The Construction industry is adopting cutting-edge technologies such as Artificial Intelligence (AI) and that brought unique opportunities to execute construction jobs more efficiently. Research by Construction Business Owner says about the future of AI technology in construction industry “Artificial intelligence spending in the construction industry has been forecast to reach over $4.5 billion by 2026, and has the potential to increase the construction industry’s profits by 71% by 2035.”
From Design to bidding, and financing; procurement to construction; operations and asset management to business model transformation in construction industry, AI has the potential to assist AEC professionals throughout building lifecycle. In construction AI assists in overcoming some of its major difficulties, such as labor shortages, safety concerns, cost consumption and schedule overruns.
What is Artificial Intelligence and Machine Learning in Construction?
When a machine duplicates human cognitive functions including problem-solving, pattern recognition, and learning, it is referred to as Artificial Intelligence (AI). Machine learning (ML) is just a subset of artificial intelligence. Machine learning incorporates statistical techniques into electronic devices like computer systems, to enable the capability of learning from data without having to be explicitly programmed. A machine is exposed to more data, so it improves its ability to interpret and provide insights.
For example, in construction technology, a machine learning algorithm may follow and analyze progress in a grading plan in order to discover scheduling hazards early. To establish a risk score and assess whether notifications are required, the program may ask questions regarding cut and fill volume measurements, weather trends, previous projects, machine uptime, and downtime, or any number of inputs.
AI and ML – Smart Construction Technology
AI and Machine Learning have a lot of potential in the Architectural, Engineering, Construction and Operation (AECO) industry. Making an entire list of all possible uses is not possible in a single article. For example, a few years ago, our mailboxes were filled with a lot of spam emails whereas in today’s scenario we probably receive relatively few. The reason behind it is that spam filters now rely on machine learning to detect trends and keep spam at bay, and they are really excellent at it.
While this is not a construction-specific application however the application solves the in-general issues and impacts everyone irrespective of their industry. The technology is allowing us to be more productive and focused on our work-related conversations rather than wasting time on deleting irreverent emails.
On any given day, a typical construction project may contain several change orders, hundreds of RFIs and thousands of open issues. Imagine having a smart assistant who can sift through this mound of project data and send you an alert about the top ten things that need your attention right now. Machine learning is that smart helper, assisting teams in identifying the most essential risk variables that require immediate attention from the perspective of construction safety and quality.
10 Application of Artificial Intelligence (AI) in the Construction Industry
Prevention of Project Over-budget by AI
Despite having the best project teams, most major projects go over budget. On projects, multiple AI powered software are used to anticipate cost overruns based on parameters such as project size, contract type, and project manager competence. Predictive models employ historical data such as projected start and finish dates to create realistic timetables for future projects. AI enables employees to gain remote access to real-world training materials, allowing them to quickly improve their skills and expertise. This cuts down the time it takes to onboard new professional to projects and results in faster project completion.
AI Improvised building design through generative BIM design
Building Information Modeling (BIM) is a process of generating data-rich 3D model that helps designers, architects, engineers, modelers, drafters, and construction professionals to plan, design, build, and manage buildings, facilities, infrastructure, maintenance, and operation more efficiently. The 3D models must be taken into account with respect to the architecture, engineering, mechanical, electrical, and plumbing (MEP) plans in order to plan and design the building project. The coordination and collaboration amongst multi-disciplinary project teams and models are essential to detect clashes and resolve them during the preconstruction stage. This results in efficient construction completion.
To avoid rework, the construction industry employs machine learning in the form of AI-powered generative design to discover and minimise incompatibilities between the multi-trade models generated by the multiple project teams. There is software that utilizes machine learning algorithms to investigate all possible solutions and generate design options. When a user indicates requirements into the model, the generative BIM design software develops 3D models that are optimised for the constraints, learning from each iteration until the optimal model is found.
AI for Improved Risk Management
Every construction project has some risk, which can take numerous forms, including quality, safety, timeliness, and cost. Because several subcontractors are working on different disciplines on job sites at the same time, hence the greater the project, the higher the risk. Today, general contractors employ AI and machine learning technologies to monitor and prioritise risk on the construction site, allowing the project team to spend their limited time and resources on the most critical risk variables. AI is used to assign priority to issues automatically. Construction managers can engage closely with high-risk teams to decrease risk by assigning a risk score to subcontractors.
AI for Strategic Project Planning
In 2017, one construction intelligence start-up announced that their robotics and artificial intelligence would be the solution of fixing late and overbudget building projects. Robots acquire 3D scans of construction sites autonomously, which are then fed into a deep neural network that classifies how far along different sub-projects are. If things begin to go wrong, the management team can intervene to address minor concerns before they become large problems. Future algorithms will employ an AI approach known as “reinforcement learning.” Algorithms can be learned through trial and error using this technique. It can evaluate an infinite number of combinations and alternatives depending on previous work. Because it optimizes the optimal path and corrects itself over time, it helps with project planning.
AI for More Productive Job-site
Some construction companies are offering self-driving AI-powered construction machinery to execute repetitive operations more efficiently than human counterparts, such as welding, bricklaying, demolition, and pouring concrete. Excavation and preparation work is done by semi-autonomous or autonomous bulldozers with the help of a human programmer to prepare a specified job site. This frees up human labor for the actual construction job and cuts the overall completion time of a project in half. Project managers can also monitor work on the job site in real-time. They monitor worker productivity and process compliance using facial recognition, Unmanned Aerial vehicles (UAV), Drones, onsite cameras, and other similar technology.
AI for Better Safety
As per a report by Occupational Safety and Health Administration (OSHA) “FALLS ARE THE LEADING CAUSE OF DEATH IN CONSTRUCTION. In 2018, there were 320 fatal falls to a lower level out of 1,008 construction fatalities (BLS data).” An algorithm developed by a Boston-based construction technology company analyzes photos from its job sites, identifies safety dangers such as construction staffs not wearing protective equipment, and compares the images to its accident data. According to the company, it may theoretically compute risk ratings for projects in order to hold safety briefings when an elevated threat is discovered.
AI Identifies Labor Shortages
Construction companies are investing in AI and data science due to labor constraints and a drive to improve the industry’s low productivity. Construction firms are beginning to adopt artificial intelligence (AI) and machine learning for construction management resulting better arrangement of manpower distribution and equipment across jobs.
With the help of Artificial Intelligence and robots, project managers can instantly know which job sites have enough people and equipment to complete the project on time, and which are falling behind and might use additional personnel. Robot continually analyses job progress and the placement of workers and equipment. Spot the Dog, an AI-powered robot, can independently inspect a project every night to assess work progress, allowing contractor to complete more work in remote places where trained labour is scarce.
AI-Powered Off-site Construction
Off-site facilities staffed by automated systems are increasingly utilized by construction businesses to piece together building components, which are subsequently patched together on-site by human workers. For example, Walls can be built more effectively on an assembly line by autonomous machinery than by humans, leaving human workers to handle the specific work such as HVAC, Mechanical, electrical and systems once the building structure is put together.
AI Integrated with Big-data in Construction
AI systems are vulnerable to an infinite quantity of data to learn from and develop data on every day at a time when vast amounts of data are being created every day. Every building construction site becomes a possible data source for artificial intelligence. Data collected from building information modelling (BIM), drone captured data, mobile device photos, onsite security sensors, and other sources has accumulated into a pool of data. With the use of AI and machine learning systems, construction industry professionals and clients will be able to identify and get an overview from the data.
AI for Maintenance and Operation
Long after the work is finished, building managers can use AI. Advanced analytics and AI-powered algorithms generate useful insights into the operation and performance of a road, bridge, building, and nearly anything in the built environment by gathering information about a structure using drones, 3D Laser Scanners, sensors, and other wireless technologies. This implies Artificial Intelligence can be used to track the progression of issues, identify when preventative maintenance is required, and even identify human nature for maximum security and safety.
How Does AI Work?
Artificial intelligence uses the cognitive power of computers to simulate human intelligence. Artificial intelligence can be gained in two ways:
Machine learning – Machine learning (ML) is the study of computer algorithms that can learn and develop on their own with experience and data. Computers are capable of recognizing patterns and learning from examples. For instance, Machine learning keeps spam out of email inboxes. Examples of emails categorized as spam and non-spam have been fed into the algorithms.
Deep learning – Deep learning simulates the neurons in the brain, taking machine learning to the next level. As computers and algorithms process more data over a longer period of time, they learn and adapt their algorithms in the same way that humans do. Deep learning in construction is utilized to recognize project type, material requirements, scheduling, predict scenarios, and respond accordingly.
Advantages of Artificial Intelligence in Construction Management
Artificial intelligence is built on data. This data provides rich learning information for artificial intelligence applications because data is collected at various stages of the construction project across multiple distinct projects in the AEC industry. Artificial intelligence is beneficial at every stage of the construction process.
AI for Designing Stage
Designers utilizing AI-based computer applications can quickly generate complicated building designs. Designers can enter diverse design goals and criteria using tools like Autodesk’s Generative Design solutions. The solution makes use of artificial intelligence to produce alternatives for designers to choose from and edit as needed.
Artificial Intelligence for Cost Estimation
Estimators benefit greatly from generative design mixed with data on costs and schedules of similar projects. To create early cost and schedule estimations, machine learning systems evaluate data from similar projects. Combining BIM and AI estimators provide more accurate construction estimating services c in less time.
AI for Onsite Safety Management
For safety managers, AI-based systems that use visual processing algorithms are a significant risk monitoring and prevention tool. Photos and videos from the project are examined for potential safety issues, such as missing personnel who are not wearing the proper PPE. Many safety managers, like project managers, are responsible for several projects and are unable to physical present on-site at all times. The field team, on the other hand, is photographing the jobsite. AI-based safety monitoring solutions scan massive amounts of pictures and swiftly identify workers and incidents that violate safety protocols.
AI for Project Management in Preconstruction Stage
To monitor job site activity, project managers use autonomous devices such as drones, cameras and sensors. Data is used by AI-based tools like Doxel to calculate the Bill of quantity (BOM) installed by studying the project site in 3D. The learning algorithms compare the progress to the initial plans, schedule, and budget in real time. Project managers use real-time data to monitor worker productivity and make modifications as needed to keep the project on track.
AI for Construction Stage
The quality of work is measured using the same AI-based technologies that were used to measure the number of installed materials. The original design is compared to the 3D model collected through drones and UAVs to find any flaws or discrepancies. Foreman can receive fast information to act on corrections right away before the scope of rework expands.
Guidelines to Implement AI in Construction Management
For a building, construction artificial intelligence (AI) is a tremendously powerful technology. It is, however, easily accessible to anyone with a computer and Internet access. Because of this combination of power and accessibility, some people are calling for more strict ethical standards to be implemented.
The following rules present a set of seven basic conditions that AI systems must achieve in order to be considered trustworthy.
Human Engagement and Supervision: AI systems should empower people by allowing them to make informed decisions and promoting their basic rights. Ensure proper supervision mechanisms at the same time, which can be accomplished by continual human involvement, continuous human control and oversight.
Robust and Secure Technology: AI systems must be both resilient and secure. They must be cautious and have a back-up plan in case something goes wrong. They must also be dependable, repeatable, and precise. This is the only way to assure that harm, especially unintended injury, is minimized and avoided.
Privacy and data governance: In addition to ensuring complete respect for data protection and privacy, suitable data governance processes must be given, assuring legitimate access to data and considering integrity and quality of data.
Transparency: The data system as well as the AI business models should be available to project team members. Traceability methods can aid in this endeavour. Furthermore, AI systems and their conclusions should be conveyed in a way that is tailored to the specific stakeholder. Humans must be aware that they are engaging with an AI system, as well as its capabilities and limitations.
Diversity, Non-discrimination, and Fairness: Bias must be avoided since it can have a variety of harmful consequences, ranging from marginalizing vulnerable populations to intensifying prejudice and discrimination. In order to promote diversity, AI systems should be accessible to everyone, regardless of any disability, and collaborated with all key stakeholders throughout their life cycle.
Well-Being of Society and The Environment: AI systems should benefit all humans, including future generations. They should be environmentally friendly and long-lasting. Furthermore, they must consider the environment, including other living beings, as well as their social and societal implications.
Accountability: Establish methods to ensure that AI systems and their consequences are held responsible and accountable. Auditability, which allows for the evaluation of algorithms, data, and design processes, is crucial in critical applications. Furthermore, ensure that proper and accessible remedies is available.
The Future of AI in Construction
A survey by McKinsey says “Asset productivity increases of up to 20% are possible, and overall maintenance costs may be reduced by up to 10%” utilizing AI and advanced Internet of Things (IoT) sensors. Engineers can wear virtual reality (VR) goggles and dispatch mini-robots into under-construction buildings. These robots utilize cameras to monitor the progress of the work. In modern structures, artificial intelligence is being utilized to plan the routing of electrical and plumbing systems. AI is being used by businesses to construct workplace safety solutions. Artificial intelligence (AI) is being used to track the real-time interactions of construction professionals, machinery, and materials on the job site and warn supervisors about potential productivity concerns, construction errors, and safety hazards.
Despite projections of significant job losses, AI will not be able to completely replace the human workforce. Instead, it will change the business models of Architecture, Engineering, Construction and Operation (AECO) industry mitigating jobsite injuries, reducing costly errors, and increasing the efficiency of construction operations.
Construction professional should focus their investments on areas where AI may have the greatest influence on their business and project specific demands. Early adopters will shape the industry’s future and benefit in both the short and long term. Tejjy Inc. being one of early adopter of advanced technology like AI-powered Virtual Reality (VR), Augmented Reality (AR), Drone and 3D Laser Scanning technology, providing ISO-certified quality assured construction project management and BIM services to the Architecture Engineering and Construction (AEC) industry in the USA. With the presence in multiple locations in the USA including Maryland, Washington DC and Alaska our expert BIM professionals, Engineers, Architects and Drafters served over 2400+ happy clients.