3 steps guideline to choosing an ML based solution for your AEC projects

Machine Learning is a technology that assists computer devices to learn (to think and understand) and make decisions like humans do. Based on the provided data, the Machine Learning system learns to predict the further course of actions, thereby reducing human efforts and saving time. In our last blog, we shared insights into Understanding the Implications of Machine Learning for the Construction Industry to help you get started on this technology-driven smart journey.

However, the quality of the data would play an important role in the effective functioning of the technology. Machine Learning tends to make mistakes/take wrong decisions/make irrelevant predictions if fed with wrong data.

Insight Into the Future of Machine Learning in Construction & Increasing Efficiency Through ML

According to the Gartner Inc report published in 2019, in 2021, AI augmentation will generate $2.9 trillion as a commercial or business value across the multiple sectors of the world economy. These numbers speak for themselves. Hence it is vital for the construction industry to identify the need of the time and learn this technology to be able to ride the curve.

MGI’s Industry Digitization Index states that among all the sectors in the world, the construction sector is the least digitized sector. As per the same index, the construction sector ranks second-last in the USA and falls down to the last rank in Europe. And yet, AI’s commercial or business value in the construction sector is valued at $1 Billion by 2021, and it is expected to reach $3.3 billion by 2025.

When To Use Machine Learning

There is no one-word answer to this question. If implemented consciously and effectively, Machine Learning is a brilliant technology that will increase business efficiency and cut down the stress. But, considering the craze for virtual solutions in the market, ignorant businesses might land up in a trap by deploying meaningless and insignificant solutions.

Many companies face multiple challenges while implementing AI. Some of them are:

• Failure to identify the problem
• Misaligned expectations &
• Lack of data management capabilities

(While we will move ahead with problem identification and settling right expectations, it is important to understand that Machine Learning is not the ultimate solution. It is simply an efficient technology that can be leveraged as an effective assistant to meet business needs)

Businesses can begin with the below mentioned three-step approach in building a right base for the right ML technology implementation.

● Feasibility Analysis

Existing types/type of problem and the availability of data in terms of quantity as well as quality. Here businesses will learn and resolve ‘When to use ML?’ question.

● Intuition Fitment

Identifying the type of technique, tools, and algorithms that can be used to solve the problem/problems. Be it solving issues in the designing process, on-site predictions, off-site decision making, etc, intuition fitment would help in streamlining an effective problem-solving process.

● Expectations Setting

Setting the limit for expected results. Which means, reckoning and accounting for the result that can be expected or derived from the ML tool in order to employ the tool for further course of action

How do you decide if the ML is the solution to your construction operation challenge?

As a common market practice, Machine Learning is usually considered for projects that demand to predict a result or possible future trend. But, if businesses want a machine to make sense of the data on its own then businesses can go ahead and use Machine Learning in the below-mentioned scenarios:

A massive number of complex guidelines

Many of the human tasks cannot be efficiently and accurately solved on the basis of simple, rule-based guidelines. There are many factors that could affect the process or the result. When there are multiple rules and guidelines overlapped on each other, a common human brain will find it hard to reach the results accurately in the given time. Here, Machine Learning works effectively. Again, provided the data ( no matter how complex it is) is accurate, qualified, and well-processed.

Scaling task

Given a task, a human mind can sort out hundreds or thousands of emails (depending upon the time and the capacity) and categorize them effectively as important, unread, spam, etc. However, the same task gets tedious for millions of emails. Machine Learning jumps in for rescue here. ML solutions can be effectively used to handle large-scale problems.

Pure learning or automation with learning?

Businesses must learn to differentiate between the type of problem they are facing. Is it a pure automation problem or automation with a learning problem?

If the problem is clear enough, the environment is controlled, all the procedures and inputs are clearly pre-defined, then automation without learning (traditional automation) should work.

However, if the inputs are complex with multiple variations and the problem demands constant learning from the experiences, pure automation solutions would work.

Our perfect feasibility analysis clears if the problem/product needs an AI solution or otherwise. The feasibility analysis of the problem and the subsequent solutions are shortlisted based on the time and cost required for the problem to get resolved. The perfect solution will be the one where the time spent and the cost are less and the impact is high.

Then comes the Intuition Fitment Test, where businesses answer a few questions like:

1. Do the solution and its result make sense?
2. Does it fit in the business framework?

And they identify and understand which Machine Learning solution they need.

Expectation Alignment 

Many businesses, after deploying (and before deploying) any technology tend to develop unreasonable and heightened expectations from it. Take the example of driver less cars. One cannot expect them to be ready in some months or so. Before going completely autonomous the car has to pass multiple tests and stages of development. Through these stages, enough data is received (quantitative & qualitative) to facilitate automation with learning.

Yes, Machine Learning will make the right predictions only if given enough data, and time for the machine to ‘learn’. Hence, businesses have to make sure that the fed data is accurate. For example, in the medical or healthcare (and aligned) sectors even 1% of error can cost somebody’s life. Here, the organization cannot afford an error. Choosing the right Machine Learning for architecture and construction companies, hence matters the most.

If you have any questions while applying the three 3 steps guideline to choosing a machine learning solution for your business challenge, we would love to help.