AI in Construction (AIC)

Current Situation – Construction

The sector of building design, construction, renovation and management is one of the slowest in adoption of digitalization and automation technologies, even though there is a significant potential for time and cost savings, material saving, reuse and optimization and energy reduction.  Due to the small profit margins and high percentage of small SMEs operating in the field, research and innovation in the field advance very slowly.   At the same time, construction is one of the most challenging and risky activities from a human worker health perspective, making the sector unattractive to the workforce.  

Potential of AI

AI is already taking its first steps in the field and can contribute to a variety of applications related to the built environment. Computer Vision techniques can be used to transform unstructured 3D scans into building models (Scan-to-BIM) and enrich building digital twins with semantic information on building parts, materials and condition information. Their analysis with AI reasoning methods on 3D data can provide information for construction progress tracking and deviations, task planning and logistics optimization or renovation requirements. Multimodal AI approaches can reason over combined building data such as documentation, building models, technical reports and product passports to provide improvement suggestions for design and material selection, process execution and energy saving.

Meanwhile, robots equipped with perception sensors and reasoning capabilities can be used to perform autonomous inspections for scanning and damage detection or even take over or collaborate with humans on challenging and unsafe tasks involving handling of heavy materials or demolition tasks. Intelligent wearable devices such as exoskeletons and XR-devices can enhance worker capabilities, guide them in task execution and training and monitor their condition.  

Challenges

In order for AI to deliver on this potential for construction, there are significant challenges that researchers need to address. Construction sites are extremely challenging and dynamic environments for perception where existing AI foundation models fail to perform and labeled data for training is scarce. The complexity of tasks and multidisciplinary involvement make the collection expert knowledge for advancing AI systems difficult. The variety of construction tasks make task-specific robotic devices not viable, indicating a need for multi-purpose construction robots that can be adapted to the task at hand on the fly. Meanwhile, acceptance of new technologies from industry representatives and management requires additional effort.