Over the last couple of decades, the construction industry has been adopting AI technologies to automate and optimize processes, improve site safety and security, and extract insights from complex data. And the demand for AI-powered construction solutions is only growing — by 2030, the market is expected to generate a revenue of USD $5 billion globally.
In this post, we explore the history of AI in construction and its modern applications, as well as the benefits of AI technology for construction companies, workers, and clients. Dive in to discover all the ways AI is shaping the future of the construction industry.
What is AI in construction?
AI in construction is an umbrella term that describes a wide range of AI-powered tools and machinery designed specifically for use in the construction industry. It includes specialized software that utilizes machine learning algorithms as well as advanced industrial robots.
AI-powered systems for construction have been developed in the last two decades, but they stem from automations dating back to the 1960s. That’s when industry professionals started using computer-aided design (CAD) to assist in the creation and optimization of construction plans. By the 1980s, the industry started adopting computer-aided manufacturing (CAM) and computer numerical control (CNC) machines to further automate construction, namely the production of building components and tools.
In the early 2000s, researchers began exploring the use of machine learning algorithms for construction-related applications. Predictive maintenance (using AI algorithms to monitor equipment health and forecast failures) is an example of an early application of AI in construction.
In recent years, the pace of AI adoption across the construction sector has picked up. The industry is now using AI-powered technologies and machines to automate repetitive and physically demanding tasks, improve work safety, and increase site efficiency. AI algorithms can, for example, analyze images of construction sites to detect quality issues in the materials being used. AI-powered robots, on the other hand, can perform tasks like bricklaying, painting, and welding, reducing labor costs and improving project completion times.
Read on for more examples of how modern AI technology is used in construction.
The main components of AI in construction
In this section, we explain the specific AI technologies used in construction. These are general definitions that will help you understand the mechanics that power most relevant AI tools. For specific examples of construction technology, skip to the next section.
Machine learning
Machine learning (ML) is a category of AI science focused on creating systems and machines that learn from data. ML technology relies on artificial neural networks (ANNs) and simulated neural networks (SNNs) that imitate the structure of the human brain.
Machine learning algorithms are trained to form predictions or make decisions based on patterns in the data they analyze. They are able to scan large, complex, and unstructured datasets in real-time, simplifying and speeding up decision-making processes for end users.
Unlike traditional systems that only perform explicitly programmed functions, ML technology can autonomously produce infinite outcomes.
Generative AI
Generative AI is a category of artificial intelligence technology that involves creating new data (text, images, or audio) based on prompts or data inputs. It relies on deep learning — an advanced form of machine learning that allows modern AI algorithms to process images and even sounds rather than just text.
Generative AI learns from vast amounts of data to generate new, original outputs similar to the datasets it can access. This could mean using existing blueprints and models to generate new building designs that follow the same principles.
Internet of things (IoT)
The phrase "internet of things" refers to physical objects equipped with technology (primarily sensors and software) that enables them to communicate and exchange data with other devices and systems over communication networks including, but not limited to, the internet.
The most common application of IoT technology is in “smart home” devices. Systems like Google Home can be used to control several home appliances (lighting, thermostats, security cameras, etc.) via a smartphone or speaker. More advanced, AI-powered IoT systems can operate autonomously, adjusting device settings based on the data they collect and analyze.
AI robotics
AI robotics refers to the field of robotics that uses artificial intelligence to make robots capable of learning to perform tasks and make decisions.
AI-powered robots use advanced sensors and machine learning algorithms to analyze and interpret sensory data, recognize patterns, and make decisions based on that input. Simply put, they analyze their environment and learn from experience, which enables them to perform complex tasks and adapt to changing conditions.
7 popular uses of AI in construction
A 2020 report by McKinsey & Company identified a whopping 37 specific use cases for AI technology in construction. Below, we dive into the 7 most common categories.
1. Project management
AI software designed for construction project management captures photo and video footage on work sites and automatically generates relevant insights, helping construction management:
- track work progress and crew productivity in real-time
- automatically identify issues and address them as soon as they arise
- adjust project completion dates based on actual progress
- visualize progress in BIM (building information modeling) and 3D models
- forecast project lifecycle costs and estimate timelines with more accuracy
AI project management tools also help with budgeting, invoicing, tracking payments, and preventing cost overruns.
2. Scheduling and resource allocation
Using AI tools for scheduling and resource allocation helps construction professionals plan their work more efficiently, and keep project budgets and completion dates on track.
Unlike traditional scheduling based solely on project timelines and worker availability, AI-powered software can map out reliable schedules that also take external factors like the weather, supply chain delays, etc. into account — which is particularly useful in large projects.
3. Estimations and bidding
Construction estimations and bidding (a.k.a. the “takeoff” of a project) have historically been highly manual and time-consuming processes. Once architects and developers come up with a design, a general contractor uses the blueprint to calculate a bid. They then repeat the process with subcontractors: plumbers, electricians, framers, etc.
Putting every individual bid together is a laborious (and unpaid) process without guaranteed returns for the general contractor. The solution? Specialized software powered by deep learning algorithms.
Tribe AI partnered with Togal to build an AI-powered construction estimation software that automatically and accurately detects, labels, and measures project spaces, effectively cutting project takeoff times down from weeks to minutes — and solving one of the construction industry’s longest standing bottlenecks.
Read the full case study to learn more about how Togal and Tribe AI worked together to build an innovative AI solution for construction.
4. Risk mitigation
AI technology can help mitigate common risks in construction in many ways:
- Keeping construction workers and sites safe. Computer vision tools working in tandem with ML-powered software can identify potential hazards on site (e.g., dangerous machine operating conditions) and suggest appropriate preventative measures. (Learn more about innovative applications of computer vision technology in our Tribe AI x Fantasmo case study.)
- Increasing long-term construction safety and quality. AI technology can automate site inspections and detect quality issues in 3D models, video footage, and reports.
- Preventing cost overruns. AI-powered scheduling, resource allocation, and asset management solutions help keep project budgets on track.
- Tightening on-site security. AI and ML technologies can be used to analyze video footage and flag risks (e.g. theft or vandalism) in real-time. Specialized software is more reliable than human security guards, as it’s able to detect threats 24/7 across even the largest sites.
4. Robotics
AI in construction goes beyond software and algorithms — some of it has a very physical presence. AI robotics is becoming an increasingly important and rapidly growing field of construction technology. AI robots that are already present on sites in 2023 include:
- 3D printers
- Bricklaying robots
- Painting robots
- Welding robots
- Layout drawing robots
- Rebar tying robots
- Monitoring drones and rovers
- Self-operating heavy equipment
- Self-driving trucks
The goal of introducing intelligent machinery to job sites is not to replace human construction workers. AI robots complement the work of human workers, and make it safer and more efficient. In fact, some of the robots listed above are considered cobots, a.k.a. collaborative robots designed specifically to share tasks with humans.
This bricklaying robot, for example, works alongside masons and only completes the literal heavy-lifting part of the bricklaying process.
5. Design and planning
AI technologies like machine learning and generative design algorithms can streamline decision-making processes for architects, engineers, and construction professionals involved in design and planning.
AI can analyze complex data, including building codes, zoning laws, topographical information, and historical projects, to generate designs that are:
- in line with regulatory requirements
- cost and resource-efficient
- environmentally sustainable
As we already mentioned above, AI in construction also plays an important role in quality assurance. AI-powered tools can analyze designs and BIM models to identify potential flaws and inefficiencies early on in the planning process, allowing for adjustments to be made before construction begins.
Finally, AI can be used to automate budgeting from the earliest stages of planning and help construction companies accurately determine the materials needed for a project, speeding up the bidding process.
6. Maintenance
Predictive maintenance was one of the original uses of AI in construction. To this day, it helps construction companies extend the lifecycle of machinery, improve on-site safety, and prevent project downtimes.
How does predictive maintenance work in construction? Machine learning algorithms monitor equipment health by analyzing sensor data (including information about environmental conditions and equipment performance stats) and detect anomalies in real-time. They then diagnose issues and alert operators before critical failures occur, suggesting appropriate preventative maintenance action.
The benefits of using AI in construction
- Improved site productivity. AI solutions can automate workflows and repetitive tasks, making teams more efficient and scaling back project completion times. The use of specialized, AI-powered machinery (like the bricklaying robot mentioned in the section above) can further speed up construction work.
- Better project planning. AI systems can streamline scheduling and resource allocation, optimizing the profitability and timeliness of large projects. Artificial intelligence can also recognize structural and functional issues in models, allowing for early course correction and reducing the risk of reworks and waste.
- Lower construction costs. Shorter project timelines made possible by AI-powered automations reduce labor costs, and better planning optimizes the use of materials.
- Safer work sites. Artificial intelligence can identify and flag safety issues. For example, AI applications can scan video footage and sensor data for signs of equipment malfunctions or unsafe working conditions, and alert site managers to take corrective action in real-time.
- Sustainability. AI tools can optimize the use of resources and reduce waste, helping construction companies adopt more sustainable construction practices. For example, artificial intelligence can help optimize the use of energy and water, reducing the environmental impact of construction projects.
Artificial intelligence is the future of smart construction
AI is helping construction companies cut costs and deliver better work faster while keeping job sites organized and safe. Embrace digital transformation and start thinking about how you can use AI to improve your construction firm’s operations and bottom line today. Becoming an early adopter will help you stay on top of industry trends and market demands, and become a leader in your niche.
AI will revolutionize engineering and construction. Our machine learning experts are here to help you adopt AI technologies that will streamline your work. Contact us to find out how to implement AI in your processes.
FAQs about AI in construction
How is AI used in construction?
AI can automate many processes involved in construction projects, making construction work safer and more efficient. Common use cases include scheduling, resource allocation planning, quality control, and predictive maintenance of heavy machinery.
What is an example of AI in construction?
A great example of AI in construction is the use of AI-powered predictive maintenance algorithms to monitor equipment health and detect issues before they become critical and force downtime or compromise site safety. AI is also used to automate estimation and budgeting processes in construction, which are highly manual and time-consuming without the help of specialized tools.
What are the common barriers to implementing artificial intelligence?
Implementing artificial intelligence solutions in the construction industry requires strategic planning and a financial investment. Organizations that implement AI technology without clear goals might not see a positive ROI, which, in turn, can increase stakeholder skepticism towards integrating modern technologies in the future.
Working with professionals will help you get tangible results out of your investment. At Tribe AI, we help organizations apply machine learning to their business by connecting them with experienced ML engineers and leading data scientists. Take risk out of the equation — book a free consultation today.
What are the typical limitations of AI systems?
AI systems only work well if they are implemented to address clear goals, operated by skilled professionals, and equipped with accurate data. If any one of these elements is missing, the work of AI systems might not produce satisfactory results.
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