Can AI replace humans on construction sites?
Can artificial intelligence replace people (and their jobs) in the Architecture, Engineering and Construction (AEC) industry?
Would the construction site of the future be a machine-run environment, buzzing with drones, walking robots and autonomous vehicles, with human oversight limited to a couple of individuals sitting in a remote location – far from the dust, grind and noise?
Artificial intelligence (AI) is no longer in the realm of science fiction. With the rapidly escalating problem of skilled labour shortage, operational inefficiencies, budget and time constraints, and productivity and safety issues, coupled with global supply chain hiccups, the human-centred construction site of today could experience a paradigm shift to one that’s significantly controlled and operated by intelligent machines.
Doomsday predictions for AI anticipate a world where humans are entirely replaced by machines. Are we looking at a mega-sized disruption, or is human-machine collaboration a more balanced outcome of AI in construction?
According to this ResearchDive report, the global market size of artificial intelligence in construction was almost $500 million (AUD 750 million) in 2021, with predicted revenues of about $8,545 million (AUD 12,775 million) by 2031. For an industry with an estimated $10 trillion (AUD 15 trillion) share of the global economy but rather slow to adopt technology and digitalisation, how will AI drive the building design and construction market?
Here are 5 ways AI will disrupt the construction industry – for the better:
Improving the building design process
AI-powered generative design accelerates the building design process, eliminating inefficiencies, and helping meet the client’s vision faster.
Using AI-based 3D models, you can visualise your designs, identify design flaws and conflicts, run through multiple design iterations, specify the right materials in the right quantities to reduce waste onsite, and optimise utilisation of labour and equipment.
Designers and engineers can collaborate more efficiently on a shared BIM platform, keeping all stakeholders up to speed on progress while reducing potential rework. Instead of information existing in silos for each project, data captured systematically across multiple projects can help build a centralised repository of valuable information that can be analysed using AI and machine learning, and leveraged to apply key learnings, insights and best practices when designing future buildings.
This would mean designing climate-compatible, energy-efficient and cost-effective buildings, reducing construction errors, and ensuring compliance. This would also result in more considerate building design that has a better understanding of the place and future occupants of the space, and its impact on the local ecosystem.
Keeping workers safe onsite
With the construction industry having one of the highest worksite fatality rates in the world, worker safety is a priority (and a compliance requirement) that can be efficiently addressed with AI-powered oversight. Combining drone surveillance, onsite cameras, sensors, computer vision and image recognition technology, construction sites can be constantly monitored in real-time for high-risk situations and unsafe practices, with potential hazards and violations being identified and flagged for action.
Using robots to operate heavy machinery onsite prevents potential accidents that could take place when handled by workers. This also addresses the labour shortage problem in the industry while allowing optimal use of workers onsite.
Back in 2020, Oracle along with its partners set up a range of digital tools on a simulated construction site to showcase their innovations at work. These included:
- Drones and onsite cameras providing live status updates
- Spot, the dog robot from Boston Dynamics mounted with a 360-degree camera and a LIDAR scanner to generate a 3D virtual model of the site in real time – allowing project stakeholders to walk through the site, and eliminating the need for them to be onsite
- Cameras for hazard monitoring, capturing images of people working at height or violating safety rules, as well as slip and trip hazards onsite
- Wearables for workers to alert companies to falls
Operational efficiencies and productivity gains
One of the biggest advantages that AI brings to the construction industry is productivity. By leveraging AI insights and trends from captured data, project stakeholders can monitor construction progress and identify risks earlier to make course corrections. AI analysis of historical data makes predictive modelling and scheduling more precise and enables accurate day-to-day planning of construction activities. Tracking of project milestones can be tailored to each unique project based on real-time progress inputs from drones and site cameras.
AI technology facilitates more consistent, repeatable and accurate data inputs and analysis than from human sources, allowing for data-driven decision-making in project management, and minimising scope for human error. Staff, trade and labour resources can be accurately estimated based on project considerations and data from past projects. Learnings from historical data also assist with quick onboarding and training of new workers.
With the application of robots and autonomous vehicles for repetitive tasks on the construction site, substantial savings can be achieved in both time and cost while mitigating safety risks, preventing unplanned downtime, and improving scheduling. For instance, TyBOT and IronBOT are autonomous robots improving site productivity and reducing lost time injuries at multiple US construction sites. TyBOT ties rebar grids at a speed of over 1,100 ties per hour while IronBOT lifts and carries rebar bundles weighing roughly 2,250kg and places them in the correct grid pattern.
AI algorithms also enable more precise allocation of construction equipment and labour based on real-time tracking of progress onsite, improving the workflow. Very importantly, worker performance can be monitored even without the presence of a site supervisor.
According to Deloitte, AI can help ‘C&I teams generate more accurate estimates, reducing budgets and timeline deviations by an estimated 10-20% and engineering hours by 10-30%’.
AI insights from past projects can inform project stakeholders during material procurement, ensuring only project-specific, sustainable materials are specified in the right quantities, and non-compliant or unsuitable materials are avoided to reduce risk of replacement or expensive rework.
AI tools can also analyse past trends and provide intelligence on shortlisting the right suppliers, as well as the right time to buy materials based on multiple factors including project schedules, competitive pricing, and potential supply chain hurdles.
Beyond materials, AI uses predictive analysis to prevent cost overruns on project budgets, and provides realistic timelines for completion, reducing financial risks for stakeholders.
AI is helping concrete producers in the US optimise concrete mixes to meet each unique project’s concrete performance and sustainability goals. With the Concrete.ai system, a client, for instance was able to use the specially formulated low carbon concrete to reduce their carbon footprint.
Speaking at the annual ENR FutureTech conference, held in San Francisco, USA, Ryan Henkensiefken, vice president of business development at Concrete.ai, said, “This is what AI is really, really good for – it runs hundreds of thousands of different formulations and calculations to find that unique set of raw materials that go together to meet all requirements for the project.”
AI-powered procurement processes also ensure accurate forecasting of materials, addressing the chronic issue of material waste on construction sites, while providing significant cost savings.
From pre- to post-construction, through design concepts, compliance with building laws and standards, quality control, to ongoing building inspection and management, AI delivers value throughout the project lifecycle, and beyond. AI is being employed to monitor building performance for better energy efficiency as well as air quality, water leaks, building operations and property maintenance among others.
Israeli company WINT has innovated a water management system that combines AI, advanced data analysis, and pattern recognition to detect and stop leaks at the source. Using this system, a major technology facility was able to reduce their total water consumption by 46%, achieving annual water savings of over 30 million litres and reducing their carbon footprint by 350MT/year.
About AI and machine learning in construction, Roz Buick, Oracle Construction and Engineering’s senior vice president product, strategy, and development, says, “We're not replacing human beings; it's about ensuring that humans can do their job better so they can do more interesting and challenging intellectual problems beyond that.”
From Hadrian, the robotic bricklayer with a capacity to lay 1,000 bricks an hour to building a 12-metre long bridge in Amsterdam using AI-driven 3D printing, from round-the-clock drone surveillance allowing stakeholders to monitor progress to autonomous machines executing complete tasks to reduce human error and accidents, AI has indeed evolved over the past decade to make a significant impact on the construction industry.
Artificial intelligence will help design better buildings, improve and speed up processes, optimise use of labour, material, time and money, interpret data for greater efficiencies, increase site safety through continuous monitoring, and manage building performance after occupation, but at the end of the day, it will take a combination of human intelligence and resourcefulness supported by smart machines to make it work in construction.
Image source: Construction Solutions | Boston Dynamics