In 2016, to the shock of many, the Alpha Go computer program, developed by Google Deep Mind, managed to beat professional Go player Lee Sedol.
Experts believed then that development of a program that could beat the complex game of Go was at least five years in the future.
That momentous event spurred the Chinese government to release the next Generation Artificial Intelligence Development Plan in 2017.
This is a comprehensive strategy to advance the development of AI in China to catch up with the US by 2020, overtake it by 2025 and become the global leader by 2030. Thus the seeds of the current US- China trade war were sown.
Meanwhile in the US, private corporations such as Google, Amazon, Facebook and Tesla are largely responsible for the US’s current competitive edge in AI. The US also remains the leader when it comes to the amount of funding, number of AI firms and patents filed.
US companies such as Google, Nvidia and Tesla have a leading edge in manufacturing the most powerful AI chips.
Across the causeway, the Singapore Government has just rolled out their national strategy on AI. It has invested US$500mil on AI and other digital technologies through 2020 and has attracted Chinese and American firms to the country with policies that support AI research.
The global construction industry has grown by only 1% per year over the past few decades.
Compare this with a growth rate of 3.6% in manufacturing, and 2.8% for the whole world economy. Productivity, or the total economic output per worker, has remained flat in construction.
In comparison, productivity has grown 1500%in retail, manufacturing, and agriculture since 1945.
One of the reasons for this is that construction is one of the most under-digitized industries in the world and is slow to adopt new technologies (McKinsey, 2017). McKinsey expects the spread of AI in the construction sector to be modest in the immediate future.
Nonetheless, a shift is coming.
AI is an aggregative term for describing when a machine mimics human cognitive functions, like problem-solving, pattern recognition, and learning. Machine Learning is a subset of AI.
Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to “learn” from data, without being explicitly programmed. At the moment machine vision and speech recognition already surpass human capabilities.
Machine learning and AI are helping make job sites more efficient and saving money.
AI solutions that have made an impact in other industries are beginning to emerge in the construction industry. The potential applications of machine learning and AI in construction are vast.
Let’s look at a few examples to illustrate the potential changes.
In order to plan and design the construction of a building, 3D models need to take into consideration the architecture, engineering, mechanical, electrical, and plumbing (MEP) plans and the sequence of activities of the respective teams.
The challenge is to ensure that the different models from the sub- teams do not clash with each other.
The industry is trying to use machine learning in the form of “generative design” to identify and mitigate clashes between the different models generated by the different teams in the planning and design phase to prevent rework.
Thanks to “generative design” tools, even complex plumbing, mechanical and electrical work can be theorised within 3D modelling design, meaning logistical challenges can be worked through while planning, saving time and costs in the building stages.
Robotic capability is still quite primitive and requires controlled conditions for AI to be useful. However autonomous construction equipment are already in production that can undertake repetitive tasks like earthworks and haulage of earthwork materials, pouring concrete, bricklaying, welding, and demolition.
These machines can undertake these works more efficiently than their human counterparts. The growth of efficient construction will be further enabled by the rollout of 5G wireless technology.
5G will provide the platform for remote operations of construction equipment miles away. This will be a significant boost to productivity of civil construction where the constraint of operators will become nullified.
Construction sites are especially risky due to the number of variables involved. One way to mitigate the dangers on a construction site is to do as little as possible at the site – or move the construction site altogether.
A combination of AI and robotics can produce prefabricated construction, which allows building elements to be built in a controlled factory and then transported to a construction site.
This process controls many of the would-be hazards on a standard construction site, and completes the most dangerous tasks without risking human injury.
A second application that is poised to have a huge impact on construction is image tagging and analysis. We already see powerful AI at work in social media, where algorithms identify facial patterns to automatically tag individual people with astonishing accuracy.
The same AI technology, with new training, can be used to identify and analyse safety hazards, categorise and tag site photography, and send notifications when PPE is not being properly used on the job site.
It can even be used to identify who is violating safety standards, and tag them for their supervisors to address the problem.
AI will have minimal impact on the labour component of the construction industry in the immediate future. Instead the initial impact will be white collar staff who will find that the mundane aspects of their work will get automated.
This requires engineers, quantity surveyors and site inspectors to develop new skillsets to prepare for the future when AI will become more prevalent in the construction industry.
So what can the industry do to prepare for the new paradigm that will emerge. Firstly institutions like Institution of Engineers, Institution of Quantity Surveyors and Institution of Surveyors need to instill greater awareness of AI among its members through articles in their monthly magazines, talks from experts in this field and the setting up of online research libraries for their members to keep abreast of the latest developments.
Secondly, development boards like CIDB need to formulate and AI strategy for the Industry similar to what MDEC is undertaking for the SME’s.
The key success factor in AI deployment is in the collection of data that is tagged. Vast amount of data needs to be utilised to train AI computer logarithms to learn and be able to classify objects or make decisions.
One approach is to have all drawings submitted for planning approval in BIM to ensure that building plans are digitised going forward. Finally, there will an explosive demand for data scientists.
MDEC predicts that there will be a demand for 15,000 data professionals in 2020 when currently there are only 7,000. If this skill shortage is not bridged then we will have to import these skilled professionals from overseas.
Politically this will be difficult situation for the government as job losses by locals will be replaced by imported foreign professionals.
Our Shared Prosperity Vision will depend very much on how well our government, industry, professional institutions and learning institutions manage the profound changes that AI and Industrial Revolution 4 will bring.
The views expressed here are the writer;s own.
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