As in most other sectors of the economy, it is undisputed that artificial intelligence is changing the construction industry. AI influences the preparation of construction processes, real-time decision making and the execution of work on site.
Generative AI:
Large language models (LLMs) are trained on large amounts of text using self-supervised machine learning and used for language generation. Examples include ChatGPT, Gemini (Chrome) and ClaudeAI. Generative artificial intelligence systems can be used to create text, images, videos, audio, software code, or other forms of data by learning patterns from existing data. Teams in the construction industry use LLMs and generative systems to automate proposals, create contract summaries, draft submissions, and create initial designs or layouts that can then be refined by a human. In design, generative design tools can combine rules and performance goals and constraints to suggest design options that minimize material consumption and costs, thereby speeding up early-stage decisions and reducing rework. This can lead to faster quote responses, fewer errors in documentation, and new creative/verification loops for humans to evaluate alternatives suggested by AI.
Digital twins:
Digital twins are live, data-driven 3D models of physical assets and locations. This involves the use of virtual/digital replicas of physical objects, spaces, processes or systems. Digital twins are used for real-time analysis, simulation and prediction. By continuously synchronizing with its physical counterpart via sensors and data, a digital twin can help optimize operations, prevent problems and make informed decisions. In the construction industry, Building Information Modeling (BIM) and other forms of 3D modeling provide detailed digital design. Information from the project documentation as well as data from IoT sensors are then incorporated into the digital twin, with which data about the physical environment is collected and converted into digital information. Using the information collected from the digital twin, the construction team can predict and reduce risks, closely monitor progress on site, test alternative strategies, and predict decision outcomes before implementation.
Robotics and autonomous heavy machinery:
The use of robot-assisted masonry work, 3D printing of components and entire structures, the use of drones for surveying and monitoring, and the use of robotics in inspections are increasingly being observed in the construction industry. These tools have the potential to increase productivity and safety. Although the initial cost of implementing these technologies may currently somewhat prohibit their use on many projects, their use will undoubtedly become more common as implementation costs stabilize.
Predictive security and workforce analytics:
AI systems that combine video feeds, wearables, weather, crew schedules and historical incident data are now being used to predict higher risk periods or behaviors and suggest targeted interventions to supervisors. Using these systems can lead to a measurable reduction in incidents when AI alerts are paired with human responsibility. But they can also raise privacy issues, employment issues and other potential legal concerns.
Diploma:
AI in construction is no longer experimental. It offers real added value in terms of speed, creativity, security, sustainability and cost control. The industry winners will be the companies that combine targeted AI deployment with clean data, clear governance and strong field adoption. As with other industries and professions where AI is used, construction workers should not rely solely on the technology, but instead use the technology like any other tool, with appropriate human supervision, review and intervention.