Ricardo Farinha
Sweco
Hey,
I am originaly from Portugal, Lisbon. I have graduated from University in 2008 where I have a master degree in Electrotechnic and computer science. Since 2008 I have been working in the AEC field, first 4.5 years in Ramboll, now in Sweco for over 5 years.
For more detailed information please check my LinkedIn profile: https://www.linkedin.com/in/ricardo-farinha-6852b321
The recent use of building information modeling (BIM) has made available a significant amount of digital data in the construction industry. This makes possible the use of machine learning techniques in the BIM field. However, this use remains rare.
The construction industry is renowned for its poor productivity and lags behind other industries in the rate by which improvements are introduced.
The next innovation to make designing processes more efficient is likely to be its automation. In this regard, the automatic generation of BIM models has been explored. Present approaches to tackle this problem consist either of generating a BIM model from data on existing buildings or the generation of buildings design from a set of predefined rules. These approaches, while promising, have not yet been able to take full advantage of the information gathered in previously engineered BIM models.
Machine learning is widely used in many fields, ranging from Computer Science, to Physics and Biology. However, it’s not yet widely researched in BIM or more generally in Architecture, Engineering and Construction (AEC) industry. One reason for this is that the storage of digital information in this area that can be used in quantitative research has only recently been used. Storing this data in digital format is what allows the introduction of data analysis techniques in this area. A consequence of this is already visible, in that of the increased amount of BIM is providing more accessible and structured data.
Here, in this presentation, we address this issue by describing some of the challenges faced in the AEC industry and how technology is trying to tackle them. We will also try to give some concrete examples on how the use of Machine Learning techniques and design automation tools are currently improving the productivity of our design processes.
