Developments within the engineering sector, what does that make you think of? New materials, extensive calculation methods, challenging implementation techniques? With developments, we often focus on traditional designs, but the development where the most benefit is to be gained is you.
Since the rise of modern science, a knowledge revolution has taken place. Since that time, the development of calculation methods and new materials has progressed rapidly.
During the industrial revolution, artificial energy was invented and it became possible to allow machines to carry out work. People began to do a more abstract form of work. This also gave rise to a large number of new implementation techniques.
We are currently on the brink of a new revolution: the revolution that is artificial knowledge. Artificial Intelligence and machine learning: we hear it everywhere we go.
Technologies that still sounded extremely futuristic to me 10 years ago are now becoming reality. There are self-driving cars, SpaceX rockets that are able to land again after use and self-learning algorithms that already know that we are depressed before any symptoms start to appear.
Which is why, when I think about development in the engineering sector, I think about IT. In recent years, the work of an engineer has increasingly shifted from producing images of a shell using pen and paper, to modelling complex structures in a set of final elements. The computer will become an increasingly important part of a constructor’s work.
If we return to the topic of Artificial Intelligence, we can see that the high-profile companies in this field – Google, Tesla, SpaceX, Apple – are each working on these developments. If we think about developments in the field of IT within the construction industry, we are thinking about BIM.
Then we are thinking about the connection between programmes. I have noticed that we, as structural engineers, are encouraging this development. We are looking towards the ‘major’ software companies such as Scia Engineer and Revit to implement new developments and make it possible to link programme models.
Opportunities for the construction industry
This is not where the major opportunities for development lie, however. If the construction industry wants to move away from the image that it is developing slowly, we will have to encourage the development ourselves from the bottom up.
A significant proportion of our work includes repetition and is simply generating data. This is exactly where the opportunities for development and innovation lie.
If you spend over 60% of your day sitting behind a PC, it’s worth learning a programming language. We don’t all need to do that, but some of our colleagues should specialise in the connection between computers and technology. We need to make that link between programs ourselves.
After all, as user and professionals, we are in the best position to know what developments are necessary. If we obtain more programming knowledge, we will see plenty of opportunities in the work we currently do. A software company will only optimise its program.
We need to use programs that are ‘open’, along with an Application Programming Interface (API). In other words, have access to the data and the ability to capture all those models in parametric models. We thereby need to be smart when storing the data that we generate every day. Ultimately, a machine can see links that we would never have been able to make ourselves from large quantities of date due to the rise of deep learning.
For example, this video of a Tesla self-driving car seeing an oncoming accident before we as people notice anything has now gone viral.
People aren’t replaceable
I am personally working on innovation in our field by controlling DIANA with programming languages such as Python and C#, by writing software that turns the generation of models, reading and processing of data into a few lines of code. Work that usually takes around two days to complete is being reduced to just two hours.
Machines are efficient and productive, but need to be programmed or require large quantities of data in order to work. Let’s not try to be more efficient and more productive than machines; that’s unrealistic. Machines are starting to carry out increasingly more complex tasks.
Let’s do what we’re good at. Solving new problems, making links with small quantities of data. Skills that we are unable to program.
And allow machines to be productive. Then we will finally have the time to actually be creative.
Machines are efficient and productive, but need to be programmed to work.