construction
Even though we commonly refer to it as Artificial intelligence, it is primarily an aggregative term that describes various human cognitive functions as pattern recognition, problem-solving, continuous learning, etc. Machine learning can act as a subset of artificial intelligence, which is used as a statistical technique to give the computing systems an add-on ability to learn from data without the need for explicit programming. The machine can become a better tool to understand things by itself and provide insights as more data is fed into it.
In every use case, machine learning consists of various algorithms. When used in the field of construction, these algorithms and questions may get more complex. For example, a standard machine learning program can track and assess the progress in the grading plans for identifying the schedule risks. An algorithm may ask questions about cutting the volume measurements, uptime and downtime of machines, weather conditions, past projects, and also various inputs to generate risk scores.
Smart construction with AI and ML
The possible applications of AI and ML in the field of construction are really huge, and only a fraction of them are being used now. Several applications are already in use with AI. It can request information, attend the open issues, and also can change orders to refresh the industry standards. Machine learning will also assist in scrutinizing a huge chunk of data, and it can also alert the project managers about the critical things which need more attention. It also benefits the range from spam emails to filtering emails for safety monitoring. It is also important for smart AI and ML applications to have a solid and inclusive database, which can be set up and administered by RemoteDBA.com.
Preventing cost overheads
Many of the big construction projects tend to go far above the budget despite using the best project administration approaches. Here, Artificial Neural Networks will help predict the project cost overruns by factoring in various data like the size of the project, type of contract, team of project engineers and managers, historical data, etc. AI will also help the remote staff access the training materials, which will help them improvise their skills quickly. It can reduce the time taken and cut short the funds needed for new projects and resources. As a result of it, the development and delivery of your project can also be expedited.
AI for the generative design of buildings
There is a 3D-based modelling approach to construction modelling, which offers a better insight to construction professionals and engineers a better insight to plan more efficiently and construct building infrastructure. It can help to plan and design the construction of a project and do the 3D modelling of with engineering, mechanical, and plumbing plans along with a sequence of activities with the respective teams. It possesses the challenge to ensure different models from the sub-teams do not clash with one another.
The construction industry also uses machine learning technology for AI-powered design models to identify the clashes between various models and effectively mitigate these. There is also software that can be used with machine learning algorithms for exploring a variety of solutions and degenerate effective design alternatives. Once the users set up the requirements in the given model, generative design software can create 3D models to optimize the constraints and learn from various iteration until they come up with ideal models.
Mitigating project risks
All construction projects come with certain risks in terms of safety, quality, cost, time, etc. The larger your project is, the more the risk becomes. As many sub-contractors are working on different simultaneous tasks at the job sites, AI and ML can be used to effectively monitor and coordinate the tasks based on priorities. With this approach, project teams can effectively focus on their limited time and also help to plan the resources based on the biggest risk factors. AI can also be used to assign the priorities automatically to the issues. The subcontractors can also be rated based on the risk scores with which the managers can focus more on high-risk tasks to mitigate risks.
Construction companies now use robots to automatically capture 3D scans of the construction fields and analyze the data using a deep neural network that can classify how far various sub-projects have reached. If anything seems to be misaligned or off-track, project managers can then step in and deal with the issues before they escalate into major challenges. The future algorithms will use AI-based approaches known as reinforcement learning to do this well. Such an approach will allow the algorithms to learn autonomously based on the trial-and-error approach. It can also assess endless combinations based on various projects. This will also aid in proper project planning and optimize the most appropriate paths to correct by themselves over time.
With all these possibilities, the thought leaders of the construction industry should now prioritize their investment in AI-based technologies, which can fulfill the unique needs of changing construction sector. The early movers in this will set the right direction and benefit the most in the shortest time period.
(This is a sponsored article from our independent contributor)