The Rise of AI and Machine Learning in Construction
The field of construction is well placed to benefit from the advent of machine learning and artificial intelligence (AI). As part of the BIM 360 Project IQ Team at Autodesk, I’ve had the privilege to participate in Autodesk’s foray into machine learning for construction. This article summarizes developments in this space, and covers some ways in which one can prepare to maximize value from this technology, including a broad survey of some of the applications of AI and machine learning in construction, and the potential impact. These processes are making changes across various areas, including risk management, schedule management, subcontractor management, construction site environment monitoring, and safety, to name a few.
What Do We Mean by AI?
The public perception of artificial intelligence usually ranges between the two extremes of having it rule the world to it being dismissed as fantasy with no place in a serious conversation. In reality, the truth lies somewhere in the middle where AI is very far from being a form of super-intelligence but a branch of study that has found tremendous application and is a big driving factor of applications in today’s technology.
Traditionally, defining AI has always been a challenge. ‘Artificial’ is the easier part of the definition where it can simply mean ‘not naturally occurring.’ ‘Intelligence’ on the other hand has led researchers down several rabbit holes. In general, AI refers to a broad field of science encompassing a range of subjects from computer science and psychology to philosophy and linguistics. It is primarily concerned with getting computers to do tasks that would normally require human intelligence. This series of articles provides deeper reading into understanding the definition and history of artificial intelligence.
There are now many fields of work within the broader scope of AI, but here I’d like to define two of the more popular areas — machine learning and deep learning. Machine learning is one such subset that deals with writing algorithms that allow computers to learn from data without being explicitly programmed. If, for example, you want to write an algorithm to identify spam in emails, you will have to train the algorithm by exposing it to many examples of emails that are…