Construction predictions 2023

Trust is based on all parties having access to the same information at the same time. This trust is further reinforced if the information has been validated by all parties following the same rules. It is expected that blockchain can be used as that cornerstone in the construction industry to democratise project data, remove confidence barriers that certain centralised processes can entail and unlock a variety of use cases. This level of automation can accelerate the administration of contracts and significantly reduce end-to-end processing times, particularly where certain contractual conditions are well defined.

This technology also adds an additional layer of security. Once the information has been stored in the appropriate block of the ledger and is visible to all parties, any tampering is averted. For an industry in which claims, and disputes cause major delays, significant settlement fees and widespread frustration, blockchain technology could help significantly reduce the quantity of them. This level of automation can remove the time consuming and often costly manual processes involved at all levels of the supply chain and enable them to work together more effectively.³

Supported by improvements in cloud and edge computing, mobile hardware, and apps, and onsite highspeed internet access (5G/ mobile wireless networks) the adoption of digital models has vastly increased.

Digital twin technology is no longer limited to viewing and navigating a model. Organisations will now be able to replicate a range of processes related to executing the project lifecycle. Clients will also be able to track materials and plant movements more accurately, model resources and monitor progress using a digital representation of the workflow⁵. This increased end-to-end visibility of all site specification, materials and resources becomes even more impactful when coupled with automation and AI analytics. Digital twin models will act as a digital partner notifying the project team of any potential blockers and acting early to mitigate an impact to the project schedule. This could include automated orders when certain levels have been reached or highlighting where there is a lack of resource on site to complete planned work.

The augmented reality functionalities that are found in our smartphones that enable consumers to interact with products and services in a smarter way are now being used widely within the infrastructure sector. The use of mobile phones with LiDAR scanning technology coupled with smart apps that allow accurate, rapid model update without hiring specialist services⁶. The technology provides a live view of the construction site by collating images from hard hats that are integrated with AR screens and computers that allow technicians to navigate and update 3D models in the field, in real time⁷.

These technologies are also supporting virtual and remote ways of working and improving the safety and efficiency of workers. This includes robots coupled with AI that provide continuous site surveying for model development and construction progress updates⁸, UAV drones provide visualisation data across vast areas and extreme heights at low cost⁹ and even fully wireless sacrificial sensors which provide live feedback on concrete maturation to enable real time quality assurance¹⁰.

Emerging construction technology-based SME’s are rapidly evolving their capabilities in capturing and leveraging data insights throughout the project lifecycle to influence areas surrounding cost, schedule, and productivity. Their aim is to support the construction industry to drive project objectives using AI (Artificial Intelligence), ML (Machine Learning) and Algorithm-Powered optioneering technology tools.

Innovative start-ups are leveraging large databases of historical schedules and identify patterns in past projects to provide accurate estimates for key activities. The technology is now able to estimate a level of certainty for a project deadline or instantly generate multiple future scenarios to be assessed by senior management. Using predictive tools offers greater certainty when addressing delivery risk. AI and ML draws on multiple data sources to give insights beyond human limitations to consider site constraints, design specifications and other key requirements. Leverage data insights in this manner mitigates risk, reduces costs and optimises output.