The construction industry is poised for an artificial intelligence (AI) revolution, according to a Gallagher global report. The shift has been building for decades with architectural, engineering and construction (AEC) businesses using new computer technologies to estimate, manage and execute work. Now they are using AI to perform evaluative functions such as time management and quality control.
Because AI 'learns' from existing data, the increased early adoption of AI in construction is most likely to occur in environments where large amounts of digital information already exist and this is the case for large-scale projects in the construction sector, through building information modelling (BIM), for example, or cost tracking in accounting systems.
The need to bring projects to completion faster, cheaper and better may be the financial factors driving adoption of AI technology as contractors compete for tenders while seeking to retain profit margins.
Types of AI uses in the construction industry
Machine learning applications of use in the construction industry
- Health and safety: wearables and trackers can alert employees to dangers such as hazardous conditions, the presence of dangerous fumes and risky behaviours. These trackers can be programmed to signal when indicators fall outside the 'norm'.
- Project planning and scheduling: using models to analyse previous projects to develop timelines, predictions and customisations for new projects and search for ways to overcome various jobsite impacts and trial different sequences to see how they may affect the schedule.
- Cost estimating: use of learning models to identify productivity and efficiency to refine cost estimations and other factors affecting cost impacts such as assessing the impact of weather incidents and estimating changes in prices of materials.
- Site logistics: analysis of worker and materials movements to identify efficiencies as well as location and movement data to determine if materials have been efficiently distributed on the project for installation.
- Quality control: detection of non-conforming work and pinpointing patterns associated with poor outcomes through examining historical data associated with poor quality work, helping managers address the root causes.
- Optimisation of resources: analysis of waste streams to find better management solutions to reduce materials and disposal costs and improve sustainability.
- Loss prevention: tracking movement of materials to prevent theft and monitoring instances of water damage to limit future impacts.
- Equipment management: maintenance and performance issues, performance of tasks like global positioning system (GPS) steering and light detection and ranging (LiDAR) detection.
Computer vision technology
This AI capability uses computer vision to assist with assessment and analysis by pulling information from digital images, including videos, and taking actions or making recommendations based on that information.
Pulling the visual content from digital images, including videos, provides information for asset management systems to inspect work and to monitor progress, productivity and safety on site.
This can enable image classification, object detection, object tracking and image retrieval based on the content of the image, which is useful for digital asset management systems.
Computer vision may help significantly with quality control as it can inspect work and identify improper practices. It can also be used to monitor progress, analyse productivity and enhance safety management.
Robotics
Robots may be able to perform some repetitive tasks, while fabrication of 3D concrete printing uses robotics to produce building components.
Robotics are also being incorporated into exoskeletons to assist construction workers in performing physically taxing tasks.
AI assisted design
The use of BIM for modelling and eliminating design flaws is well established in construction. This technology also has the potential to help architects explore and generate alternative design solutions.
In quality control AI can be used to conduct checks and calculate different tolerances or predict incompatible materials and systems.
Perhaps most significantly AI can be used to compare the performance of various options to create more efficient structures, improve structural integrity and perform life cycle analysis.
It could also be used to assist with sustainability by designing for energy efficiency, greater accessibility and better traffic flow.
Concerns around the use of AI in construction
Machine learning has great potential but also has inherent limitations. As an analytical tool with applications to the building industry it has great potential for improving efficiencies, saving time and using resources more effectively, but human evaluation will always be a necessary part of the process.
While AI is a powerful analytical tool, professionals must always keep in mind that it is a tool. Users will need to vet the answers and content it gives to make sure that AI is focusing on the right things and presenting the right results.
Gallagher construction insurance specialists
Our specialist construction expertise supports a vast range of construction clients, from major works to smaller projects, advising on industry risks and insurance coverage.
