Kenneth Stallard and Nima Youchidje | Carr Maloney
Just as in most other sectors of business, it is unquestionable that Artificial Intelligence is transforming the construction industry. AI is affecting how construction workflows are prepared, how real-time decisions are made, and how on-site work is performed.
Generative AI:
Large language models (LLM’s) are trained with self-supervised machine learning on vast amounts of text and are 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. Construction industry teams are using LLMs and generative systems to automate proposals, produce contract summaries, draft submittals, and generate first-pass designs or layouts that a human can then refine. In design, generative design tools can combine rules, and performance goals and constraints to propose design options that minimize material use and costs thereby accelerating early-stage decisions and reducing rework. This can result in faster bid responses, fewer errors in documentation, and new creative/verification loops, where humans can assess AI-proposed alternatives.
Digital Twins:
Digital Twins are live, data-backed 3D models of physical assets and sites. It involves the use of virtual/digital replicas of a physical objects, spaces, processes, or systems. Digital Twins are used for real-time analysis, simulation, and prediction. By continuously synchronizing with its physical counterpart through sensors and data, a digital twin can help optimize operations, prevent problems, and inform decision-making. In the construction industry, Building Information Modeling (BIM) and other forms of 3D modeling provide the detailed digital design. Information from project documentation is then fed into the digital twin, as well as data from IoT sensors, which are used to collect and convert data about the physical environment into digital information. Through the use of information gathered from the digital twin, the construction team can predict and reduce risks, closely monitor on-site progress, test alternative strategies, and predict decision outcomes in advance of implementation.
Robotics and autonomous heavy equipment:
The use of robotic bricklaying, 3D printing of construction components and entire structures, and the use of drones for surveying and monitoring, as well as the use of robotics in inspections are all starting to be seen in the construction industry. These tools have the potential of increasing productivity and safety. Although the initial expense of implementing these technologies may currently be somewhat prohibitive of their use on many projects, their use will undoubtedly become more commonplace as implementation costs stabilize.
Predictive safety 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 to suggest targeted interventions to supervisors. Use of these systems can show measurable reductions in incidents when AI alerts are paired with human accountability. But they can also raise privacy issues, labor questions, and other potential legal concerns.
Conclusion:
AI in construction is no longer experimental. It is delivering real value in speed, creativity, safety, sustainability, and cost control. The industry winners will be the companies that pair focused AI use with clean data, clear governance, and strong field adoption. As with other industries and professions those using AI, those in the construction industry should not rely entirely on the technology, and instead, should use the technology as any other tool, with proper human oversight, review, and intervention.
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