Machine learning will soon allow software applications to synthesise vast amounts of engineering knowledge in seconds. Architects and engineering professionals, by contrast, take years acquiring the skills and experience needed to design buildings, leaving them unable to compete. AI likely will be specialised at first to automate menial tasks, coordinate, and perform quality control. Many tools are starting to display potential in these areas, as AI improves these areas of the field and others will loose billable hours per project.
AEC software is highly monopolised and Revit, for example, has allowed you to run a team with less staff than you might’ve needed 20 years ago, but you pay upwards of £2,200 per individual in software subscription fees per year, so instead of labour cost you have very high software cost paid to companies with market capture. I think that alongside maybe rendering software is the best example of automation currently, and it hasn't delivered much savings in the end just higher quantity or quality and a transfer of cost to software.
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Any construction professional that realises what a regulatory quagmire the industry operates in knows that AI will never be able to fully integrate this context, it is a shifting mosaic that would first require complete incorporation – even building codes.
More broadly, computational design is in practice at every large and medium, as well as some small, architecture firms around the world; used to do heavy lifting of analysing and optimising work and today, combined with BIM, we have the ability to do more with less people. This trend is not going away. We should all get more savvy with technology as it will be the best assistant to our work. Those who can’t will be forced to retire, or leave the profession like those who still wanted to use pencil on drawing boards after CAD was well established. Human coorperation with intelligent machnies will define the next era of history; using a machine which is connected through the Internet, that can work as a collaborative, creative partner.
In the data-driven future of project management, construction project managers will be augmented by artificial intelligence that can highlight project risks, determine the optimal allocation of resources and automate project management tasks.
Google’s CEO goes so far as to say that “AI is one of the most important things humanity is working on. It is more profound than […] electricity or fire.”
With applications of artificial intelligence already disrupting industries ranging from finance to healthcare, construction project managers who can grasp this opportunity must understand how AI project management is distinct and how they can best prepare for the changing landscape.
In the data-driven future of project management, construction project managers will be augmented by artificial intelligence that can highlight project risks, determine the optimal allocation of resources and automate project management tasks.
Construction is a $10 trillion industry and accounts for approx. 10% of worldwide employment. This industry consumes 25-40% of all raw materials so in other words it is ENORMOUS! But even though we are such a large industry, we are among the least digitalised. In general, we spend less than 1% of turnover on IT, much less than most other industries. This is unfortunately not because we are so awesome that we have no need for change. The average construction worker only spends 30% of his or her time on-site actually building something and the rework rate is 7-15%. So in other words there are a big pile of money just waiting to be taken for those who can improve efficiency.
No matter if we build houses or huge civil engineering projects, a lot of the processes are repetitive. And when we have repetitive activities, we can start consolidating the data and making assumptions based on facts and not gut feelings. Learnings can then be shared across the company and the industry which would contribute to radical efficiency improvements.
This data is our hard-earned knowledge, data-based, that can be shared between people involved in the project. The more knowledge we have amassed, the more likely the project will be delivered on budget and time. The client will know what it will cost to build as he has experience from previous projects, the advisors will know exactly how to create a buildable design as they know which elements to put together in the BIM model and the contractor will be able to tell exactly how long it will take to build as they have done it several times before and have captured the data. This is where data and machine learning/AI is going to help a lot.
If we model a project, machine learning can tell us if we miss something that we normally use to make a project like this. And if we want to start projects in country X in month Y, we can already take weather conditions into account as we know how the weather normally is (these data points have been available for the past 50 years). So the “system” tells you that if you want to start your construction phase at this time, there is a X% chance of rain, snow etc. So now we go from having great experience (single person knowledge) to actually sharing information based on knowledge across the company and projects we have been involved in - a massive amplification of joint knowledge.
How the application of AI can impact the construction project management, and in particular the BIM project, is still unknown. Designers, architects, and engineers find more questions than answers. What is clear is that the processes for simulation of the building and BIM produce so much data that the majority of the organizations do not know what to do with them.
Hence, it is fundamental to understand the amount of data that is produced in the process of drawing, BIM modelling, construction, and building maintenance. The architects, engineers, and other construction professionals are not using all of this data for their own benefit, or that of their customers. The data stream generated by construction is not usually used, or at least it is not used in the proportion of the possibilities provided by AI.
The tendency in a sector not accustomed to the standardized methods and processes, is to move on to the next project without considering how to use the collected data for improvement. The expert in construction technology Nicholas Klokhol explains the possible implications of AI and Big Data applied to the context of BIM in the construction sector, and its main current problem: once the architectural project is built, 95% of the generated data is either deleted or not properly archived, hampering future analyses and exploitation.
When the construction process begins, plans must be made and this is where AI is first introduced. Autonomous equipment is considered as AI as it is aware of its surroundings and is capable of navigation without human input. In the planning stages, AI machinery can survey a proposed construction site and gather enough information to create 3D maps, blueprints and construction plans. Before AI was introduced, this was a process that would take a while to complete – weeks, in fact – but now, this can be accomplished within one day. This helps to save firms both time and money in the form of labour.
A job that was regularly carried out by physical workers, AI is now able to control and manage a project. For example, workers can input sick days, vacancies and sudden departures into a data system and it will adapt the project accordingly. The AI will understand that the task must be moved to another employee and will do so on its own accord.
AI is also good for communication, as this type of system can help direct engineers with how to carry out specific projects and better their performance. For example, if engineers were working on a proposed new bridge, AI systems would be able to advise and present a case for how the bridge should be constructed. This would be based on past projects over the last 50 years, as well as verifying pre-existing blueprints for the design and implementation stages of the project. By having this information to hand, engineers can make crucial decisions based on evidence that they may not have previously had at their disposal. Construction sites can be dauting, with huge structures and risky heights, but with the introduction of autonomous machines – workers can now be outside of the vehicle. Using sensors and GPS, the vehicle can calculate the safest route.
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